ARIMA/0_label_missing_processing.ipynb

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  {
   "cell_type": "markdown",
   "source": [
    "# Missing value processing for spot price"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "source": [
    "import pandas as pd\r\n",
    "import ast\r\n",
    "from datetime import datetime\r\n",
    "import numpy as np\r\n",
    "import matplotlib\r\n",
    "import matplotlib.pyplot as plt\r\n",
    "\r\n",
    "from statsmodels.tsa.arima.model import ARIMA"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "source": [
    "def GetSpotPrice(element_str):\r\n",
    "    element = ast.literal_eval(element_str)\r\n",
    "    timespans = element['TimeSpans']\r\n",
    "    date = [datetime.strptime(element['Date'][:10]+' '+x['TimeSpan'][:5], \\\r\n",
    "        '%Y-%m-%d %H:%M') for x in timespans if x['TimeSpan']!='dst']\r\n",
    "    value = [x['Value'] for x in timespans if x['TimeSpan']!='dst']\r\n",
    "    data_per_day = pd.DataFrame(data={'date':date, 'value': value})\r\n",
    "    return data_per_day\r\n",
    "\r\n",
    "def ConcatNewLabel():\r\n",
    "    old_data = pd.read_csv('Original_Data/spotprices_2011_2021.csv')\r\n",
    "    old_label = list(map(GetSpotPrice, old_data['Elements']))\r\n",
    "    old_label = pd.concat(old_label)\r\n",
    "\r\n",
    "    new_data = pd.read_csv('Original_Data/spotprices_new.csv')\r\n",
    "    new_data['date'] = [datetime.strptime(x, '%Y/%m/%d %H:%M') for x in new_data['date']]\r\n",
    "    new_label = new_data[['date', 'value']][24:]\r\n",
    "\r\n",
    "    label = pd.concat([old_label, new_label])\r\n",
    "    return label\r\n"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "source": [
    "label = ConcatNewLabel()\r\n",
    "label = label.set_index('date')\r\n",
    "# get continuous timespan\r\n",
    "label = label.asfreq(freq='h')\r\n",
    "label[label['value'].isna()].index"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "DatetimeIndex(['2011-03-27 02:00:00', '2012-03-25 02:00:00',\n",
       "               '2013-03-31 02:00:00', '2014-03-30 02:00:00',\n",
       "               '2015-03-29 02:00:00', '2016-03-27 02:00:00',\n",
       "               '2017-03-26 02:00:00', '2018-03-25 02:00:00',\n",
       "               '2019-03-31 02:00:00', '2020-03-29 02:00:00',\n",
       "               '2021-03-28 00:00:00', '2021-03-28 01:00:00',\n",
       "               '2021-03-28 02:00:00', '2021-03-28 03:00:00',\n",
       "               '2021-03-28 04:00:00', '2021-03-28 05:00:00',\n",
       "               '2021-03-28 06:00:00', '2021-03-28 07:00:00',\n",
       "               '2021-03-28 08:00:00', '2021-03-28 09:00:00',\n",
       "               '2021-03-28 10:00:00', '2021-03-28 11:00:00',\n",
       "               '2021-03-28 12:00:00', '2021-03-28 13:00:00',\n",
       "               '2021-03-28 14:00:00', '2021-03-28 15:00:00',\n",
       "               '2021-03-28 16:00:00', '2021-03-28 17:00:00',\n",
       "               '2021-03-28 18:00:00', '2021-03-28 19:00:00',\n",
       "               '2021-03-28 20:00:00', '2021-03-28 21:00:00',\n",
       "               '2021-03-28 22:00:00', '2021-03-28 23:00:00'],\n",
       "              dtype='datetime64[ns]', name='date', freq=None)"
      ]
     },
     "metadata": {},
     "execution_count": 18
    }
   ],
   "metadata": {}
  },
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   "cell_type": "markdown",
   "source": [
    "Here we can see from 2011 to 2020, there are missing values for 2:00 - 3:00 at the end of March each year, which may be the annual maintenance time. Then, assume that the missing values are likely to be similar with the previous values (one timestep before the missing value). Our strategy to deal with the missing values from 2011 to 2020 is using next observation to fill gap. But for year 2021, the values of the entire March 28th were missing, therefore, for that particular day, we need to fill the average spot price of the previous day or forecast the missing values based on historical data. "
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Filling missing time serie using ARIMA"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "source": [
    "# function for forecasting missing time series\r\n",
    "def FillTimeSereisMissing(data):\r\n",
    "    # get corresponding timeperiod\r\n",
    "    data = data.loc['2021-03-01 00:00:00':'2021-3-31 23:00:00']\r\n",
    "    # set train and test data\r\n",
    "    train = data.loc['2021-03-01 00:00:00':'2021-3-27 23:00:00']\r\n",
    "    test = data.loc['2021-03-28 00:00:00':'2021-3-28 23:00:00']\r\n",
    "    # fit model\r\n",
    "    model = ARIMA(train, order=(15,1,0))\r\n",
    "    model_fit = model.fit()\r\n",
    "    # forecast\r\n",
    "    forecast = model_fit.forecast(steps=len(test))\r\n",
    "    result = forecast\r\n",
    "\r\n",
    "    # get complete time series for visualization\r\n",
    "    forecast = pd.DataFrame({'date': test.index, 'value': forecast}).set_index('date')\r\n",
    "    pre_date = test.index[0] - test.index[0].freq\r\n",
    "    post_date = test.index[-1] + test.index[0].freq\r\n",
    "    pre_forecast = pd.DataFrame({'date': pre_date, 'value': data.loc[pre_date]}).set_index('date')\r\n",
    "    post_forecast = pd.DataFrame({'date': post_date, 'value': data.loc[post_date]}).set_index('date')\r\n",
    "    forecast = pd.concat([pre_forecast,forecast,post_forecast])\r\n",
    "\r\n",
    "    # plot\r\n",
    "    matplotlib.rcParams['figure.figsize'] = [12, 5]\r\n",
    "    fig, ax = plt.subplots(1, 1)\r\n",
    "    data.loc['2021-03-23 00:00:00':'2021-3-31 23:00:00'].plot(ax=ax, style='-', label='train', lw=2, color='blue')\r\n",
    "    forecast.plot(ax=ax, style='--', label='forecast', lw=2, color='limegreen')\r\n",
    "\r\n",
    "    plt.legend(['Spot Price'])\r\n",
    "    plt.title(\"Visualization of filling missing values\")\r\n",
    "    plt.xlabel('Date')\r\n",
    "    plt.ylabel('Price (€)')\r\n",
    "    plt.grid()\r\n",
    "    return result"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "source": [
    "# getting the predicted missing values and visualization\r\n",
    "missing_series = FillTimeSereisMissing(label)"
   ],
   "outputs": [
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"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "Above figure shows the result of filling the missing values. We used ARIMA model to forecast the spot price on March 28th based on the historical price in March 2021. With order (15,1,0) for ARIMA, which means we carried out the differenced 15th-order autoregressive model， we can obtain quite good forecast for the missing values. "
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "source": [
    "# filling missing values\r\n",
    "label.loc['2021-03-28 00:00:00':'2021-3-28 23:00:00'] = np.array(missing_series).reshape(24,1)"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "##  Filling single missing values using previous values"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "source": [
    "label[label['value'].isna()].index"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "DatetimeIndex(['2011-03-27 02:00:00', '2012-03-25 02:00:00',\n",
       "               '2013-03-31 02:00:00', '2014-03-30 02:00:00',\n",
       "               '2015-03-29 02:00:00', '2016-03-27 02:00:00',\n",
       "               '2017-03-26 02:00:00', '2018-03-25 02:00:00',\n",
       "               '2019-03-31 02:00:00', '2020-03-29 02:00:00'],\n",
       "              dtype='datetime64[ns]', name='date', freq=None)"
      ]
     },
     "metadata": {},
     "execution_count": 22
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "Now just single missing values are remained, which can be filled by the next valid observation."
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "source": [
    "# filling the gap using next valid observation\r\n",
    "label = label.fillna(method='bfill')"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "source": [
    "label.loc['2011-03-27 00:00:00':'2011-03-27 04:00:00']"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "                     value\n",
       "date                      \n",
       "2011-03-27 00:00:00  48.08\n",
       "2011-03-27 01:00:00  42.50\n",
       "2011-03-27 02:00:00  40.09\n",
       "2011-03-27 03:00:00  40.09\n",
       "2011-03-27 04:00:00  43.10"
      ],
      "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>value</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2011-03-27 00:00:00</th>\n",
       "      <td>48.08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-03-27 01:00:00</th>\n",
       "      <td>42.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-03-27 02:00:00</th>\n",
       "      <td>40.09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-03-27 03:00:00</th>\n",
       "      <td>40.09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-03-27 04:00:00</th>\n",
       "      <td>43.10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ]
     },
     "metadata": {},
     "execution_count": 24
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "source": [
    "# validation\r\n",
    "label.isnull().sum()"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "value    0\n",
       "dtype: int64"
      ]
     },
     "metadata": {},
     "execution_count": 25
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    " Now, all missing values in label data are filled properly."
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "source": [
    "# save the processed label\r\n",
    "label.index.freq = None\r\n",
    "label.to_pickle('label.pkl')"
   ],
   "outputs": [],
   "metadata": {}
  }
 ]
}