AnalyzeCommodityStoragePriceData_Quant.ipynb

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        "# Load Libreries\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import seaborn as sns\n",
        "from scipy import stats\n",
        "import matplotlib.pyplot as plt\n",
        "import matplotlib.dates as mdates\n",
        "from matplotlib import pyplot\n",
        "from scipy.stats import norm\n",
        "import datetime as dt"
      ]
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        "id": "xlM73Z6UxgM0"
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      "source": [
        "# Import data\n",
        "Nat_gas = pd.read_csv(\"/content/Nat_Gas.csv\",index_col=0)"
      ]
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              "          Prices\n",
              "Dates           \n",
              "12/31/23    12.8\n",
              "1/31/24     12.6\n",
              "2/29/24     12.4\n",
              "3/31/24     12.7\n",
              "4/30/24     12.1\n",
              "5/31/24     11.4\n",
              "6/30/24     11.5\n",
              "7/31/24     11.6\n",
              "8/31/24     11.5\n",
              "9/30/24     11.8"
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              "summary": "{\n  \"name\": \"Nat_gas\",\n  \"rows\": 10,\n  \"fields\": [\n    {\n      \"column\": \"Prices\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.548128127762519,\n        \"min\": 11.4,\n        \"max\": 12.8,\n        \"samples\": [\n          11.6,\n          12.6,\n          11.4\n        ],\n        \"num_unique_values\": 9,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
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            "<class 'pandas.core.frame.DataFrame'>\n",
            "Index: 48 entries, 10/31/20 to 9/30/24\n",
            "Data columns (total 1 columns):\n",
            " #   Column  Non-Null Count  Dtype  \n",
            "---  ------  --------------  -----  \n",
            " 0   Prices  48 non-null     float64\n",
            "dtypes: float64(1)\n",
            "memory usage: 768.0+ bytes\n"
          ]
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        "Nat_gas.info()"
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              "          Prices\n",
              "count  48.000000\n",
              "mean   11.207083\n",
              "std     0.757897\n",
              "min     9.840000\n",
              "25%    10.650000\n",
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              "\n",
              "\n",
              "<div id=\"df-ce761245-afc8-4a7b-aa24-3588a9883b05\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-ce761245-afc8-4a7b-aa24-3588a9883b05')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-ce761245-afc8-4a7b-aa24-3588a9883b05 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"Nat_gas\",\n  \"rows\": 8,\n  \"fields\": [\n    {\n      \"column\": \"Prices\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 14.040087313383776,\n        \"min\": 0.7578973105463123,\n        \"max\": 48.0,\n        \"samples\": [\n          11.207083333333332,\n          11.3,\n          48.0\n        ],\n        \"num_unique_values\": 8,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 6
        }
      ],
      "source": [
        "Nat_gas.describe()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "scrolled": false,
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 850
        },
        "id": "Pn1GAF42xgM2",
        "outputId": "0d3f7ca2-357f-42b0-9e45-1a0a76ae200e"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x1000 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "ax=Nat_gas[\"Prices\"].plot(figsize=(10,10))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RP5kEuZ1xgM2",
        "outputId": "e4ce651d-3a85-4fa3-e199-2126f3f70beb"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Index(['10/31/20', '11/30/20', '12/31/20', '1/31/21', '2/28/21', '3/31/21',\n",
            "       '4/30/21', '5/31/21', '6/30/21', '7/31/21', '8/31/21', '9/30/21',\n",
            "       '10/31/21', '11/30/21', '12/31/21', '1/31/22', '2/28/22', '3/31/22',\n",
            "       '4/30/22', '5/31/22', '6/30/22', '7/31/22', '8/31/22', '9/30/22',\n",
            "       '10/31/22', '11/30/22', '12/31/22', '1/31/23', '2/28/23', '3/31/23',\n",
            "       '4/30/23', '5/31/23', '6/30/23', '7/31/23', '8/31/23', '9/30/23',\n",
            "       '10/31/23', '11/30/23', '12/31/23', '1/31/24', '2/29/24', '3/31/24',\n",
            "       '4/30/24', '5/31/24', '6/30/24', '7/31/24', '8/31/24', '9/30/24'],\n",
            "      dtype='object', name='Dates')\n"
          ]
        }
      ],
      "source": [
        "print(Nat_gas.index)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "54gMcNd0xgM2"
      },
      "outputs": [],
      "source": [
        "Nat_gas[\"SMA_3\"]=Nat_gas.Prices.rolling(3).mean()\n",
        "Nat_gas[\"SMA_12\"]=Nat_gas.Prices.rolling(12).mean()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ylBFrT2GxgM2",
        "outputId": "11f4df63-f842-4e1b-b6b3-01b7f18d1739"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Prices</th>\n",
              "      <th>SMA_3</th>\n",
              "      <th>SMA_12</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Dates</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>10/31/20</th>\n",
              "      <td>10.10</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11/30/20</th>\n",
              "      <td>10.30</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12/31/20</th>\n",
              "      <td>11.00</td>\n",
              "      <td>10.466667</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1/31/21</th>\n",
              "      <td>10.90</td>\n",
              "      <td>10.733333</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2/28/21</th>\n",
              "      <td>10.90</td>\n",
              "      <td>10.933333</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3/31/21</th>\n",
              "      <td>10.90</td>\n",
              "      <td>10.900000</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4/30/21</th>\n",
              "      <td>10.40</td>\n",
              "      <td>10.733333</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5/31/21</th>\n",
              "      <td>9.84</td>\n",
              "      <td>10.380000</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6/30/21</th>\n",
              "      <td>10.00</td>\n",
              "      <td>10.080000</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7/31/21</th>\n",
              "      <td>10.10</td>\n",
              "      <td>9.980000</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8/31/21</th>\n",
              "      <td>10.30</td>\n",
              "      <td>10.133333</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9/30/21</th>\n",
              "      <td>10.20</td>\n",
              "      <td>10.200000</td>\n",
              "      <td>10.411667</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10/31/21</th>\n",
              "      <td>10.10</td>\n",
              "      <td>10.200000</td>\n",
              "      <td>10.411667</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11/30/21</th>\n",
              "      <td>11.20</td>\n",
              "      <td>10.500000</td>\n",
              "      <td>10.486667</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12/31/21</th>\n",
              "      <td>11.40</td>\n",
              "      <td>10.900000</td>\n",
              "      <td>10.520000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1/31/22</th>\n",
              "      <td>11.50</td>\n",
              "      <td>11.366667</td>\n",
              "      <td>10.570000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2/28/22</th>\n",
              "      <td>11.80</td>\n",
              "      <td>11.566667</td>\n",
              "      <td>10.645000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3/31/22</th>\n",
              "      <td>11.50</td>\n",
              "      <td>11.600000</td>\n",
              "      <td>10.695000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4/30/22</th>\n",
              "      <td>10.70</td>\n",
              "      <td>11.333333</td>\n",
              "      <td>10.720000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5/31/22</th>\n",
              "      <td>10.70</td>\n",
              "      <td>10.966667</td>\n",
              "      <td>10.791667</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6/30/22</th>\n",
              "      <td>10.40</td>\n",
              "      <td>10.600000</td>\n",
              "      <td>10.825000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7/31/22</th>\n",
              "      <td>10.50</td>\n",
              "      <td>10.533333</td>\n",
              "      <td>10.858333</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8/31/22</th>\n",
              "      <td>10.40</td>\n",
              "      <td>10.433333</td>\n",
              "      <td>10.866667</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9/30/22</th>\n",
              "      <td>10.80</td>\n",
              "      <td>10.566667</td>\n",
              "      <td>10.916667</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10/31/22</th>\n",
              "      <td>11.00</td>\n",
              "      <td>10.733333</td>\n",
              "      <td>10.991667</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11/30/22</th>\n",
              "      <td>11.60</td>\n",
              "      <td>11.133333</td>\n",
              "      <td>11.025000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12/31/22</th>\n",
              "      <td>11.60</td>\n",
              "      <td>11.400000</td>\n",
              "      <td>11.041667</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1/31/23</th>\n",
              "      <td>12.10</td>\n",
              "      <td>11.766667</td>\n",
              "      <td>11.091667</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2/28/23</th>\n",
              "      <td>11.70</td>\n",
              "      <td>11.800000</td>\n",
              "      <td>11.083333</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3/31/23</th>\n",
              "      <td>12.00</td>\n",
              "      <td>11.933333</td>\n",
              "      <td>11.125000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4/30/23</th>\n",
              "      <td>11.50</td>\n",
              "      <td>11.733333</td>\n",
              "      <td>11.191667</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5/31/23</th>\n",
              "      <td>11.20</td>\n",
              "      <td>11.566667</td>\n",
              "      <td>11.233333</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6/30/23</th>\n",
              "      <td>10.90</td>\n",
              "      <td>11.200000</td>\n",
              "      <td>11.275000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7/31/23</th>\n",
              "      <td>11.40</td>\n",
              "      <td>11.166667</td>\n",
              "      <td>11.350000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8/31/23</th>\n",
              "      <td>11.10</td>\n",
              "      <td>11.133333</td>\n",
              "      <td>11.408333</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9/30/23</th>\n",
              "      <td>11.50</td>\n",
              "      <td>11.333333</td>\n",
              "      <td>11.466667</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10/31/23</th>\n",
              "      <td>11.80</td>\n",
              "      <td>11.466667</td>\n",
              "      <td>11.533333</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11/30/23</th>\n",
              "      <td>12.20</td>\n",
              "      <td>11.833333</td>\n",
              "      <td>11.583333</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12/31/23</th>\n",
              "      <td>12.80</td>\n",
              "      <td>12.266667</td>\n",
              "      <td>11.683333</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1/31/24</th>\n",
              "      <td>12.60</td>\n",
              "      <td>12.533333</td>\n",
              "      <td>11.725000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2/29/24</th>\n",
              "      <td>12.40</td>\n",
              "      <td>12.600000</td>\n",
              "      <td>11.783333</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3/31/24</th>\n",
              "      <td>12.70</td>\n",
              "      <td>12.566667</td>\n",
              "      <td>11.841667</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4/30/24</th>\n",
              "      <td>12.10</td>\n",
              "      <td>12.400000</td>\n",
              "      <td>11.891667</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5/31/24</th>\n",
              "      <td>11.40</td>\n",
              "      <td>12.066667</td>\n",
              "      <td>11.908333</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6/30/24</th>\n",
              "      <td>11.50</td>\n",
              "      <td>11.666667</td>\n",
              "      <td>11.958333</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7/31/24</th>\n",
              "      <td>11.60</td>\n",
              "      <td>11.500000</td>\n",
              "      <td>11.975000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8/31/24</th>\n",
              "      <td>11.50</td>\n",
              "      <td>11.533333</td>\n",
              "      <td>12.008333</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9/30/24</th>\n",
              "      <td>11.80</td>\n",
              "      <td>11.633333</td>\n",
              "      <td>12.033333</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "          Prices      SMA_3     SMA_12\n",
              "Dates                                 \n",
              "10/31/20   10.10        NaN        NaN\n",
              "11/30/20   10.30        NaN        NaN\n",
              "12/31/20   11.00  10.466667        NaN\n",
              "1/31/21    10.90  10.733333        NaN\n",
              "2/28/21    10.90  10.933333        NaN\n",
              "3/31/21    10.90  10.900000        NaN\n",
              "4/30/21    10.40  10.733333        NaN\n",
              "5/31/21     9.84  10.380000        NaN\n",
              "6/30/21    10.00  10.080000        NaN\n",
              "7/31/21    10.10   9.980000        NaN\n",
              "8/31/21    10.30  10.133333        NaN\n",
              "9/30/21    10.20  10.200000  10.411667\n",
              "10/31/21   10.10  10.200000  10.411667\n",
              "11/30/21   11.20  10.500000  10.486667\n",
              "12/31/21   11.40  10.900000  10.520000\n",
              "1/31/22    11.50  11.366667  10.570000\n",
              "2/28/22    11.80  11.566667  10.645000\n",
              "3/31/22    11.50  11.600000  10.695000\n",
              "4/30/22    10.70  11.333333  10.720000\n",
              "5/31/22    10.70  10.966667  10.791667\n",
              "6/30/22    10.40  10.600000  10.825000\n",
              "7/31/22    10.50  10.533333  10.858333\n",
              "8/31/22    10.40  10.433333  10.866667\n",
              "9/30/22    10.80  10.566667  10.916667\n",
              "10/31/22   11.00  10.733333  10.991667\n",
              "11/30/22   11.60  11.133333  11.025000\n",
              "12/31/22   11.60  11.400000  11.041667\n",
              "1/31/23    12.10  11.766667  11.091667\n",
              "2/28/23    11.70  11.800000  11.083333\n",
              "3/31/23    12.00  11.933333  11.125000\n",
              "4/30/23    11.50  11.733333  11.191667\n",
              "5/31/23    11.20  11.566667  11.233333\n",
              "6/30/23    10.90  11.200000  11.275000\n",
              "7/31/23    11.40  11.166667  11.350000\n",
              "8/31/23    11.10  11.133333  11.408333\n",
              "9/30/23    11.50  11.333333  11.466667\n",
              "10/31/23   11.80  11.466667  11.533333\n",
              "11/30/23   12.20  11.833333  11.583333\n",
              "12/31/23   12.80  12.266667  11.683333\n",
              "1/31/24    12.60  12.533333  11.725000\n",
              "2/29/24    12.40  12.600000  11.783333\n",
              "3/31/24    12.70  12.566667  11.841667\n",
              "4/30/24    12.10  12.400000  11.891667\n",
              "5/31/24    11.40  12.066667  11.908333\n",
              "6/30/24    11.50  11.666667  11.958333\n",
              "7/31/24    11.60  11.500000  11.975000\n",
              "8/31/24    11.50  11.533333  12.008333\n",
              "9/30/24    11.80  11.633333  12.033333"
            ]
          },
          "execution_count": 9,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "Nat_gas"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 850
        },
        "id": "0VShY_QGxgM2",
        "outputId": "36b0ed90-ff85-4c5c-ae28-ea9a320758e6"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x1000 with 1 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        }
      ],
      "source": [
        "Nat_gas.loc[:,[\"Prices\",\"SMA_3\",\"SMA_12\"]].plot(figsize=(10,10))\n",
        "plt.legend(loc=\"upper left\",fontsize=8)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 238
        },
        "id": "8m3WetBvxgM3",
        "outputId": "a0c73c66-7126-4e77-8102-4916764d2273"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "         time  Prices\n",
              "Dates                \n",
              "5/31/24    44    11.4\n",
              "6/30/24    45    11.5\n",
              "7/31/24    46    11.6\n",
              "8/31/24    47    11.5\n",
              "9/30/24    48    11.8"
            ],
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              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
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              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
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              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
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              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
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              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
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              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-3d190e01-65bd-44d9-b4c0-e42936eaee2e button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"data\",\n  \"rows\": 5,\n  \"fields\": [\n    {\n      \"column\": \"time\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 44,\n        \"max\": 48,\n        \"samples\": [\n          45,\n          48,\n          46\n        ],\n        \"num_unique_values\": 5,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Prices\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.15165750888103116,\n        \"min\": 11.4,\n        \"max\": 11.8,\n        \"samples\": [\n          11.5,\n          11.8,\n          11.4\n        ],\n        \"num_unique_values\": 4,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 11
        }
      ],
      "source": [
        "time = np.arange(1, len(Nat_gas) + 1)\n",
        "Nat_gas['time'] = time\n",
        "data = Nat_gas[['time', 'Prices']]\n",
        "data.tail()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nl3e1VkZxgM3",
        "outputId": "3b224f8a-5da0-43a5-8cb6-a7c6bde96692"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([ 1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,\n",
              "       18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34,\n",
              "       35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48])"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ],
      "source": [
        "time"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "scrolled": true,
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 238
        },
        "id": "S_7ndWjcxgM3",
        "outputId": "94807cfd-1c49-4358-8dff-05d98121f09c"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "         Prices      SMA_3     SMA_12  time\n",
              "Dates                                      \n",
              "5/31/24    11.4  12.066667  11.908333    44\n",
              "6/30/24    11.5  11.666667  11.958333    45\n",
              "7/31/24    11.6  11.500000  11.975000    46\n",
              "8/31/24    11.5  11.533333  12.008333    47\n",
              "9/30/24    11.8  11.633333  12.033333    48"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-6de81985-6e95-4274-9d01-ef323f7e23d4\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
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              "        vertical-align: middle;\n",
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              "      <th>5/31/24</th>\n",
              "      <td>11.4</td>\n",
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              "      <th>6/30/24</th>\n",
              "      <td>11.5</td>\n",
              "      <td>11.666667</td>\n",
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              "      <td>45</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7/31/24</th>\n",
              "      <td>11.6</td>\n",
              "      <td>11.500000</td>\n",
              "      <td>11.975000</td>\n",
              "      <td>46</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8/31/24</th>\n",
              "      <td>11.5</td>\n",
              "      <td>11.533333</td>\n",
              "      <td>12.008333</td>\n",
              "      <td>47</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9/30/24</th>\n",
              "      <td>11.8</td>\n",
              "      <td>11.633333</td>\n",
              "      <td>12.033333</td>\n",
              "      <td>48</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
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              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
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              "      display: none;\n",
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              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
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              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
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              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-6de81985-6e95-4274-9d01-ef323f7e23d4 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-6de81985-6e95-4274-9d01-ef323f7e23d4');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
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              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
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              "\n",
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              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
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              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-61284d45-4132-4547-9bfe-bd1da5f4b7af button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"Nat_gas\",\n  \"rows\": 5,\n  \"fields\": [\n    {\n      \"column\": \"Prices\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.15165750888103116,\n        \"min\": 11.4,\n        \"max\": 11.8,\n        \"samples\": [\n          11.5,\n          11.8,\n          11.4\n        ],\n        \"num_unique_values\": 4,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"SMA_3\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.22681367781605405,\n        \"min\": 11.5,\n        \"max\": 12.066666666666668,\n        \"samples\": [\n          11.666666666666666,\n          11.633333333333333,\n          11.5\n        ],\n        \"num_unique_values\": 5,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"SMA_12\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.04801620096962632,\n        \"min\": 11.908333333333333,\n        \"max\": 12.033333333333333,\n        \"samples\": [\n          11.958333333333334,\n          12.033333333333333,\n          11.975\n        ],\n        \"num_unique_values\": 5,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"time\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 44,\n        \"max\": 48,\n        \"samples\": [\n          45,\n          48,\n          46\n        ],\n        \"num_unique_values\": 5,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 13
        }
      ],
      "source": [
        "Nat_gas.tail()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kdFIlRXoxgM3",
        "outputId": "0c8ccdd2-1c46-42d5-aecd-de6252ad6540"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([ 0.03901324, 10.25125887])"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ],
      "source": [
        "reg = np.polyfit(data['time'], data[\"Prices\"], deg = 1)\n",
        "reg"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 522
        },
        "id": "0RylxGTKxgM3",
        "outputId": "b90fdfe6-6cc2-4ec8-ff68-7c1f8508687c"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "trend = np.polyval(reg, data['time'])\n",
        "std = data['Prices'].std()\n",
        "plt.figure(figsize=(10,6))\n",
        "plt.plot(data['time'].values, data['Prices'].values)\n",
        "plt.plot(data['time'].values, trend, 'r--')\n",
        "plt.plot(data['time'].values, trend - std, 'g--')\n",
        "plt.plot(data['time'].values, trend + std, 'g--');"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "7KUSGwG7xgM4"
      },
      "outputs": [],
      "source": [
        "from statsmodels.tsa.seasonal import seasonal_decompose"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "cbWYNTbwxgM4"
      },
      "outputs": [],
      "source": [
        "result = seasonal_decompose(Nat_gas['Prices'], model='multiplicative',period = 12)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "scrolled": true,
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 487
        },
        "id": "9Y_yb3LsxgM4",
        "outputId": "387a9e83-6c13-4a19-be3a-fdf93a0f7457"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 4 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "result.plot()\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 592
        },
        "id": "o23K4cm5xgM4",
        "outputId": "cbaad7b8-199a-4f10-8fc3-f228b7897d46"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1600x700 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "plt.figure(figsize = (16,7))\n",
        "result.seasonal.plot();"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 592
        },
        "id": "Q_wh-a-8xgM4",
        "outputId": "b93003ad-adb2-483b-c39b-ce1badd917fe"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1600x700 with 1 Axes>"
            ],
            "image/png": 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DyZMn4ezsDJVKZfDemsbhf/7zH7Rv315/BSWvULRco0kolpaWIicnB99++y2WLFmCVq1aITMzEwAQFRWFYcOGGb2nsLAQmZmZOHDgALy8vBAZGWmybXMJxRs3bsDV1dXkyXdRURFef/11/Pa3vzX7mZlQbDxiYmLQrl07xMTEICMjA3v37kWLFi0MTqRPnDiBzp07G7337t27yMrKwpEjR+Dn54fZs2eb3Ia5ZE5xcTFUKhUKCwtNlr355psIDg5GWVmZyXaZUGycTJ1Y22oMajQatGrVCqtXr65+x8hmOTg4ICgoyGDdRx99hLfeeku/bE0MWnIS87LjcUpKClxcXLBnzx7LdpZskrUJRWvmiZYkeMzFO2BZv7d//364uLjg1KlTZuuQbSksLISHhweuXbumX/fTsdMW4y0/Px8ZGRnYvn07WrRoYbQtalhyc3Pxy1/+EiICpVKJfv36YcqUKejWrRsA2xyLLT0vp4YnPDwc3t7e+Pe//22w3lxfCJifl92/fx9jx46FnZ0dlEolfH19MWfOHDg5OenrWBOH48ePx+LFi/XLTChartEkFH9q+PDhmDVrFgAgODgYGzdurLJ+5YOKtVqtUZm5hOKnn36KcePGGa2/ffs2fHx8MHXq1Cp/XWFCsfFo166d0dVZq1evRteuXfXL4eHhWLBgQZXt/P3vf4eI4M6dO0Zl5pI5sbGx6NWrl9H6J0+eICgoCMOHDzd7xRnAhGJjZerE2hZjMDMzEx4eHlVevUgNQ4cOHYwmaFFRUWjbtq1+2ZoY/MUvfmEUd7t27YKbm5t++WXG47S0NLi6uvJPCBoBaxOK1swT27dvbzQ3/OMf/4iAgAD9srl4t6Tfi4mJgbOzs9krLMg2HT58WJ/EqXyJCBQKBZRKJbRarU3G24tWr16tv5KNGraSkhL9WDpx4kSMHj0agG2OxS+q6rycGpa5c+eiXbt2yM/PNyoz1xdaMi97/vw5ioqKUFFRgYiICPj5+enLrIlDd3d3g37bzs5O35d/+eWXNdnln51G9wzFShUVFVJaWiolJSWSmppa5b35lfXLy8uloqLC4m0cOXLEqN3bt2/LkCFD5I033pDo6Gixs2u0h5he8OzZM6PvWqlU6uMJgBw9etSiOBT58fkQljIVh0+ePJF33nlHVCqVJCQkGD1rgn5+bDEGMzMzZejQoTJ9+nRZs2aNxdsj2zRw4EC5efOmwbrs7Gzx9vYWEetjMCgoSM6cOWNQ59SpUwbPSbZ2PE5LS5OQkBCJjIyUWbNmWbin1JhYO0+sLi7Nxbsl/V5MTIzMnDlTYmJiJCQkxNpdo3owfPhw0Wg0kp6ern/17dtXpkyZIunp6fL8+XObizdT267JHIBsl6urq3h6esrjx48lOTlZxo4da5Njsalt1/S8nGwLAPnwww/l8OHDkpKSIp06dTIoNzf2Wjovc3JyEi8vL9FqtXLo0CGDdqyJw4sXLxr026tWrZKmTZtKenq6jB8/3trD8PNQj8nMWrNkyRKcPXsWBQUFyMjIwJIlS6BQKHDy5EkcPHgQ/v7+BvW/+uorxMXF4caNG8jLy0NcXBzatm1r8O9ApaWlUKvVUKvV8PT0xKJFi6BWq5GTkwMAuHfvHhwcHPDgwQP9e4qKitClSxcMHz4cRUVFuHv3rv71oszMTKjVaoSGhmLIkCH67VDDNX36dHh5eeHYsWMoKChAfHw8WrVqhYiICADA5cuX0bx5c4N/uD1+/Dh27doFjUaDgoICHDt2DN27dzd6vkRlfLzxxhv4zW9+A7Varb+dv7y8HM2aNcOVK1f09YuLi9G/f3/4+/sjNzfXIA5f/KUvJycHarUaH3zwAXx9ffXbKS0trctDRXXo6dOn+u9RRLBhwwao1Wp89913NheDGo0GrVu3RlhYmEH5/fv3X8GRorpw6dIl2NvbY82aNcjJydHfrvnVV18BsL4fzM/Ph4uLC/7whz8gKysL27Ztg1KpRFJSEgDrx+PK22mWLl1qUP7w4cO6PlRUi6rq9wDg4cOHUKvV+lvzYmNjoVar9bFg7Tyx8nl0f/nLX5CVlYWVK1fCwcEBGo0GgOl4t6Tf279/P+zt7bFt2zaDOv/973/r7BhS3Xrx6n5bi7etW7ciISEB2dnZyM7Oxs6dO9G0aVODfwOmhicpKQmJiYnIz8/HyZMnERgYiP79+6OsrMzmxmJL4p8antmzZ8Pd3R1paWkG33vl7fGm+kJL5mX//Oc/cejQIeTl5eHcuXMYNmwYOnXqpH8O48vkaF7EW54t1ygSiu+//z68vb2hUqnQunVrDB8+HCdPngQAhIWFGQ2KsbGx6NOnD5o0aQJXV1f4+flh7dq1BrfkVd4a89PX4MGDAQA7d+40OumOjo42+Z6f5m29vb2rrUMNy5MnTzB//nx06NABTk5O6Ny5M5YvX65Pzq1YscJoYExJSUFQUBDc3d3h5OQEHx8fLF682OjBtKZixdvbGwBw+vRptGvXzqB+amqq2TgsKCjQ1xs8eHC1dahhMffdT58+3eZicOXKlVW2Sw3T0aNH0bNnTzg6OqJbt24GD7x+mRhMTU1Fr169oFKp0LlzZ0RHR+vLrB2Pp0+fXuU4Tw1DVf0eYD4WVq5cCcD6eSIAfP311/D19YVKpUKPHj0M/hDQVLxb0u+ZG5sr94canhcTirYWb5s3b0aPHj3g4uICNzc39O7dG1FRUfxDjAYuLi4OnTt3hkqlQps2bTB37lz9jxK2NhZbGv/UsJj73itjxlRfaMm8LC0tDd27d4ejoyNatmyJqVOn4vbt2/ryl8nR/PQ9TChaRgEA0khptVp57bXXJDExUd58881abftXv/qVDBo0SCIiImq1XWqcAgICZMWKFTJx4sRabXfevHmi1WolKiqqVtulxocxSPWtrmKQ4zFZqy7niXUV79RwMd7IFnAspvrGHE3jYl/fH6AuPXr0SD7++GPp169frbc9aNAgmTx5cq23S41PWVmZTJgwQUaNGlXrbffs2dPg2SVEpjAGqb7VZQxyPCZr1dU8sS7jnRouxhvVN47FZAuYo2lcGvUVikRERERERERERFS7+BfEREREREREREREZDEmFImIiIiIiIiIiMhiTCgSERERERERERGRxZhQJCIiIiIiIiIiIosxoUhEREREREREREQWY0KRiIiIiIiIiIiILMaEIhERERFVacaMGaJQKEShUIiDg4O89tprMmLECNm1a5dUVFRY3M7u3bulWbNmdfdBiYiIiOiVYEKRiIiIiKoVHBwsd+/elVu3bkliYqIMHTpU5s+fL2PGjBGtVlvfH4+IiIiIXiEmFImIiIioWo6OjtKmTRvx8vKSPn36yLJly+TIkSOSmJgou3fvFhGRDRs2iL+/v7i6ukr79u1lzpw5UlJSIiIiaWlpMnPmTCkuLtZf7finP/1JRERKS0tl0aJF4uXlJa6urtK/f39JS0vTb/u7776T0NBQad68ubi6ukqPHj3kxIkTr/gIEBEREVElJhSJiIiIyCrDhg2TwMBAiY+PFxEROzs72bx5s2RmZsqePXskJSVFIiIiRERkwIABsnHjRnFzc5O7d+/K3bt3ZdGiRSIi8uGHH8rFixclNjZWMjIy5L333pPg4GDJyckREZG5c+dKaWmpnDt3TjQajURGRkqTJk3qZ6eJiIiISOzr+wMQERERUcPVrVs3ycjIEBGRBQsW6Nd37NhR/vznP0t4eLhERUWJSqUSd3d3USgU0qZNG329wsJCiY6OlsLCQmnbtq2IiCxatEiSkpIkOjpa1q5dK4WFhTJhwgTx9/cXEZHOnTu/uh0kIiIiIiNMKBIRERGR1QCIQqEQEZHTp0/LunXr5F//+pc8efJEtFqt/PDDD/Ls2TNxcXEx+X6NRiM6nU58fX0N1peWlkrLli1FRGTevHkye/ZsOXnypLz99tsyYcIECQgIqNsdIyIiIiKzeMszEREREVktKytLOnXqJLdu3ZIxY8ZIQECAHDp0SK5cuSLbtm0TEZGysjKz7y8pKRGlUilXrlyR9PR0/SsrK0s2bdokIiK/+93vJD8/X6ZOnSoajUb69u0rW7ZseSX7R0RERETGmFAkIiIiIqukpKSIRqORCRMmyJUrV6SiokI+++wzeeutt8TX11fu3LljUF+lUolOpzNY17t3b9HpdHL//n3p0qWLwevFW6Pbt28v4eHhEh8fLwsXLpQdO3a8kn0kIiIiImO85ZmIiIiIqlVaWirff/+96HQ6uXfvniQlJcm6detkzJgxMm3aNLl+/bqUl5fLli1bJDQ0VC5cuCB//etfDdro2LGjlJSUyJkzZyQwMFBcXFzE19dXpkyZItOmTZPPPvtMevfuLQ8ePJAzZ85IQECAhISEyIIFC2TUqFHi6+srjx8/ltTUVOnevXs9HQkiIiIi4hWKRERERFStpKQk8fT0lI4dO0pwcLCkpqbK5s2b5ciRI6JUKiUwMFA2bNggkZGR0rNnT9m/f7+sW7fOoI0BAwZIeHi4TJo0SVq3bi3r168XEZHo6GiZNm2aLFy4ULp27Srjxo2Ty5cvS4cOHURERKfTydy5c6V79+4SHBwsvr6+EhUV9cqPARERERH9SAEA9f0hiIiIiIiIiIiIqGHgFYpERERERERERERkMSYUiYiIiIiIiIiIyGJMKBIREREREREREZHFmFAkIiIiIiIiIiIiizGhSERERERERERERBZjQpGIiIiIiIiIiIgsxoQiERERERERERERWYwJRSIiIiIiIiIiIrIYE4pERERERERERERkMSYUiYiIiIiIiIiIyGJMKBIREREREREREZHFmFAkIiIiIiIiIiIii/0fZ5CGIji+BYMAAAAASUVORK5CYII=\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "plt.figure(figsize = (16,7))\n",
        "result.trend.plot();"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PIXME15FxgM4"
      },
      "source": [
        "# Forecasting using SARIMA"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "1dGWXSMTxgM5"
      },
      "outputs": [],
      "source": [
        "# Import data\n",
        "Nat_gas_df = pd.read_csv(\"/content/Nat_Gas.csv\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "FVsHZ3r-xgM5",
        "outputId": "baca5997-e5d6-4a34-bf80-a21ca37f0bf8"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Dates</th>\n",
              "      <th>Prices</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>10/31/20</td>\n",
              "      <td>10.1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>11/30/20</td>\n",
              "      <td>10.3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>12/31/20</td>\n",
              "      <td>11.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1/31/21</td>\n",
              "      <td>10.9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2/28/21</td>\n",
              "      <td>10.9</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "      Dates  Prices\n",
              "0  10/31/20    10.1\n",
              "1  11/30/20    10.3\n",
              "2  12/31/20    11.0\n",
              "3   1/31/21    10.9\n",
              "4   2/28/21    10.9"
            ]
          },
          "execution_count": 29,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "Nat_gas_df.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "6jMjDIoKxgM6"
      },
      "outputs": [],
      "source": [
        "from statsmodels.tsa.statespace.sarimax import SARIMAX"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "pvHPacecxgM6"
      },
      "outputs": [],
      "source": [
        "# Convert Month into Datetime\n",
        "Nat_gas_df['Dates']=pd.to_datetime(Nat_gas_df['Dates'])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "l8VMEV4GxgM6",
        "outputId": "63bfbee0-1d84-4f62-b115-dc102f7a213f"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       Dates  Prices\n",
              "0 2020-10-31    10.1\n",
              "1 2020-11-30    10.3\n",
              "2 2020-12-31    11.0\n",
              "3 2021-01-31    10.9\n",
              "4 2021-02-28    10.9"
            ],
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "Nat_gas_df",
              "summary": "{\n  \"name\": \"Nat_gas_df\",\n  \"rows\": 48,\n  \"fields\": [\n    {\n      \"column\": \"Dates\",\n      \"properties\": {\n        \"dtype\": \"date\",\n        \"min\": \"2020-10-31 00:00:00\",\n        \"max\": \"2024-09-30 00:00:00\",\n        \"samples\": [\n          \"2023-01-31 00:00:00\",\n          \"2024-02-29 00:00:00\",\n          \"2022-12-31 00:00:00\"\n        ],\n        \"num_unique_values\": 48,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Prices\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.7578973105463123,\n        \"min\": 9.84,\n        \"max\": 12.8,\n        \"samples\": [\n          11.2,\n          12.1,\n          10.1\n        ],\n        \"num_unique_values\": 25,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 25
        }
      ],
      "source": [
        "Nat_gas_df.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "mBdoFxdpxgM6"
      },
      "outputs": [],
      "source": [
        "Nat_gas_df.set_index('Dates',inplace=True)"
      ]
    },
    {
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      "execution_count": null,
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        "id": "1-E2jgorxgM6",
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      },
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        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "            Prices\n",
              "Dates             \n",
              "2020-10-31    10.1\n",
              "2020-11-30    10.3\n",
              "2020-12-31    11.0\n",
              "2021-01-31    10.9\n",
              "2021-02-28    10.9"
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              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-edc4a930-dac6-455b-b4db-f45ae310a506 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "Nat_gas_df",
              "summary": "{\n  \"name\": \"Nat_gas_df\",\n  \"rows\": 48,\n  \"fields\": [\n    {\n      \"column\": \"Prices\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.7578973105463123,\n        \"min\": 9.84,\n        \"max\": 12.8,\n        \"samples\": [\n          11.2,\n          12.1,\n          10.1\n        ],\n        \"num_unique_values\": 25,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 27
        }
      ],
      "source": [
        "Nat_gas_df.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 483
        },
        "id": "4YE9s0SyxgM6",
        "outputId": "b18cad53-0bcb-4144-c69d-b8fbf2e7bb07"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: xlabel='Dates'>"
            ]
          },
          "metadata": {},
          "execution_count": 28
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "Nat_gas_df.plot()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "y7-TOoU_xgM6"
      },
      "outputs": [],
      "source": [
        "### Testing For Stationarity\n",
        "\n",
        "from statsmodels.tsa.stattools import adfuller"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "FndU4YI7xgM7"
      },
      "outputs": [],
      "source": [
        "test_result=adfuller(Nat_gas_df['Prices'])\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ed3FHjl9xgM7"
      },
      "outputs": [],
      "source": [
        "#Ho: It is non stationary\n",
        "#H1: It is stationary\n",
        "\n",
        "def adfuller_test(Prices):\n",
        "    result=adfuller(Prices)\n",
        "    labels = ['ADF Test Statistic','p-value','#Lags Used','Number of Observations Used']\n",
        "    for value,label in zip(result,labels):\n",
        "        print(label+' : '+str(value) )\n",
        "    if result[1] <= 0.05:\n",
        "        print(\"strong evidence against the null hypothesis(Ho), reject the null hypothesis. Data has no unit root and is stationary\")\n",
        "    else:\n",
        "        print(\"weak evidence against null hypothesis, time series has a unit root, indicating it is non-stationary \")\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Of2IXCRbxgM7",
        "outputId": "4ed6f18f-73ba-474d-b7d4-7ab37c1abc7a"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "ADF Test Statistic : 0.21807686169999427\n",
            "p-value : 0.973257438844869\n",
            "#Lags Used : 10\n",
            "Number of Observations Used : 37\n",
            "weak evidence against null hypothesis, time series has a unit root, indicating it is non-stationary \n"
          ]
        }
      ],
      "source": [
        "adfuller_test(Nat_gas_df['Prices'])\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pip install pmdarima"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "aN_OsKvKzU0w",
        "outputId": "7161db4f-b339-4bb3-87c5-26128a251194"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting pmdarima\n",
            "  Downloading pmdarima-2.0.4-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.manylinux_2_28_x86_64.whl (2.1 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m2.1/2.1 MB\u001b[0m \u001b[31m8.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: joblib>=0.11 in /usr/local/lib/python3.10/dist-packages (from pmdarima) (1.3.2)\n",
            "Requirement already satisfied: Cython!=0.29.18,!=0.29.31,>=0.29 in /usr/local/lib/python3.10/dist-packages (from pmdarima) (3.0.8)\n",
            "Requirement already satisfied: numpy>=1.21.2 in /usr/local/lib/python3.10/dist-packages (from pmdarima) (1.25.2)\n",
            "Requirement already satisfied: pandas>=0.19 in /usr/local/lib/python3.10/dist-packages (from pmdarima) (1.5.3)\n",
            "Requirement already satisfied: scikit-learn>=0.22 in /usr/local/lib/python3.10/dist-packages (from pmdarima) (1.2.2)\n",
            "Requirement already satisfied: scipy>=1.3.2 in /usr/local/lib/python3.10/dist-packages (from pmdarima) (1.11.4)\n",
            "Requirement already satisfied: statsmodels>=0.13.2 in /usr/local/lib/python3.10/dist-packages (from pmdarima) (0.14.1)\n",
            "Requirement already satisfied: urllib3 in /usr/local/lib/python3.10/dist-packages (from pmdarima) (2.0.7)\n",
            "Requirement already satisfied: setuptools!=50.0.0,>=38.6.0 in /usr/local/lib/python3.10/dist-packages (from pmdarima) (67.7.2)\n",
            "Requirement already satisfied: packaging>=17.1 in /usr/local/lib/python3.10/dist-packages (from pmdarima) (23.2)\n",
            "Requirement already satisfied: python-dateutil>=2.8.1 in /usr/local/lib/python3.10/dist-packages (from pandas>=0.19->pmdarima) (2.8.2)\n",
            "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.10/dist-packages (from pandas>=0.19->pmdarima) (2023.4)\n",
            "Requirement already satisfied: threadpoolctl>=2.0.0 in /usr/local/lib/python3.10/dist-packages (from scikit-learn>=0.22->pmdarima) (3.2.0)\n",
            "Requirement already satisfied: patsy>=0.5.4 in /usr/local/lib/python3.10/dist-packages (from statsmodels>=0.13.2->pmdarima) (0.5.6)\n",
            "Requirement already satisfied: six in /usr/local/lib/python3.10/dist-packages (from patsy>=0.5.4->statsmodels>=0.13.2->pmdarima) (1.16.0)\n",
            "Installing collected packages: pmdarima\n",
            "Successfully installed pmdarima-2.0.4\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "BajG9qD9xgM7"
      },
      "outputs": [],
      "source": [
        "import pmdarima as pm\n",
        "from pmdarima.model_selection import train_test_split"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 894
        },
        "id": "1jQXHENxxgM7",
        "outputId": "b7244e8a-7e7f-4e2e-88d7-5677518401b8"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Performing stepwise search to minimize aic\n",
            " ARIMA(2,1,2)(1,0,1)[12] intercept   : AIC=37.147, Time=1.98 sec\n",
            " ARIMA(0,1,0)(0,0,0)[12] intercept   : AIC=48.334, Time=0.09 sec\n",
            " ARIMA(1,1,0)(1,0,0)[12] intercept   : AIC=38.394, Time=0.41 sec\n",
            " ARIMA(0,1,1)(0,0,1)[12] intercept   : AIC=42.336, Time=0.61 sec\n",
            " ARIMA(0,1,0)(0,0,0)[12]             : AIC=46.741, Time=0.09 sec\n",
            " ARIMA(2,1,2)(0,0,1)[12] intercept   : AIC=inf, Time=1.64 sec\n",
            " ARIMA(2,1,2)(1,0,0)[12] intercept   : AIC=inf, Time=2.71 sec\n",
            " ARIMA(2,1,2)(2,0,1)[12] intercept   : AIC=39.069, Time=4.75 sec\n",
            " ARIMA(2,1,2)(1,0,2)[12] intercept   : AIC=38.330, Time=5.04 sec\n",
            " ARIMA(2,1,2)(0,0,0)[12] intercept   : AIC=36.109, Time=1.42 sec\n",
            " ARIMA(1,1,2)(0,0,0)[12] intercept   : AIC=53.197, Time=0.35 sec\n",
            " ARIMA(2,1,1)(0,0,0)[12] intercept   : AIC=53.566, Time=0.33 sec\n",
            " ARIMA(3,1,2)(0,0,0)[12] intercept   : AIC=53.701, Time=2.22 sec\n",
            " ARIMA(2,1,3)(0,0,0)[12] intercept   : AIC=53.674, Time=1.34 sec\n",
            " ARIMA(1,1,1)(0,0,0)[12] intercept   : AIC=51.953, Time=0.57 sec\n",
            " ARIMA(1,1,3)(0,0,0)[12] intercept   : AIC=54.043, Time=0.63 sec\n",
            " ARIMA(3,1,1)(0,0,0)[12] intercept   : AIC=inf, Time=2.06 sec\n",
            " ARIMA(3,1,3)(0,0,0)[12] intercept   : AIC=inf, Time=3.32 sec\n",
            " ARIMA(2,1,2)(0,0,0)[12]             : AIC=inf, Time=1.43 sec\n",
            "\n",
            "Best model:  ARIMA(2,1,2)(0,0,0)[12] intercept\n",
            "Total fit time: 31.162 seconds\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<class 'statsmodels.iolib.summary.Summary'>\n",
              "\"\"\"\n",
              "                               SARIMAX Results                                \n",
              "==============================================================================\n",
              "Dep. Variable:                      y   No. Observations:                   48\n",
              "Model:               SARIMAX(2, 1, 2)   Log Likelihood                 -12.055\n",
              "Date:                Sat, 17 Feb 2024   AIC                             36.109\n",
              "Time:                        06:43:31   BIC                             47.210\n",
              "Sample:                    10-31-2020   HQIC                            40.287\n",
              "                         - 09-30-2024                                         \n",
              "Covariance Type:                  opg                                         \n",
              "==============================================================================\n",
              "                 coef    std err          z      P>|z|      [0.025      0.975]\n",
              "------------------------------------------------------------------------------\n",
              "intercept      0.0098      0.008      1.276      0.202      -0.005       0.025\n",
              "ar.L1          1.6926      0.050     33.525      0.000       1.594       1.792\n",
              "ar.L2         -0.9590      0.048    -19.946      0.000      -1.053      -0.865\n",
              "ma.L1         -1.8031      0.277     -6.501      0.000      -2.347      -1.260\n",
              "ma.L2          0.9498      0.307      3.094      0.002       0.348       1.552\n",
              "sigma2         0.0896      0.030      3.003      0.003       0.031       0.148\n",
              "===================================================================================\n",
              "Ljung-Box (L1) (Q):                   6.30   Jarque-Bera (JB):                 2.32\n",
              "Prob(Q):                              0.01   Prob(JB):                         0.31\n",
              "Heteroskedasticity (H):               0.86   Skew:                             0.51\n",
              "Prob(H) (two-sided):                  0.76   Kurtosis:                         2.60\n",
              "===================================================================================\n",
              "\n",
              "Warnings:\n",
              "[1] Covariance matrix calculated using the outer product of gradients (complex-step).\n",
              "\"\"\""
            ],
            "text/html": [
              "<table class=\"simpletable\">\n",
              "<caption>SARIMAX Results</caption>\n",
              "<tr>\n",
              "  <th>Dep. Variable:</th>           <td>y</td>        <th>  No. Observations:  </th>   <td>48</td>   \n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Model:</th>           <td>SARIMAX(2, 1, 2)</td> <th>  Log Likelihood     </th> <td>-12.055</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Date:</th>            <td>Sat, 17 Feb 2024</td> <th>  AIC                </th> <td>36.109</td> \n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Time:</th>                <td>06:43:31</td>     <th>  BIC                </th> <td>47.210</td> \n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Sample:</th>             <td>10-31-2020</td>    <th>  HQIC               </th> <td>40.287</td> \n",
              "</tr>\n",
              "<tr>\n",
              "  <th></th>                   <td>- 09-30-2024</td>   <th>                     </th>    <td> </td>   \n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Covariance Type:</th>        <td>opg</td>       <th>                     </th>    <td> </td>   \n",
              "</tr>\n",
              "</table>\n",
              "<table class=\"simpletable\">\n",
              "<tr>\n",
              "      <td></td>         <th>coef</th>     <th>std err</th>      <th>z</th>      <th>P>|z|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
              "</tr>\n",
              "<tr>\n",
              "  <th>intercept</th> <td>    0.0098</td> <td>    0.008</td> <td>    1.276</td> <td> 0.202</td> <td>   -0.005</td> <td>    0.025</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>ar.L1</th>     <td>    1.6926</td> <td>    0.050</td> <td>   33.525</td> <td> 0.000</td> <td>    1.594</td> <td>    1.792</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>ar.L2</th>     <td>   -0.9590</td> <td>    0.048</td> <td>  -19.946</td> <td> 0.000</td> <td>   -1.053</td> <td>   -0.865</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>ma.L1</th>     <td>   -1.8031</td> <td>    0.277</td> <td>   -6.501</td> <td> 0.000</td> <td>   -2.347</td> <td>   -1.260</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>ma.L2</th>     <td>    0.9498</td> <td>    0.307</td> <td>    3.094</td> <td> 0.002</td> <td>    0.348</td> <td>    1.552</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>sigma2</th>    <td>    0.0896</td> <td>    0.030</td> <td>    3.003</td> <td> 0.003</td> <td>    0.031</td> <td>    0.148</td>\n",
              "</tr>\n",
              "</table>\n",
              "<table class=\"simpletable\">\n",
              "<tr>\n",
              "  <th>Ljung-Box (L1) (Q):</th>     <td>6.30</td> <th>  Jarque-Bera (JB):  </th> <td>2.32</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Prob(Q):</th>                <td>0.01</td> <th>  Prob(JB):          </th> <td>0.31</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Heteroskedasticity (H):</th> <td>0.86</td> <th>  Skew:              </th> <td>0.51</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Prob(H) (two-sided):</th>    <td>0.76</td> <th>  Kurtosis:          </th> <td>2.60</td>\n",
              "</tr>\n",
              "</table><br/><br/>Warnings:<br/>[1] Covariance matrix calculated using the outer product of gradients (complex-step)."
            ],
            "text/latex": "\\begin{center}\n\\begin{tabular}{lclc}\n\\toprule\n\\textbf{Dep. Variable:}          &        y         & \\textbf{  No. Observations:  } &     48      \\\\\n\\textbf{Model:}                  & SARIMAX(2, 1, 2) & \\textbf{  Log Likelihood     } &  -12.055    \\\\\n\\textbf{Date:}                   & Sat, 17 Feb 2024 & \\textbf{  AIC                } &   36.109    \\\\\n\\textbf{Time:}                   &     06:43:31     & \\textbf{  BIC                } &   47.210    \\\\\n\\textbf{Sample:}                 &    10-31-2020    & \\textbf{  HQIC               } &   40.287    \\\\\n\\textbf{}                        &   - 09-30-2024   & \\textbf{                     } &             \\\\\n\\textbf{Covariance Type:}        &       opg        & \\textbf{                     } &             \\\\\n\\bottomrule\n\\end{tabular}\n\\begin{tabular}{lcccccc}\n                   & \\textbf{coef} & \\textbf{std err} & \\textbf{z} & \\textbf{P$> |$z$|$} & \\textbf{[0.025} & \\textbf{0.975]}  \\\\\n\\midrule\n\\textbf{intercept} &       0.0098  &        0.008     &     1.276  &         0.202        &       -0.005    &        0.025     \\\\\n\\textbf{ar.L1}     &       1.6926  &        0.050     &    33.525  &         0.000        &        1.594    &        1.792     \\\\\n\\textbf{ar.L2}     &      -0.9590  &        0.048     &   -19.946  &         0.000        &       -1.053    &       -0.865     \\\\\n\\textbf{ma.L1}     &      -1.8031  &        0.277     &    -6.501  &         0.000        &       -2.347    &       -1.260     \\\\\n\\textbf{ma.L2}     &       0.9498  &        0.307     &     3.094  &         0.002        &        0.348    &        1.552     \\\\\n\\textbf{sigma2}    &       0.0896  &        0.030     &     3.003  &         0.003        &        0.031    &        0.148     \\\\\n\\bottomrule\n\\end{tabular}\n\\begin{tabular}{lclc}\n\\textbf{Ljung-Box (L1) (Q):}     & 6.30 & \\textbf{  Jarque-Bera (JB):  } & 2.32  \\\\\n\\textbf{Prob(Q):}                & 0.01 & \\textbf{  Prob(JB):          } & 0.31  \\\\\n\\textbf{Heteroskedasticity (H):} & 0.86 & \\textbf{  Skew:              } & 0.51  \\\\\n\\textbf{Prob(H) (two-sided):}    & 0.76 & \\textbf{  Kurtosis:          } & 2.60  \\\\\n\\bottomrule\n\\end{tabular}\n%\\caption{SARIMAX Results}\n\\end{center}\n\nWarnings: \\newline\n [1] Covariance matrix calculated using the outer product of gradients (complex-step)."
          },
          "metadata": {},
          "execution_count": 36
        }
      ],
      "source": [
        "#Let's run auto_arima() function to get best p,d,q,P,D,Q values\n",
        "\n",
        "pm.auto_arima(Nat_gas_df['Prices'], seasonal=True, m=12,max_p=7, max_d=5,max_q=7, max_P=4, max_D=4,max_Q=4,\n",
        "               trace=True,error_action='ignore', suppress_warnings=True, stepwise=True).summary()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "qESR_4YVxgM7"
      },
      "outputs": [],
      "source": [
        "# Let's split the data into train and test set\n",
        "\n",
        "train_data = Nat_gas_df[:len(Nat_gas_df)-12]\n",
        "test_data = Nat_gas_df[len(Nat_gas_df)-12:]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "3Xl0Fl6mxgM7"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 640
        },
        "id": "laLLL1TQxgM8",
        "outputId": "d95cd665-e21a-4d7c-e948-af2b2fcff59f"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/statsmodels/tsa/base/tsa_model.py:473: ValueWarning: No frequency information was provided, so inferred frequency M will be used.\n",
            "  self._init_dates(dates, freq)\n",
            "/usr/local/lib/python3.10/dist-packages/statsmodels/tsa/base/tsa_model.py:473: ValueWarning: No frequency information was provided, so inferred frequency M will be used.\n",
            "  self._init_dates(dates, freq)\n",
            "/usr/local/lib/python3.10/dist-packages/statsmodels/tsa/statespace/sarimax.py:866: UserWarning: Too few observations to estimate starting parameters for seasonal ARMA. All parameters except for variances will be set to zeros.\n",
            "  warn('Too few observations to estimate starting parameters%s.'\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<class 'statsmodels.iolib.summary.Summary'>\n",
              "\"\"\"\n",
              "                                      SARIMAX Results                                       \n",
              "============================================================================================\n",
              "Dep. Variable:                               Prices   No. Observations:                   36\n",
              "Model:             SARIMAX(2, 1, 2)x(1, 1, [1], 12)   Log Likelihood                   0.168\n",
              "Date:                              Sat, 17 Feb 2024   AIC                             13.664\n",
              "Time:                                      06:44:01   BIC                             21.612\n",
              "Sample:                                  10-31-2020   HQIC                            15.663\n",
              "                                       - 09-30-2023                                         \n",
              "Covariance Type:                                opg                                         \n",
              "==============================================================================\n",
              "                 coef    std err          z      P>|z|      [0.025      0.975]\n",
              "------------------------------------------------------------------------------\n",
              "ar.L1         -1.0966    174.505     -0.006      0.995    -343.119     340.926\n",
              "ar.L2         -0.0968     17.037     -0.006      0.995     -33.488      33.295\n",
              "ma.L1         -0.2542   2038.124     -0.000      1.000   -3994.904    3994.395\n",
              "ma.L2         -0.7457   1519.857     -0.000      1.000   -2979.611    2978.119\n",
              "ar.S.L12      -0.6443      0.742     -0.869      0.385      -2.098       0.809\n",
              "ma.S.L12      -0.9986   2743.759     -0.000      1.000   -5378.668    5376.671\n",
              "sigma2         0.0156     51.605      0.000      1.000    -101.129     101.160\n",
              "===================================================================================\n",
              "Ljung-Box (L1) (Q):                   0.04   Jarque-Bera (JB):                 0.40\n",
              "Prob(Q):                              0.84   Prob(JB):                         0.82\n",
              "Heteroskedasticity (H):               0.92   Skew:                            -0.22\n",
              "Prob(H) (two-sided):                  0.90   Kurtosis:                         2.53\n",
              "===================================================================================\n",
              "\n",
              "Warnings:\n",
              "[1] Covariance matrix calculated using the outer product of gradients (complex-step).\n",
              "\"\"\""
            ],
            "text/html": [
              "<table class=\"simpletable\">\n",
              "<caption>SARIMAX Results</caption>\n",
              "<tr>\n",
              "  <th>Dep. Variable:</th>                <td>Prices</td>              <th>  No. Observations:  </th>   <td>36</td>  \n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Model:</th>           <td>SARIMAX(2, 1, 2)x(1, 1, [1], 12)</td> <th>  Log Likelihood     </th>  <td>0.168</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Date:</th>                    <td>Sat, 17 Feb 2024</td>         <th>  AIC                </th> <td>13.664</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Time:</th>                        <td>06:44:01</td>             <th>  BIC                </th> <td>21.612</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Sample:</th>                     <td>10-31-2020</td>            <th>  HQIC               </th> <td>15.663</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th></th>                           <td>- 09-30-2023</td>           <th>                     </th>    <td> </td>  \n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Covariance Type:</th>                <td>opg</td>               <th>                     </th>    <td> </td>  \n",
              "</tr>\n",
              "</table>\n",
              "<table class=\"simpletable\">\n",
              "<tr>\n",
              "      <td></td>        <th>coef</th>     <th>std err</th>      <th>z</th>      <th>P>|z|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
              "</tr>\n",
              "<tr>\n",
              "  <th>ar.L1</th>    <td>   -1.0966</td> <td>  174.505</td> <td>   -0.006</td> <td> 0.995</td> <td> -343.119</td> <td>  340.926</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>ar.L2</th>    <td>   -0.0968</td> <td>   17.037</td> <td>   -0.006</td> <td> 0.995</td> <td>  -33.488</td> <td>   33.295</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>ma.L1</th>    <td>   -0.2542</td> <td> 2038.124</td> <td>   -0.000</td> <td> 1.000</td> <td>-3994.904</td> <td> 3994.395</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>ma.L2</th>    <td>   -0.7457</td> <td> 1519.857</td> <td>   -0.000</td> <td> 1.000</td> <td>-2979.611</td> <td> 2978.119</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>ar.S.L12</th> <td>   -0.6443</td> <td>    0.742</td> <td>   -0.869</td> <td> 0.385</td> <td>   -2.098</td> <td>    0.809</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>ma.S.L12</th> <td>   -0.9986</td> <td> 2743.759</td> <td>   -0.000</td> <td> 1.000</td> <td>-5378.668</td> <td> 5376.671</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>sigma2</th>   <td>    0.0156</td> <td>   51.605</td> <td>    0.000</td> <td> 1.000</td> <td> -101.129</td> <td>  101.160</td>\n",
              "</tr>\n",
              "</table>\n",
              "<table class=\"simpletable\">\n",
              "<tr>\n",
              "  <th>Ljung-Box (L1) (Q):</th>     <td>0.04</td> <th>  Jarque-Bera (JB):  </th> <td>0.40</td> \n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Prob(Q):</th>                <td>0.84</td> <th>  Prob(JB):          </th> <td>0.82</td> \n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Heteroskedasticity (H):</th> <td>0.92</td> <th>  Skew:              </th> <td>-0.22</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Prob(H) (two-sided):</th>    <td>0.90</td> <th>  Kurtosis:          </th> <td>2.53</td> \n",
              "</tr>\n",
              "</table><br/><br/>Warnings:<br/>[1] Covariance matrix calculated using the outer product of gradients (complex-step)."
            ],
            "text/latex": "\\begin{center}\n\\begin{tabular}{lclc}\n\\toprule\n\\textbf{Dep. Variable:}          &              Prices              & \\textbf{  No. Observations:  } &     36      \\\\\n\\textbf{Model:}                  & SARIMAX(2, 1, 2)x(1, 1, [1], 12) & \\textbf{  Log Likelihood     } &   0.168     \\\\\n\\textbf{Date:}                   &         Sat, 17 Feb 2024         & \\textbf{  AIC                } &   13.664    \\\\\n\\textbf{Time:}                   &             06:44:01             & \\textbf{  BIC                } &   21.612    \\\\\n\\textbf{Sample:}                 &            10-31-2020            & \\textbf{  HQIC               } &   15.663    \\\\\n\\textbf{}                        &           - 09-30-2023           & \\textbf{                     } &             \\\\\n\\textbf{Covariance Type:}        &               opg                & \\textbf{                     } &             \\\\\n\\bottomrule\n\\end{tabular}\n\\begin{tabular}{lcccccc}\n                  & \\textbf{coef} & \\textbf{std err} & \\textbf{z} & \\textbf{P$> |$z$|$} & \\textbf{[0.025} & \\textbf{0.975]}  \\\\\n\\midrule\n\\textbf{ar.L1}    &      -1.0966  &      174.505     &    -0.006  &         0.995        &     -343.119    &      340.926     \\\\\n\\textbf{ar.L2}    &      -0.0968  &       17.037     &    -0.006  &         0.995        &      -33.488    &       33.295     \\\\\n\\textbf{ma.L1}    &      -0.2542  &     2038.124     &    -0.000  &         1.000        &    -3994.904    &     3994.395     \\\\\n\\textbf{ma.L2}    &      -0.7457  &     1519.857     &    -0.000  &         1.000        &    -2979.611    &     2978.119     \\\\\n\\textbf{ar.S.L12} &      -0.6443  &        0.742     &    -0.869  &         0.385        &       -2.098    &        0.809     \\\\\n\\textbf{ma.S.L12} &      -0.9986  &     2743.759     &    -0.000  &         1.000        &    -5378.668    &     5376.671     \\\\\n\\textbf{sigma2}   &       0.0156  &       51.605     &     0.000  &         1.000        &     -101.129    &      101.160     \\\\\n\\bottomrule\n\\end{tabular}\n\\begin{tabular}{lclc}\n\\textbf{Ljung-Box (L1) (Q):}     & 0.04 & \\textbf{  Jarque-Bera (JB):  } &  0.40  \\\\\n\\textbf{Prob(Q):}                & 0.84 & \\textbf{  Prob(JB):          } &  0.82  \\\\\n\\textbf{Heteroskedasticity (H):} & 0.92 & \\textbf{  Skew:              } & -0.22  \\\\\n\\textbf{Prob(H) (two-sided):}    & 0.90 & \\textbf{  Kurtosis:          } &  2.53  \\\\\n\\bottomrule\n\\end{tabular}\n%\\caption{SARIMAX Results}\n\\end{center}\n\nWarnings: \\newline\n [1] Covariance matrix calculated using the outer product of gradients (complex-step)."
          },
          "metadata": {},
          "execution_count": 38
        }
      ],
      "source": [
        "arima_model = SARIMAX(train_data['Prices'], order = (2,1,2), seasonal_order = (1,1,1,12))\n",
        "arima_result = arima_model.fit()\n",
        "arima_result.summary()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sY9AGhluxgM8",
        "outputId": "d6367116-dbee-4440-c490-f3a60eed2e60"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/statsmodels/tsa/statespace/representation.py:374: FutureWarning: Unknown keyword arguments: dict_keys(['typ']).Passing unknown keyword arguments will raise a TypeError beginning in version 0.15.\n",
            "  warnings.warn(msg, FutureWarning)\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2023-10-31    11.123726\n",
              "2023-11-30    12.293481\n",
              "2023-12-31    12.406998\n",
              "2024-01-31    12.668372\n",
              "2024-02-29    12.660525\n",
              "2024-03-31    12.666545\n",
              "2024-04-30    11.678595\n",
              "2024-05-31    11.728106\n",
              "2024-06-30    11.412530\n",
              "2024-07-31    11.637279\n",
              "2024-08-31    11.530545\n",
              "2024-09-30    11.863028\n",
              "Freq: M, Name: ARIMA Predictions, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 39
        }
      ],
      "source": [
        "arima_pred = arima_result.predict(start = len(train_data), end = len(Nat_gas_df)-1, typ=\"levels\").rename(\"ARIMA Predictions\")\n",
        "arima_pred"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 453
        },
        "id": "_mUZV9QVxgNG",
        "outputId": "a380a8a5-4812-489f-cad3-7eb94cd3c6d6"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1600x500 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "test_data['Prices'].plot(figsize = (16,5), legend=True)\n",
        "arima_pred.plot(legend = True);"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 729
        },
        "id": "QuBuiB3rxgNG",
        "outputId": "254800b4-a937-4a6c-a279-aadaff4c2f30"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: xlabel='Dates'>"
            ]
          },
          "metadata": {},
          "execution_count": 41
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x800 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "Nat_gas_df['forecast']=arima_result.predict(start=35,end=47,dynamic=True)\n",
        "Nat_gas_df[['Prices','forecast']].plot(figsize=(12,8))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 457
        },
        "id": "WN9cvs8GxgNG",
        "outputId": "ab7729bc-bdb7-426e-9a8d-9e263040b0dc"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "            Prices   forecast\n",
              "Dates                        \n",
              "2023-10-31    11.8  11.147138\n",
              "2023-11-30    12.2  12.254501\n",
              "2023-12-31    12.8  12.421252\n",
              "2024-01-31    12.6  12.632132\n",
              "2024-02-29    12.4  12.672660\n",
              "2024-03-31    12.7  12.632363\n",
              "2024-04-30    12.1  11.688678\n",
              "2024-05-31    11.4  11.695976\n",
              "2024-06-30    11.5  11.420561\n",
              "2024-07-31    11.6  11.607203\n",
              "2024-08-31    11.5  11.536494\n",
              "2024-09-30    11.8  11.860515"
            ],
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              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-dcbda2d3-38ed-41d8-8568-6036182a1a4f button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-dcbda2d3-38ed-41d8-8568-6036182a1a4f');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-ed62ebb6-0b49-47f7-ade3-f44e90a46fdb\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-ed62ebb6-0b49-47f7-ade3-f44e90a46fdb')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-ed62ebb6-0b49-47f7-ade3-f44e90a46fdb button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"Nat_gas_df\",\n  \"rows\": 12,\n  \"fields\": [\n    {\n      \"column\": \"Prices\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.5033222956847165,\n        \"min\": 11.4,\n        \"max\": 12.8,\n        \"samples\": [\n          11.5,\n          12.2,\n          12.7\n        ],\n        \"num_unique_values\": 10,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"forecast\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.5325564467385636,\n        \"min\": 11.147137828326686,\n        \"max\": 12.672659693144132,\n        \"samples\": [\n          11.536494457482076,\n          11.607203496126726,\n          11.147137828326686\n        ],\n        \"num_unique_values\": 12,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 42
        }
      ],
      "source": [
        "Nat_gas_df.tail(12)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "selW6MH5xgNH",
        "outputId": "194ff325-4cea-43fa-f4e5-7b30c622dbed"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-43-c6fe8541f169>:1: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  test_data['ARIMA_Predictions'] = arima_pred\n"
          ]
        }
      ],
      "source": [
        "test_data['ARIMA_Predictions'] = arima_pred\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 457
        },
        "id": "UOUUfhpAxgNH",
        "outputId": "ed1d23d3-e162-4068-a4bf-a4e8a614a7e0"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "            Prices  ARIMA_Predictions\n",
              "Dates                                \n",
              "2023-10-31    11.8          11.123726\n",
              "2023-11-30    12.2          12.293481\n",
              "2023-12-31    12.8          12.406998\n",
              "2024-01-31    12.6          12.668372\n",
              "2024-02-29    12.4          12.660525\n",
              "2024-03-31    12.7          12.666545\n",
              "2024-04-30    12.1          11.678595\n",
              "2024-05-31    11.4          11.728106\n",
              "2024-06-30    11.5          11.412530\n",
              "2024-07-31    11.6          11.637279\n",
              "2024-08-31    11.5          11.530545\n",
              "2024-09-30    11.8          11.863028"
            ],
            "text/html": [
              "\n",
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              "    <div>\n",
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              "  <thead>\n",
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              "      <th>Prices</th>\n",
              "      <th>ARIMA_Predictions</th>\n",
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              "      <td>12.293481</td>\n",
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              "    <tr>\n",
              "      <th>2024-05-31</th>\n",
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              "      <td>11.728106</td>\n",
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              "    <tr>\n",
              "      <th>2024-06-30</th>\n",
              "      <td>11.5</td>\n",
              "      <td>11.412530</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2024-07-31</th>\n",
              "      <td>11.6</td>\n",
              "      <td>11.637279</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2024-08-31</th>\n",
              "      <td>11.5</td>\n",
              "      <td>11.530545</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2024-09-30</th>\n",
              "      <td>11.8</td>\n",
              "      <td>11.863028</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-403454d4-ce46-4efb-8c0d-e6bcd0d4de8a')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
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              "  </svg>\n",
              "    </button>\n",
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              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
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              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
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              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-403454d4-ce46-4efb-8c0d-e6bcd0d4de8a button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-403454d4-ce46-4efb-8c0d-e6bcd0d4de8a');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
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              "\n",
              "\n",
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              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
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              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
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              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-94e469f5-acfe-43d7-aa7f-728bfeb0a28c button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "test_data",
              "summary": "{\n  \"name\": \"test_data\",\n  \"rows\": 12,\n  \"fields\": [\n    {\n      \"column\": \"Prices\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.5033222956847165,\n        \"min\": 11.4,\n        \"max\": 12.8,\n        \"samples\": [\n          11.5,\n          12.2,\n          12.7\n        ],\n        \"num_unique_values\": 10,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"ARIMA_Predictions\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.5419580759193532,\n        \"min\": 11.123726373863558,\n        \"max\": 12.668371716964872,\n        \"samples\": [\n          11.530545208350398,\n          11.637279465503125,\n          11.123726373863558\n        ],\n        \"num_unique_values\": 12,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 44
        }
      ],
      "source": [
        "test_data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "hb8WKbEJxgNH"
      },
      "outputs": [],
      "source": [
        "from sklearn.metrics import mean_squared_error\n",
        "from statsmodels.tools.eval_measures import rmse"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "L2dOMZy9xgNH",
        "outputId": "ea9cbf38-09c8-49c0-a61e-9515bb58a4c5"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "MSE Error: 0.08278202717733533\n",
            "RMSE Error: 0.28771865976563865\n",
            "Mean: 11.207083333333332\n"
          ]
        }
      ],
      "source": [
        "arima_rmse_error = rmse(test_data['Prices'], arima_pred)\n",
        "arima_mse_error = arima_rmse_error**2\n",
        "mean_value = Nat_gas_df['Prices'].mean()\n",
        "\n",
        "print(f'MSE Error: {arima_mse_error}\\nRMSE Error: {arima_rmse_error}\\nMean: {mean_value}')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0H31Cm8pxgNH"
      },
      "source": [
        "# Run entire code after entering date"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tmqOliYCxgNH",
        "outputId": "1f98179d-7f49-40f4-a538-16cd5027dc2f"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/statsmodels/tsa/statespace/representation.py:374: FutureWarning: Unknown keyword arguments: dict_keys(['typ']).Passing unknown keyword arguments will raise a TypeError beginning in version 0.15.\n",
            "  warnings.warn(msg, FutureWarning)\n"
          ]
        }
      ],
      "source": [
        "arima_pred_full = arima_result.predict(start = 0, end = len(Nat_gas_df)-1, typ=\"levels\").rename(\"ARIMA Predictions\")\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "c1Z2FP5cxgNI",
        "outputId": "e01c0e1d-f68a-4b9b-b2b9-bcfb18e5906b"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "11.728106042808744"
            ]
          },
          "metadata": {},
          "execution_count": 48
        }
      ],
      "source": [
        "#enter Month and year in following format 'yyyy/mm/dd' -- KEEP day at end of the month AND THEN Run the ENTIRE code\n",
        "#example : userdate = 2024-05-31\n",
        "userdate = '2024-05-31'\n",
        "arima_pred_full.loc[userdate]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "G_u4leCaxgNI",
        "outputId": "4ede8696-3162-466d-e8c8-03e58fa27878"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "11.4"
            ]
          },
          "metadata": {},
          "execution_count": 49
        }
      ],
      "source": [
        "# Actual Price\n",
        "Nat_gas_df.loc[userdate]['Prices']"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gp7IAndjxgNI"
      },
      "source": [
        "# Extrapolate 1 year in the future"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "DYJ_wYlLxgNI"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Rtgz-jyVxgNI"
      },
      "outputs": [],
      "source": [
        "#enter Month and year in following format 'yyyy/mm/dd' -- KEEP day at end of the month AND THEN Run the ENTIRE code\n",
        "#example : userdate = 2024-05-31\n",
        "userdate = '2020-12-31'\n",
        "start = Nat_gas_df.index.get_loc(userdate)\n",
        "arima_pred_future = arima_result.predict(start = start, end = start+12, typ=\"levels\").rename(\"ARIMA Predictions\")\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QSuSeezsxgNJ",
        "outputId": "c4181421-381e-4a92-bfb8-4e454f4e733f"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2021-12-31    11.271263\n",
              "Freq: M, Name: ARIMA Predictions, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 51
        }
      ],
      "source": [
        "arima_pred_future.tail(1)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KpwPUVPexgNJ",
        "outputId": "147d4a99-80da-4522-cc5f-84420ef12b7d"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Prices      11.4\n",
              "forecast     NaN\n",
              "Name: 2021-12-31 00:00:00, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 52
        }
      ],
      "source": [
        "# Actual Price in next 12 months\n",
        "Nat_gas_df.iloc[start+12]\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Nc7VjMXSxgNJ"
      },
      "source": [
        "# Forecasting using Prophet"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "tEvduGO3xgNJ"
      },
      "outputs": [],
      "source": [
        "from prophet import Prophet\n",
        "from prophet.plot import plot_plotly, plot_components_plotly\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "ooLltjZBxgNJ",
        "outputId": "7869e7c1-683c-4ef9-ff5f-a529ae1eb150"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      Dates  Prices\n",
              "0  10/31/20    10.1\n",
              "1  11/30/20    10.3\n",
              "2  12/31/20    11.0\n",
              "3   1/31/21    10.9\n",
              "4   2/28/21    10.9"
            ],
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              "\n",
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              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "</style>\n",
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              "      <th>Prices</th>\n",
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              "      <td>10.3</td>\n",
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              "      <td>1/31/21</td>\n",
              "      <td>10.9</td>\n",
              "    </tr>\n",
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              "      <th>4</th>\n",
              "      <td>2/28/21</td>\n",
              "      <td>10.9</td>\n",
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              "    [theme=dark] .colab-df-convert:hover {\n",
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              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-97febe83-48ea-481f-bd1e-11aa84602118 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-97febe83-48ea-481f-bd1e-11aa84602118');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
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              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
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              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
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              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
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              "</div>\n",
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 48,\n  \"fields\": [\n    {\n      \"column\": \"Dates\",\n      \"properties\": {\n        \"dtype\": \"object\",\n        \"min\": \"1/31/21\",\n        \"max\": \"9/30/24\",\n        \"samples\": [\n          \"1/31/23\",\n          \"2/29/24\",\n          \"12/31/22\"\n        ],\n        \"num_unique_values\": 48,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Prices\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.7578973105463123,\n        \"min\": 9.84,\n        \"max\": 12.8,\n        \"samples\": [\n          11.2,\n          12.1,\n          10.1\n        ],\n        \"num_unique_values\": 25,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 54
        }
      ],
      "source": [
        "#read in the data\n",
        "df = pd.read_csv('Nat_Gas.csv')\n",
        "df.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "psY_-mhtxgNJ",
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            "text/plain": [
              "         ds     y\n",
              "0  10/31/20  10.1\n",
              "1  11/30/20  10.3\n",
              "2  12/31/20  11.0\n",
              "3   1/31/21  10.9\n",
              "4   2/28/21  10.9"
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              "      spin 1s steps(1) infinite;\n",
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              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 48,\n  \"fields\": [\n    {\n      \"column\": \"ds\",\n      \"properties\": {\n        \"dtype\": \"object\",\n        \"min\": \"1/31/21\",\n        \"max\": \"9/30/24\",\n        \"samples\": [\n          \"1/31/23\",\n          \"2/29/24\",\n          \"12/31/22\"\n        ],\n        \"num_unique_values\": 48,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"y\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.7578973105463123,\n        \"min\": 9.84,\n        \"max\": 12.8,\n        \"samples\": [\n          11.2,\n          12.1,\n          10.1\n        ],\n        \"num_unique_values\": 25,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 55
        }
      ],
      "source": [
        "#Select the date and the price\n",
        "df = df[['Dates', 'Prices']]\n",
        "#Rename the features:\n",
        "df = df.rename(columns={'Dates':'ds', 'Prices':'y'})\n",
        "df.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 426
        },
        "id": "S7jI5_o6xgNK",
        "outputId": "c4f55090-3af4-4bcf-bf4b-501025a2854d"
      },
      "outputs": [
        {
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          "data": {
            "text/plain": [
              "          ds     y\n",
              "36  10/31/23  11.8\n",
              "37  11/30/23  12.2\n",
              "38  12/31/23  12.8\n",
              "39   1/31/24  12.6\n",
              "40   2/29/24  12.4\n",
              "41   3/31/24  12.7\n",
              "42   4/30/24  12.1\n",
              "43   5/31/24  11.4\n",
              "44   6/30/24  11.5\n",
              "45   7/31/24  11.6\n",
              "46   8/31/24  11.5\n",
              "47   9/30/24  11.8"
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              "        const element = document.querySelector('#df-8f612352-ee67-44dd-8174-609a0dfe1ce5');\n",
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              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
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              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-ee4b975d-206e-4828-9b7c-69aef9f490aa button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "last",
              "repr_error": "0"
            }
          },
          "metadata": {},
          "execution_count": 56
        }
      ],
      "source": [
        "# Get last 12 rows of data and store them into a new variable\n",
        "last = df[len(df)-12:]\n",
        "last"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "n9kqop36xgNK",
        "outputId": "3f60af1d-8d2a-4dab-aefc-2503e3f0168a"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "         ds     y\n",
              "0  10/31/20  10.1\n",
              "1  11/30/20  10.3\n",
              "2  12/31/20  11.0\n",
              "3   1/31/21  10.9\n",
              "4   2/28/21  10.9"
            ],
            "text/html": [
              "\n",
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              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>10/31/20</td>\n",
              "      <td>10.1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>11/30/20</td>\n",
              "      <td>10.3</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>12/31/20</td>\n",
              "      <td>11.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1/31/21</td>\n",
              "      <td>10.9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2/28/21</td>\n",
              "      <td>10.9</td>\n",
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              "  </tbody>\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
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              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-82e370a9-c5c4-4987-a839-7c08e05c4d1d button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-82e370a9-c5c4-4987-a839-7c08e05c4d1d');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
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              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
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              "      --hover-bg-color: #E2EBFA;\n",
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              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
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              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
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              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
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              "\n",
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              "    padding: 0;\n",
              "    width: 32px;\n",
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              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
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              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
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              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-11b1e4a6-6285-44ce-b831-581f06b88f24 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 36,\n  \"fields\": [\n    {\n      \"column\": \"ds\",\n      \"properties\": {\n        \"dtype\": \"object\",\n        \"min\": \"1/31/21\",\n        \"max\": \"9/30/23\",\n        \"samples\": [\n          \"9/30/23\",\n          \"11/30/21\",\n          \"12/31/22\"\n        ],\n        \"num_unique_values\": 36,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"y\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.6161516510915612,\n        \"min\": 9.84,\n        \"max\": 12.1,\n        \"samples\": [\n          10.1,\n          11.7,\n          11.6\n        ],\n        \"num_unique_values\": 20,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 57
        }
      ],
      "source": [
        "#Get all rows except the last 12\n",
        "df = df[:-12]\n",
        "df.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000,
          "referenced_widgets": [
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        "id": "nkQY_E5NxgNK",
        "outputId": "b6b17664-18b7-482b-dae2-387d21c113d4"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO:prophet:Disabling weekly seasonality. Run prophet with weekly_seasonality=True to override this.\n",
            "INFO:prophet:Disabling daily seasonality. Run prophet with daily_seasonality=True to override this.\n",
            "DEBUG:cmdstanpy:cmd: /usr/local/lib/python3.10/dist-packages/prophet/stan_model/prophet_model.bin info\n",
            "cwd: None\n",
            "DEBUG:cmdstanpy:input tempfile: /tmp/tmpr1h5uixy/q10alzh2.json\n",
            "DEBUG:cmdstanpy:input tempfile: /tmp/tmpr1h5uixy/epxnohv9.json\n",
            "06:45:54 - cmdstanpy - INFO - CmdStan start processing\n",
            "INFO:cmdstanpy:CmdStan start processing\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "chain 1 |          | 00:00 Status"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "2a80428d924541ae9d5d5287459ebd4d"
            }
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          "metadata": {}
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              "chain 2 |          | 00:00 Status"
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              "chain 3 |          | 00:00 Status"
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              "chain 4 |          | 00:00 Status"
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        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "DEBUG:cmdstanpy:idx 0\n",
            "DEBUG:cmdstanpy:running CmdStan, num_threads: 1\n",
            "DEBUG:cmdstanpy:CmdStan args: ['/usr/local/lib/python3.10/dist-packages/prophet/stan_model/prophet_model.bin', 'id=1', 'random', 'seed=73221', 'data', 'file=/tmp/tmpr1h5uixy/q10alzh2.json', 'init=/tmp/tmpr1h5uixy/epxnohv9.json', 'output', 'file=/tmp/tmpr1h5uixy/prophet_model6cyklx5w/prophet_model-20240217064555_1.csv', 'method=sample', 'num_samples=180', 'num_warmup=180', 'algorithm=hmc', 'adapt', 'engaged=1']\n",
            "DEBUG:cmdstanpy:idx 1\n",
            "DEBUG:cmdstanpy:running CmdStan, num_threads: 1\n",
            "DEBUG:cmdstanpy:CmdStan args: ['/usr/local/lib/python3.10/dist-packages/prophet/stan_model/prophet_model.bin', 'id=2', 'random', 'seed=73221', 'data', 'file=/tmp/tmpr1h5uixy/q10alzh2.json', 'init=/tmp/tmpr1h5uixy/epxnohv9.json', 'output', 'file=/tmp/tmpr1h5uixy/prophet_model6cyklx5w/prophet_model-20240217064555_2.csv', 'method=sample', 'num_samples=180', 'num_warmup=180', 'algorithm=hmc', 'adapt', 'engaged=1']\n",
            "DEBUG:cmdstanpy:idx 2\n",
            "DEBUG:cmdstanpy:running CmdStan, num_threads: 1\n",
            "DEBUG:cmdstanpy:CmdStan args: ['/usr/local/lib/python3.10/dist-packages/prophet/stan_model/prophet_model.bin', 'id=3', 'random', 'seed=73221', 'data', 'file=/tmp/tmpr1h5uixy/q10alzh2.json', 'init=/tmp/tmpr1h5uixy/epxnohv9.json', 'output', 'file=/tmp/tmpr1h5uixy/prophet_model6cyklx5w/prophet_model-20240217064555_3.csv', 'method=sample', 'num_samples=180', 'num_warmup=180', 'algorithm=hmc', 'adapt', 'engaged=1']\n",
            "DEBUG:cmdstanpy:idx 3\n",
            "DEBUG:cmdstanpy:running CmdStan, num_threads: 1\n",
            "DEBUG:cmdstanpy:CmdStan args: ['/usr/local/lib/python3.10/dist-packages/prophet/stan_model/prophet_model.bin', 'id=4', 'random', 'seed=73221', 'data', 'file=/tmp/tmpr1h5uixy/q10alzh2.json', 'init=/tmp/tmpr1h5uixy/epxnohv9.json', 'output', 'file=/tmp/tmpr1h5uixy/prophet_model6cyklx5w/prophet_model-20240217064555_4.csv', 'method=sample', 'num_samples=180', 'num_warmup=180', 'algorithm=hmc', 'adapt', 'engaged=1']\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                                                                                                                                                                                                                                                                                                                                                                                                                "
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "06:46:02 - cmdstanpy - INFO - CmdStan done processing.\n",
            "INFO:cmdstanpy:CmdStan done processing.\n",
            "DEBUG:cmdstanpy:runset\n",
            "RunSet: chains=4, chain_ids=[1, 2, 3, 4], num_processes=4\n",
            " cmd (chain 1):\n",
            "\t['/usr/local/lib/python3.10/dist-packages/prophet/stan_model/prophet_model.bin', 'id=1', 'random', 'seed=73221', 'data', 'file=/tmp/tmpr1h5uixy/q10alzh2.json', 'init=/tmp/tmpr1h5uixy/epxnohv9.json', 'output', 'file=/tmp/tmpr1h5uixy/prophet_model6cyklx5w/prophet_model-20240217064555_1.csv', 'method=sample', 'num_samples=180', 'num_warmup=180', 'algorithm=hmc', 'adapt', 'engaged=1']\n",
            " retcodes=[0, 0, 0, 0]\n",
            " per-chain output files (showing chain 1 only):\n",
            " csv_file:\n",
            "\t/tmp/tmpr1h5uixy/prophet_model6cyklx5w/prophet_model-20240217064555_1.csv\n",
            " console_msgs (if any):\n",
            "\t/tmp/tmpr1h5uixy/prophet_model6cyklx5w/prophet_model-20240217064555_0-stdout.txt\n",
            "DEBUG:cmdstanpy:Chain 1 console:\n",
            "method = sample (Default)\n",
            "  sample\n",
            "    num_samples = 180\n",
            "    num_warmup = 180\n",
            "    save_warmup = 0 (Default)\n",
            "    thin = 1 (Default)\n",
            "    adapt\n",
            "      engaged = 1 (Default)\n",
            "      gamma = 0.050000000000000003 (Default)\n",
            "      delta = 0.80000000000000004 (Default)\n",
            "      kappa = 0.75 (Default)\n",
            "      t0 = 10 (Default)\n",
            "      init_buffer = 75 (Default)\n",
            "      term_buffer = 50 (Default)\n",
            "      window = 25 (Default)\n",
            "    algorithm = hmc (Default)\n",
            "      hmc\n",
            "        engine = nuts (Default)\n",
            "          nuts\n",
            "            max_depth = 10 (Default)\n",
            "        metric = diag_e (Default)\n",
            "        metric_file =  (Default)\n",
            "        stepsize = 1 (Default)\n",
            "        stepsize_jitter = 0 (Default)\n",
            "    num_chains = 1 (Default)\n",
            "id = 1 (Default)\n",
            "data\n",
            "  file = /tmp/tmpr1h5uixy/q10alzh2.json\n",
            "init = /tmp/tmpr1h5uixy/epxnohv9.json\n",
            "random\n",
            "  seed = 73221\n",
            "output\n",
            "  file = /tmp/tmpr1h5uixy/prophet_model6cyklx5w/prophet_model-20240217064555_1.csv\n",
            "  diagnostic_file =  (Default)\n",
            "  refresh = 100 (Default)\n",
            "  sig_figs = -1 (Default)\n",
            "  profile_file = profile.csv (Default)\n",
            "num_threads = 1 (Default)\n",
            "\n",
            "\n",
            "Gradient evaluation took 6e-05 seconds\n",
            "1000 transitions using 10 leapfrog steps per transition would take 0.6 seconds.\n",
            "Adjust your expectations accordingly!\n",
            "\n",
            "\n",
            "Iteration:   1 / 360 [  0%]  (Warmup)\n",
            "Informational Message: The current Metropolis proposal is about to be rejected because of the following issue:\n",
            "Exception: normal_id_glm_lpdf: Matrix of independent variables is inf, but must be finite! (in 'prophet.stan', line 137, column 2 to line 142, column 4)\n",
            "If this warning occurs sporadically, such as for highly constrained variable types like covariance matrices, then the sampler is fine,\n",
            "but if this warning occurs often then your model may be either severely ill-conditioned or misspecified.\n",
            "\n",
            "Iteration: 100 / 360 [ 27%]  (Warmup)\n",
            "Iteration: 181 / 360 [ 50%]  (Sampling)\n",
            "Iteration: 280 / 360 [ 77%]  (Sampling)\n",
            "Iteration: 360 / 360 [100%]  (Sampling)\n",
            "\n",
            " Elapsed Time: 2.888 seconds (Warm-up)\n",
            "               1.561 seconds (Sampling)\n",
            "               4.449 seconds (Total)\n",
            "\n",
            "\n",
            "06:46:02 - cmdstanpy - WARNING - Non-fatal error during sampling:\n",
            "Exception: normal_id_glm_lpdf: Matrix of independent variables is inf, but must be finite! (in 'prophet.stan', line 137, column 2 to line 142, column 4)\n",
            "Exception: normal_id_glm_lpdf: Scale vector is 0, but must be positive finite! (in 'prophet.stan', line 137, column 2 to line 142, column 4)\n",
            "Consider re-running with show_console=True if the above output is unclear!\n",
            "WARNING:cmdstanpy:Non-fatal error during sampling:\n",
            "Exception: normal_id_glm_lpdf: Matrix of independent variables is inf, but must be finite! (in 'prophet.stan', line 137, column 2 to line 142, column 4)\n",
            "Exception: normal_id_glm_lpdf: Scale vector is 0, but must be positive finite! (in 'prophet.stan', line 137, column 2 to line 142, column 4)\n",
            "Consider re-running with show_console=True if the above output is unclear!\n",
            "06:46:02 - cmdstanpy - WARNING - Some chains may have failed to converge.\n",
            "\tChain 1 had 1 iterations at max treedepth (0.6%)\n",
            "\tUse the \"diagnose()\" method on the CmdStanMCMC object to see further information.\n",
            "WARNING:cmdstanpy:Some chains may have failed to converge.\n",
            "\tChain 1 had 1 iterations at max treedepth (0.6%)\n",
            "\tUse the \"diagnose()\" method on the CmdStanMCMC object to see further information.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n"
          ]
        }
      ],
      "source": [
        "#Creating Prophet Object (Model)\n",
        "fbp = Prophet(seasonality_mode='multiplicative', mcmc_samples=360)\n",
        "\n",
        "#Train the model\n",
        "fbp.fit(df)\n",
        "future = fbp.make_future_dataframe(periods=24, freq='M')\n",
        "forecast = fbp.predict(future)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 617
        },
        "id": "3oWcxIHmxgNK",
        "outputId": "bf860f24-af66-4abd-983b-6ccf96ed3891"
      },
      "outputs": [
        {
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          },
          "metadata": {}
        }
      ],
      "source": [
        "#plot the data\n",
        "plot_plotly(fbp, forecast)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "scrolled": false,
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 800
        },
        "id": "mfmbs7fxxgNK",
        "outputId": "c7c8d2ff-1212-4d95-a5fc-5d80331fedf3"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "           ds      trend  yhat_lower  yhat_upper  trend_lower  trend_upper  \\\n",
              "45 2024-07-31  12.535389   11.106886   12.677680    11.911101    13.156339   \n",
              "46 2024-08-31  12.604677   11.551920   13.285610    11.924832    13.277577   \n",
              "47 2024-09-30  12.671730   11.183489   13.061251    11.946135    13.402175   \n",
              "48 2024-10-31  12.741018   11.546695   13.243215    11.950093    13.549812   \n",
              "49 2024-11-30  12.808072   11.959019   13.974420    11.956355    13.673305   \n",
              "50 2024-12-31  12.877360   12.641758   14.864465    11.961902    13.840373   \n",
              "51 2025-01-31  12.946648   12.454308   14.858852    11.961397    13.998281   \n",
              "52 2025-02-28  13.009231   12.612476   15.134324    11.948933    14.158263   \n",
              "53 2025-03-31  13.078519   12.456253   15.079598    11.941862    14.319708   \n",
              "54 2025-04-30  13.145572   11.669650   14.370990    11.948540    14.480077   \n",
              "55 2025-05-31  13.214861   11.164756   13.978455    11.927318    14.650982   \n",
              "56 2025-06-30  13.281914   11.267627   14.108612    11.881808    14.803011   \n",
              "57 2025-07-31  13.351202   11.256861   14.379803    11.859028    14.966353   \n",
              "58 2025-08-31  13.420490   11.360041   14.676595    11.817913    15.152206   \n",
              "59 2025-09-30  13.487544   11.277743   14.768824    11.772560    15.332311   \n",
              "\n",
              "    multiplicative_terms  multiplicative_terms_lower  \\\n",
              "45             -0.051894                   -0.089096   \n",
              "46             -0.016840                   -0.055053   \n",
              "47             -0.044423                   -0.081511   \n",
              "48             -0.028530                   -0.052552   \n",
              "49              0.009519                   -0.012521   \n",
              "50              0.068768                    0.044218   \n",
              "51              0.053385                    0.025019   \n",
              "52              0.063516                    0.044485   \n",
              "53              0.048576                    0.024300   \n",
              "54             -0.011876                   -0.034048   \n",
              "55             -0.051649                   -0.077176   \n",
              "56             -0.047645                   -0.070717   \n",
              "57             -0.045815                   -0.070736   \n",
              "58             -0.033248                   -0.057711   \n",
              "59             -0.039274                   -0.062664   \n",
              "\n",
              "    multiplicative_terms_upper    yearly  yearly_lower  yearly_upper  \\\n",
              "45                   -0.013346 -0.051894     -0.089096     -0.013346   \n",
              "46                    0.021259 -0.016840     -0.055053      0.021259   \n",
              "47                   -0.006674 -0.044423     -0.081511     -0.006674   \n",
              "48                   -0.004487 -0.028530     -0.052552     -0.004487   \n",
              "49                    0.032040  0.009519     -0.012521      0.032040   \n",
              "50                    0.096275  0.068768      0.044218      0.096275   \n",
              "51                    0.083299  0.053385      0.025019      0.083299   \n",
              "52                    0.083858  0.063516      0.044485      0.083858   \n",
              "53                    0.073283  0.048576      0.024300      0.073283   \n",
              "54                    0.010101 -0.011876     -0.034048      0.010101   \n",
              "55                   -0.025940 -0.051649     -0.077176     -0.025940   \n",
              "56                   -0.022762 -0.047645     -0.070717     -0.022762   \n",
              "57                   -0.022041 -0.045815     -0.070736     -0.022041   \n",
              "58                   -0.007639 -0.033248     -0.057711     -0.007639   \n",
              "59                   -0.016459 -0.039274     -0.062664     -0.016459   \n",
              "\n",
              "    additive_terms  additive_terms_lower  additive_terms_upper       yhat  \n",
              "45             0.0                   0.0                   0.0  11.884878  \n",
              "46             0.0                   0.0                   0.0  12.392417  \n",
              "47             0.0                   0.0                   0.0  12.108813  \n",
              "48             0.0                   0.0                   0.0  12.377521  \n",
              "49             0.0                   0.0                   0.0  12.929997  \n",
              "50             0.0                   0.0                   0.0  13.762911  \n",
              "51             0.0                   0.0                   0.0  13.637802  \n",
              "52             0.0                   0.0                   0.0  13.835524  \n",
              "53             0.0                   0.0                   0.0  13.713817  \n",
              "54             0.0                   0.0                   0.0  12.989453  \n",
              "55             0.0                   0.0                   0.0  12.532325  \n",
              "56             0.0                   0.0                   0.0  12.649091  \n",
              "57             0.0                   0.0                   0.0  12.739510  \n",
              "58             0.0                   0.0                   0.0  12.974280  \n",
              "59             0.0                   0.0                   0.0  12.957829  "
            ],
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              "  <tbody>\n",
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              "      <th>45</th>\n",
              "      <td>2024-07-31</td>\n",
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              "      <th>46</th>\n",
              "      <td>2024-08-31</td>\n",
              "      <td>12.604677</td>\n",
              "      <td>11.551920</td>\n",
              "      <td>13.285610</td>\n",
              "      <td>11.924832</td>\n",
              "      <td>13.277577</td>\n",
              "      <td>-0.016840</td>\n",
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              "      <td>-0.016840</td>\n",
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              "    <tr>\n",
              "      <th>47</th>\n",
              "      <td>2024-09-30</td>\n",
              "      <td>12.671730</td>\n",
              "      <td>11.183489</td>\n",
              "      <td>13.061251</td>\n",
              "      <td>11.946135</td>\n",
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              "      <td>0.0</td>\n",
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              "    <tr>\n",
              "      <th>48</th>\n",
              "      <td>2024-10-31</td>\n",
              "      <td>12.741018</td>\n",
              "      <td>11.546695</td>\n",
              "      <td>13.243215</td>\n",
              "      <td>11.950093</td>\n",
              "      <td>13.549812</td>\n",
              "      <td>-0.028530</td>\n",
              "      <td>-0.052552</td>\n",
              "      <td>-0.004487</td>\n",
              "      <td>-0.028530</td>\n",
              "      <td>-0.052552</td>\n",
              "      <td>-0.004487</td>\n",
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              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>12.377521</td>\n",
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              "    <tr>\n",
              "      <th>49</th>\n",
              "      <td>2024-11-30</td>\n",
              "      <td>12.808072</td>\n",
              "      <td>11.959019</td>\n",
              "      <td>13.974420</td>\n",
              "      <td>11.956355</td>\n",
              "      <td>13.673305</td>\n",
              "      <td>0.009519</td>\n",
              "      <td>-0.012521</td>\n",
              "      <td>0.032040</td>\n",
              "      <td>0.009519</td>\n",
              "      <td>-0.012521</td>\n",
              "      <td>0.032040</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>12.929997</td>\n",
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              "    <tr>\n",
              "      <th>50</th>\n",
              "      <td>2024-12-31</td>\n",
              "      <td>12.877360</td>\n",
              "      <td>12.641758</td>\n",
              "      <td>14.864465</td>\n",
              "      <td>11.961902</td>\n",
              "      <td>13.840373</td>\n",
              "      <td>0.068768</td>\n",
              "      <td>0.044218</td>\n",
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              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>13.762911</td>\n",
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              "    <tr>\n",
              "      <th>51</th>\n",
              "      <td>2025-01-31</td>\n",
              "      <td>12.946648</td>\n",
              "      <td>12.454308</td>\n",
              "      <td>14.858852</td>\n",
              "      <td>11.961397</td>\n",
              "      <td>13.998281</td>\n",
              "      <td>0.053385</td>\n",
              "      <td>0.025019</td>\n",
              "      <td>0.083299</td>\n",
              "      <td>0.053385</td>\n",
              "      <td>0.025019</td>\n",
              "      <td>0.083299</td>\n",
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              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
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              "    <tr>\n",
              "      <th>52</th>\n",
              "      <td>2025-02-28</td>\n",
              "      <td>13.009231</td>\n",
              "      <td>12.612476</td>\n",
              "      <td>15.134324</td>\n",
              "      <td>11.948933</td>\n",
              "      <td>14.158263</td>\n",
              "      <td>0.063516</td>\n",
              "      <td>0.044485</td>\n",
              "      <td>0.083858</td>\n",
              "      <td>0.063516</td>\n",
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              "      <td>0.083858</td>\n",
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              "      <td>0.0</td>\n",
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              "    <tr>\n",
              "      <th>53</th>\n",
              "      <td>2025-03-31</td>\n",
              "      <td>13.078519</td>\n",
              "      <td>12.456253</td>\n",
              "      <td>15.079598</td>\n",
              "      <td>11.941862</td>\n",
              "      <td>14.319708</td>\n",
              "      <td>0.048576</td>\n",
              "      <td>0.024300</td>\n",
              "      <td>0.073283</td>\n",
              "      <td>0.048576</td>\n",
              "      <td>0.024300</td>\n",
              "      <td>0.073283</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>13.713817</td>\n",
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              "    <tr>\n",
              "      <th>54</th>\n",
              "      <td>2025-04-30</td>\n",
              "      <td>13.145572</td>\n",
              "      <td>11.669650</td>\n",
              "      <td>14.370990</td>\n",
              "      <td>11.948540</td>\n",
              "      <td>14.480077</td>\n",
              "      <td>-0.011876</td>\n",
              "      <td>-0.034048</td>\n",
              "      <td>0.010101</td>\n",
              "      <td>-0.011876</td>\n",
              "      <td>-0.034048</td>\n",
              "      <td>0.010101</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>12.989453</td>\n",
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              "    <tr>\n",
              "      <th>55</th>\n",
              "      <td>2025-05-31</td>\n",
              "      <td>13.214861</td>\n",
              "      <td>11.164756</td>\n",
              "      <td>13.978455</td>\n",
              "      <td>11.927318</td>\n",
              "      <td>14.650982</td>\n",
              "      <td>-0.051649</td>\n",
              "      <td>-0.077176</td>\n",
              "      <td>-0.025940</td>\n",
              "      <td>-0.051649</td>\n",
              "      <td>-0.077176</td>\n",
              "      <td>-0.025940</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>12.532325</td>\n",
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              "    <tr>\n",
              "      <th>56</th>\n",
              "      <td>2025-06-30</td>\n",
              "      <td>13.281914</td>\n",
              "      <td>11.267627</td>\n",
              "      <td>14.108612</td>\n",
              "      <td>11.881808</td>\n",
              "      <td>14.803011</td>\n",
              "      <td>-0.047645</td>\n",
              "      <td>-0.070717</td>\n",
              "      <td>-0.022762</td>\n",
              "      <td>-0.047645</td>\n",
              "      <td>-0.070717</td>\n",
              "      <td>-0.022762</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>12.649091</td>\n",
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              "    <tr>\n",
              "      <th>57</th>\n",
              "      <td>2025-07-31</td>\n",
              "      <td>13.351202</td>\n",
              "      <td>11.256861</td>\n",
              "      <td>14.379803</td>\n",
              "      <td>11.859028</td>\n",
              "      <td>14.966353</td>\n",
              "      <td>-0.045815</td>\n",
              "      <td>-0.070736</td>\n",
              "      <td>-0.022041</td>\n",
              "      <td>-0.045815</td>\n",
              "      <td>-0.070736</td>\n",
              "      <td>-0.022041</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>12.739510</td>\n",
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              "    <tr>\n",
              "      <th>58</th>\n",
              "      <td>2025-08-31</td>\n",
              "      <td>13.420490</td>\n",
              "      <td>11.360041</td>\n",
              "      <td>14.676595</td>\n",
              "      <td>11.817913</td>\n",
              "      <td>15.152206</td>\n",
              "      <td>-0.033248</td>\n",
              "      <td>-0.057711</td>\n",
              "      <td>-0.007639</td>\n",
              "      <td>-0.033248</td>\n",
              "      <td>-0.057711</td>\n",
              "      <td>-0.007639</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>12.974280</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>59</th>\n",
              "      <td>2025-09-30</td>\n",
              "      <td>13.487544</td>\n",
              "      <td>11.277743</td>\n",
              "      <td>14.768824</td>\n",
              "      <td>11.772560</td>\n",
              "      <td>15.332311</td>\n",
              "      <td>-0.039274</td>\n",
              "      <td>-0.062664</td>\n",
              "      <td>-0.016459</td>\n",
              "      <td>-0.039274</td>\n",
              "      <td>-0.062664</td>\n",
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              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
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              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-81aa689c-3373-4b52-8ece-0f0ad11dd559 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
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              "type": "dataframe",
              "summary": "{\n  \"name\": \"forecast\",\n  \"rows\": 15,\n  \"fields\": [\n    {\n      \"column\": \"ds\",\n      \"properties\": {\n        \"dtype\": \"date\",\n        \"min\": \"2024-07-31 00:00:00\",\n        \"max\": \"2025-09-30 00:00:00\",\n        \"samples\": [\n          \"2025-04-30 00:00:00\",\n          \"2025-06-30 00:00:00\",\n          \"2024-07-31 00:00:00\"\n        ],\n        \"num_unique_values\": 15,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"trend\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.30362246209222066,\n        \"min\": 12.535388799420145,\n        \"max\": 13.487543553826708,\n        \"samples\": [\n          13.14557248006097,\n          13.281913888555808,\n          12.535388799420145\n        ],\n        \"num_unique_values\": 15,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"yhat_lower\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.5704697949820253,\n        \"min\": 11.106886301289565,\n        \"max\": 12.641757806230673,\n        \"samples\": [\n          11.669649782122459,\n          11.267627226968305,\n          11.106886301289565\n        ],\n        \"num_unique_values\": 15,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"yhat_upper\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.7832784211722059,\n        \"min\": 12.677679879551091,\n        \"max\": 15.13432395657716,\n        \"samples\": [\n          14.370989719083418,\n          14.108612062797134,\n          12.677679879551091\n        ],\n        \"num_unique_values\": 15,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"trend_lower\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.056983344519128246,\n        \"min\": 11.772559786367715,\n        \"max\": 11.961901799984924,\n        \"samples\": [\n          11.948539656641756,\n          11.881807618546848,\n          11.911100829260963\n        ],\n        \"num_unique_values\": 15,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"trend_upper\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.7010511745341771,\n        \"min\": 13.156339427481953,\n        \"max\": 15.332310732765906,\n        \"samples\": [\n          14.480077152793672,\n          14.803011203116146,\n          13.156339427481953\n        ],\n        \"num_unique_values\": 15,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"multiplicative_terms\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.045174694189281085,\n        \"min\": -0.051893931951200595,\n        \"max\": 0.06876806568726351,\n        \"samples\": [\n          -0.011876192803908796,\n          -0.04764547505516451,\n          -0.051893931951200595\n        ],\n        \"num_unique_values\": 15,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"multiplicative_terms_lower\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.04753247618300597,\n        \"min\": -0.08909561424171882,\n        \"max\": 0.04448488831873275,\n        \"samples\": [\n          -0.03404844622109913,\n          -0.07071663298469712,\n          -0.08909561424171882\n        ],\n        \"num_unique_values\": 15,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"multiplicative_terms_upper\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.04406493872309996,\n        \"min\": -0.025939621910027487,\n        \"max\": 0.09627504515756198,\n        \"samples\": [\n          0.010100763905184038,\n          -0.02276176286297526,\n          -0.013346378693060587\n        ],\n        \"num_unique_values\": 15,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"yearly\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.045174694189281085,\n        \"min\": -0.051893931951200595,\n        \"max\": 0.06876806568726351,\n        \"samples\": [\n          -0.011876192803908796,\n          -0.04764547505516451,\n          -0.051893931951200595\n        ],\n        \"num_unique_values\": 15,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"yearly_lower\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.04753247618300597,\n        \"min\": -0.08909561424171882,\n        \"max\": 0.04448488831873275,\n        \"samples\": [\n 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\"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"additive_terms_lower\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.0,\n        \"min\": 0.0,\n        \"max\": 0.0,\n        \"samples\": [\n          0.0\n        ],\n        \"num_unique_values\": 1,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"additive_terms_upper\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.0,\n        \"min\": 0.0,\n        \"max\": 0.0,\n        \"samples\": [\n          0.0\n        ],\n        \"num_unique_values\": 1,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"yhat\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.612675143648393,\n        \"min\": 11.884878186081194,\n        \"max\": 13.835523802900807,\n        \"samples\": [\n          12.989453126770009\n        ],\n        \"num_unique_values\": 15,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 60
        }
      ],
      "source": [
        "forecast.tail(15)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DwOcxDx9xgNK",
        "outputId": "7f4e0e9b-7880-45ef-8a97-0870c1c58a73"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "46    12.392417\n",
              "Name: yhat, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 61
        }
      ],
      "source": [
        "#Show the models prediction for 8/31/24\n",
        "forecast[forecast.ds == '8/31/24']['yhat']"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5wS1tENzxgNL",
        "outputId": "9b04e260-28f5-44de-8a9e-97a983967c54"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "46    11.5\n",
              "Name: y, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 62
        }
      ],
      "source": [
        "#Actual price for 8/31/24\n",
        "last[last.ds == '8/31/24']['y']"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kH55UOKzxgNL"
      },
      "source": [
        "# Run entire code after entering date"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6ez-lIwNxgNL",
        "outputId": "2586bce4-67b1-4df4-cff8-e59b5a5ecdab"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "39    13.102079\n",
              "Name: yhat, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 63
        }
      ],
      "source": [
        "future = fbp.make_future_dataframe(periods=120, freq='MS')\n",
        "forecast = fbp.predict(future)\n",
        "\n",
        "#enter Month and year in following format 'mm/01/yy' -- KEEP day @ '01' AND THEN Run the ENTIRE code\n",
        "#example : userdate = 01/01/24\n",
        "userdate = '01/01/24'\n",
        "forecast[forecast.ds == userdate]['yhat']"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "u02nMdQoxgNL",
        "outputId": "840b0ef1-0e28-4d8d-90f6-d49a16cf669c"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "39    12.6\n",
              "Name: y, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 64
        }
      ],
      "source": [
        "#Actual price for user date\n",
        "last[last.ds == '1/31/24']['y']"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0fVFmqhCxgNL",
        "outputId": "5d77d938-a222-4ba7-c6d4-750e1e457767"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([39])"
            ]
          },
          "metadata": {},
          "execution_count": 65
        }
      ],
      "source": [
        "forecast[forecast.ds == userdate].index.values"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oZ1r9sgixgNL",
        "outputId": "5f59884c-dd5f-4b62-9fda-e19a2872c0ae"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "51    14.649085\n",
              "Name: yhat, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 66
        }
      ],
      "source": [
        "# Forecast of Price next 12 months\n",
        "T = forecast[forecast.ds == userdate].index.values +12\n",
        "forecast.iloc[T]['yhat']"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "rbyEg8JAxgNM"
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