diff --git a/notebooks/01. Univariate.ipynb b/notebooks/01. Univariate.ipynb
index 4834cff..31c0130 100644
--- a/notebooks/01. Univariate.ipynb	
+++ b/notebooks/01. Univariate.ipynb	
@@ -2,18 +2,14 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": null,
    "outputs": [],
    "source": [
     "import numpy as np\n",
     "import pandas as pd"
    ],
    "metadata": {
-    "collapsed": false,
-    "ExecuteTime": {
-     "end_time": "2023-11-30T11:32:27.248716Z",
-     "start_time": "2023-11-30T11:32:25.943764Z"
-    }
+    "collapsed": false
    }
   },
   {
@@ -27,128 +23,71 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": null,
    "outputs": [],
    "source": [
-    "from enfobench.dataset import DemandDataset\n",
+    "from enfobench.datasets import ElectricityDemandDataset\n",
     "\n",
-    "ds = DemandDataset(\"../data\")"
+    "ds = ElectricityDemandDataset(\"../data/electricity-demand\")"
    ],
    "metadata": {
-    "collapsed": false,
-    "ExecuteTime": {
-     "end_time": "2023-11-30T11:32:57.878232Z",
-     "start_time": "2023-11-30T11:32:56.956499Z"
-    }
+    "collapsed": false
    }
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": null,
    "outputs": [],
    "source": [
     "unique_ids = ds.metadata_subset.list_unique_ids()"
    ],
    "metadata": {
-    "collapsed": false,
-    "ExecuteTime": {
-     "end_time": "2023-11-30T11:32:58.402183Z",
-     "start_time": "2023-11-30T11:32:58.363959Z"
-    }
+    "collapsed": false
    }
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
-   "outputs": [
-    {
-     "data": {
-      "text/plain": "100"
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "execution_count": null,
+   "outputs": [],
    "source": [
     "len(unique_ids)"
    ],
    "metadata": {
-    "collapsed": false,
-    "ExecuteTime": {
-     "end_time": "2023-11-30T11:32:58.818478Z",
-     "start_time": "2023-11-30T11:32:58.814717Z"
-    }
+    "collapsed": false
    }
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
-   "outputs": [
-    {
-     "data": {
-      "text/plain": "'70aec446c47486f3'"
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "execution_count": null,
+   "outputs": [],
    "source": [
     "unique_id = unique_ids[0]\n",
     "unique_id"
    ],
    "metadata": {
-    "collapsed": false,
-    "ExecuteTime": {
-     "end_time": "2023-11-30T11:32:59.382054Z",
-     "start_time": "2023-11-30T11:32:59.373024Z"
-    }
+    "collapsed": false
    }
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": null,
    "outputs": [],
    "source": [
     "target, _, metadata = ds.get_data_by_unique_id(unique_id)"
    ],
    "metadata": {
-    "collapsed": false,
-    "ExecuteTime": {
-     "end_time": "2023-11-30T11:33:00.059502Z",
-     "start_time": "2023-11-30T11:32:59.958501Z"
-    }
+    "collapsed": false
    }
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "<class 'pandas.core.frame.DataFrame'>\n",
-      "DatetimeIndex: 24320 entries, 2012-10-09 08:30:00 to 2014-02-28 00:00:00\n",
-      "Data columns (total 1 columns):\n",
-      " #   Column  Non-Null Count  Dtype  \n",
-      "---  ------  --------------  -----  \n",
-      " 0   y       24317 non-null  float64\n",
-      "dtypes: float64(1)\n",
-      "memory usage: 380.0 KB\n"
-     ]
-    }
-   ],
+   "execution_count": null,
+   "outputs": [],
    "source": [
     "target.info()"
    ],
    "metadata": {
-    "collapsed": false,
-    "ExecuteTime": {
-     "end_time": "2023-11-30T11:33:00.926993Z",
-     "start_time": "2023-11-30T11:33:00.910567Z"
-    }
+    "collapsed": false
    }
   },
   {
@@ -171,24 +110,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": null,
    "outputs": [],
    "source": [
-    "from enfobench.dataset import Dataset\n",
+    "from enfobench import Dataset\n",
     "\n",
     "univariate_dataset = Dataset(\n",
-    "    target=target.loc[\"2012-01-01\":\"2013-12-31\"],\n",
-    "    past_covariates=None,\n",
-    "    future_covariates=None,\n",
+    "    target=target,\n",
     "    metadata=metadata,\n",
     ")"
    ],
    "metadata": {
-    "collapsed": false,
-    "ExecuteTime": {
-     "end_time": "2023-11-30T11:33:03.990049Z",
-     "start_time": "2023-11-30T11:33:03.984986Z"
-    }
+    "collapsed": false
    }
   },
   {
@@ -200,16 +133,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2023-11-30T11:33:05.265754Z",
-     "start_time": "2023-11-30T11:33:05.262473Z"
-    }
-   },
+   "execution_count": null,
+   "metadata": {},
    "outputs": [],
    "source": [
-    "from enfobench.evaluation import AuthorInfo, ModelInfo, ForecasterType\n",
+    "from enfobench import AuthorInfo, ModelInfo, ForecasterType\n",
     "from enfobench.evaluation.utils import create_forecast_index\n",
     "\n",
     "\n",
@@ -233,6 +161,7 @@
     "        history: pd.DataFrame,\n",
     "        past_covariates=None,\n",
     "        future_covariates=None,\n",
+    "        metadata: dict | None = None,\n",
     "        level=None,\n",
     "        **kwargs,\n",
     "    ):\n",
@@ -247,17 +176,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": null,
    "outputs": [],
    "source": [
     "model = ExampleModel(1)"
    ],
    "metadata": {
-    "collapsed": false,
-    "ExecuteTime": {
-     "end_time": "2023-11-30T11:33:05.634539Z",
-     "start_time": "2023-11-30T11:33:05.628875Z"
-    }
+    "collapsed": false
    }
   },
   {
@@ -269,30 +194,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2023-11-30T11:33:06.302818Z",
-     "start_time": "2023-11-30T11:33:06.120268Z"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "100%|██████████| 30/30 [00:00<00:00, 292.54it/s]\n"
-     ]
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "from enfobench.evaluation import cross_validate\n",
     "\n",
     "crossval_df = cross_validate(\n",
     "    model,\n",
     "    univariate_dataset,\n",
-    "    start_date=pd.Timestamp(\"2013-01-01T00:00:00\"),\n",
-    "    end_date=pd.Timestamp(\"2013-02-01T00:00:00\"),\n",
+    "    start_date=pd.Timestamp(\"2022-01-01T10:00:00\"),\n",
+    "    end_date=pd.Timestamp(\"2022-02-01T00:00:00\"),\n",
     "    horizon=pd.Timedelta(\"38 hours\"),\n",
     "    step=pd.Timedelta(\"1 day\"),\n",
     ")"
@@ -300,61 +212,25 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2023-11-30T11:33:06.484260Z",
-     "start_time": "2023-11-30T11:33:06.475898Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": "  cutoff_date           timestamp      yhat      y\n0  2013-01-01 2013-01-01 00:30:00  1.164622  0.303\n1  2013-01-01 2013-01-01 01:00:00  1.164622  0.085\n2  2013-01-01 2013-01-01 01:30:00  1.164622  0.270\n3  2013-01-01 2013-01-01 02:00:00  1.164622  0.084\n4  2013-01-01 2013-01-01 02:30:00  1.164622  0.184",
-      "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>cutoff_date</th>\n      <th>timestamp</th>\n      <th>yhat</th>\n      <th>y</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>2013-01-01</td>\n      <td>2013-01-01 00:30:00</td>\n      <td>1.164622</td>\n      <td>0.303</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>2013-01-01</td>\n      <td>2013-01-01 01:00:00</td>\n      <td>1.164622</td>\n      <td>0.085</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>2013-01-01</td>\n      <td>2013-01-01 01:30:00</td>\n      <td>1.164622</td>\n      <td>0.270</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>2013-01-01</td>\n      <td>2013-01-01 02:00:00</td>\n      <td>1.164622</td>\n      <td>0.084</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>2013-01-01</td>\n      <td>2013-01-01 02:30:00</td>\n      <td>1.164622</td>\n      <td>0.184</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
-     },
-     "execution_count": 12,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "crossval_df.head()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
-   "outputs": [
-    {
-     "data": {
-      "text/plain": "<Axes: xlabel='timestamp'>"
-     },
-     "execution_count": 13,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "text/plain": "<Figure size 640x480 with 1 Axes>",
-      "image/png": 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"
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "execution_count": null,
+   "outputs": [],
    "source": [
-    "cutoff_date_to_plot = \"2013-01-02\"\n",
-    "crossval_df.loc[crossval_df.cutoff_date == cutoff_date_to_plot].set_index(\n",
-    "    \"timestamp\"\n",
-    ").drop(columns=[\"cutoff_date\"]).plot()"
+    "cutoff_date_to_plot = crossval_df.cutoff_date.unique()[0]\n",
+    "crossval_df.loc[crossval_df.cutoff_date == cutoff_date_to_plot].set_index(\"timestamp\").drop(\n",
+    "    columns=[\"cutoff_date\"]\n",
+    ").plot()"
    ],
    "metadata": {
-    "collapsed": false,
-    "ExecuteTime": {
-     "end_time": "2023-11-30T11:33:07.804978Z",
-     "start_time": "2023-11-30T11:33:06.838961Z"
-    }
+    "collapsed": false
    }
   },
   {
@@ -366,13 +242,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2023-11-30T11:33:07.805228Z",
-     "start_time": "2023-11-30T11:33:07.798161Z"
-    }
-   },
+   "execution_count": null,
+   "metadata": {},
    "outputs": [],
    "source": [
     "from enfobench.evaluation import evaluate_metrics\n",
@@ -381,58 +252,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
-   "outputs": [
-    {
-     "data": {
-      "text/plain": "        MAE       MBE  weight\n0  1.009067  1.008643     1.0",
-      "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>MAE</th>\n      <th>MBE</th>\n      <th>weight</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>1.009067</td>\n      <td>1.008643</td>\n      <td>1.0</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
-     },
-     "execution_count": 15,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "execution_count": null,
+   "outputs": [],
    "source": [
-    "evaluate_metrics(\n",
-    "    crossval_df, metrics={\"MAE\": mean_absolute_error, \"MBE\": mean_bias_error}\n",
-    ")"
+    "evaluate_metrics(crossval_df, metrics={\"MAE\": mean_absolute_error, \"MBE\": mean_bias_error})"
    ],
    "metadata": {
-    "collapsed": false,
-    "ExecuteTime": {
-     "end_time": "2023-11-30T11:33:07.886233Z",
-     "start_time": "2023-11-30T11:33:07.805105Z"
-    }
+    "collapsed": false
    }
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2023-11-30T11:33:08.005735Z",
-     "start_time": "2023-11-30T11:33:07.908132Z"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "100%|██████████| 30/30 [00:00<00:00, 462.36it/s]\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": "   cutoff_date       MAE       MBE  weight\n0   2013-01-01  0.961201  0.961201     1.0\n1   2013-01-02  0.942087  0.941597     1.0\n2   2013-01-03  0.943087  0.942621     1.0\n3   2013-01-04  0.999754  0.999754     1.0\n4   2013-01-05  0.985921  0.976416     1.0\n5   2013-01-06  1.019952  1.019952     1.0\n6   2013-01-07  1.054776  1.054776     1.0\n7   2013-01-08  1.025911  1.023660     1.0\n8   2013-01-09  1.053617  1.053617     1.0\n9   2013-01-10  1.012036  1.012036     1.0\n10  2013-01-11  0.988310  0.988310     1.0\n11  2013-01-12  0.984110  0.984110     1.0\n12  2013-01-13  0.980007  0.980007     1.0\n13  2013-01-14  1.023716  1.023716     1.0\n14  2013-01-15  1.028216  1.028216     1.0\n15  2013-01-16  0.992922  0.992922     1.0\n16  2013-01-17  1.000755  1.000755     1.0\n17  2013-01-18  1.009363  1.009363     1.0\n18  2013-01-19  1.025783  1.025783     1.0\n19  2013-01-20  1.007544  1.007544     1.0\n20  2013-01-21  1.024339  1.024339     1.0\n21  2013-01-22  1.019156  1.019156     1.0\n22  2013-01-23  1.025756  1.025756     1.0\n23  2013-01-24  1.055964  1.055964     1.0\n24  2013-01-25  0.989768  0.989768     1.0\n25  2013-01-26  0.997766  0.997766     1.0\n26  2013-01-27  1.011640  1.011640     1.0\n27  2013-01-28  1.035257  1.035257     1.0\n28  2013-01-29  1.045862  1.045862     1.0\n29  2013-01-30  1.027431  1.027431     1.0",
-      "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>cutoff_date</th>\n      <th>MAE</th>\n      <th>MBE</th>\n      <th>weight</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>2013-01-01</td>\n      <td>0.961201</td>\n      <td>0.961201</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>2013-01-02</td>\n      <td>0.942087</td>\n      <td>0.941597</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>2013-01-03</td>\n      <td>0.943087</td>\n      <td>0.942621</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>2013-01-04</td>\n      <td>0.999754</td>\n      <td>0.999754</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>2013-01-05</td>\n      <td>0.985921</td>\n      <td>0.976416</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>2013-01-06</td>\n      <td>1.019952</td>\n      <td>1.019952</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>2013-01-07</td>\n      <td>1.054776</td>\n      <td>1.054776</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>2013-01-08</td>\n      <td>1.025911</td>\n      <td>1.023660</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td>2013-01-09</td>\n      <td>1.053617</td>\n      <td>1.053617</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>2013-01-10</td>\n      <td>1.012036</td>\n      <td>1.012036</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>10</th>\n      <td>2013-01-11</td>\n      <td>0.988310</td>\n      <td>0.988310</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>11</th>\n      <td>2013-01-12</td>\n      <td>0.984110</td>\n      <td>0.984110</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>12</th>\n      <td>2013-01-13</td>\n      <td>0.980007</td>\n      <td>0.980007</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>13</th>\n      <td>2013-01-14</td>\n      <td>1.023716</td>\n      <td>1.023716</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>14</th>\n      <td>2013-01-15</td>\n      <td>1.028216</td>\n      <td>1.028216</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>15</th>\n      <td>2013-01-16</td>\n      <td>0.992922</td>\n      <td>0.992922</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>16</th>\n      <td>2013-01-17</td>\n      <td>1.000755</td>\n      <td>1.000755</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>17</th>\n      <td>2013-01-18</td>\n      <td>1.009363</td>\n      <td>1.009363</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>18</th>\n      <td>2013-01-19</td>\n      <td>1.025783</td>\n      <td>1.025783</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>19</th>\n      <td>2013-01-20</td>\n      <td>1.007544</td>\n      <td>1.007544</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>20</th>\n      <td>2013-01-21</td>\n      <td>1.024339</td>\n      <td>1.024339</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>21</th>\n      <td>2013-01-22</td>\n      <td>1.019156</td>\n      <td>1.019156</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>22</th>\n      <td>2013-01-23</td>\n      <td>1.025756</td>\n      <td>1.025756</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>23</th>\n      <td>2013-01-24</td>\n      <td>1.055964</td>\n      <td>1.055964</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>24</th>\n      <td>2013-01-25</td>\n      <td>0.989768</td>\n      <td>0.989768</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>25</th>\n      <td>2013-01-26</td>\n      <td>0.997766</td>\n      <td>0.997766</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>26</th>\n      <td>2013-01-27</td>\n      <td>1.011640</td>\n      <td>1.011640</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>27</th>\n      <td>2013-01-28</td>\n      <td>1.035257</td>\n      <td>1.035257</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>28</th>\n      <td>2013-01-29</td>\n      <td>1.045862</td>\n      <td>1.045862</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>29</th>\n      <td>2013-01-30</td>\n      <td>1.027431</td>\n      <td>1.027431</td>\n      <td>1.0</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
-     },
-     "execution_count": 16,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "metrics = evaluate_metrics(\n",
     "    crossval_df,\n",
@@ -444,34 +277,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2023-11-30T11:33:08.321288Z",
-     "start_time": "2023-11-30T11:33:08.030087Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": "<Axes: >"
-     },
-     "execution_count": 17,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "text/plain": "<Figure size 640x480 with 1 Axes>",
-      "image/png": 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"
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "metrics[[\"MAE\", \"MBE\"]].plot()"
    ]
+  },
+  {
+   "cell_type": "code",
+   "outputs": [],
+   "source": [],
+   "metadata": {
+    "collapsed": false
+   }
   }
  ],
  "metadata": {
diff --git a/notebooks/02. Multivariate.ipynb b/notebooks/02. Multivariate.ipynb
index 7c4796d..88e79b3 100644
--- a/notebooks/02. Multivariate.ipynb	
+++ b/notebooks/02. Multivariate.ipynb	
@@ -2,34 +2,14 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2023-11-24T12:10:16.618111Z",
-     "start_time": "2023-11-24T12:10:14.906920Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "import os\n",
-    "\n",
-    "os.chdir(\"..\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": null,
    "outputs": [],
    "source": [
     "import numpy as np\n",
     "import pandas as pd"
    ],
    "metadata": {
-    "collapsed": false,
-    "ExecuteTime": {
-     "end_time": "2023-11-24T12:10:17.224425Z",
-     "start_time": "2023-11-24T12:10:14.933709Z"
-    }
+    "collapsed": false
    }
   },
   {
@@ -43,167 +23,82 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": null,
    "outputs": [],
    "source": [
-    "from enfobench.dataset import DemandDataset\n",
+    "from enfobench.datasets import ElectricityDemandDataset\n",
     "\n",
-    "ds = DemandDataset(\"data\")"
+    "ds = ElectricityDemandDataset(\"../data/electricity-demand\")"
    ],
    "metadata": {
-    "collapsed": false,
-    "ExecuteTime": {
-     "end_time": "2023-11-24T12:10:21.073098Z",
-     "start_time": "2023-11-24T12:10:16.948967Z"
-    }
+    "collapsed": false
    }
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": null,
    "outputs": [],
    "source": [
     "unique_ids = ds.metadata_subset.list_unique_ids()"
    ],
    "metadata": {
-    "collapsed": false,
-    "ExecuteTime": {
-     "end_time": "2023-11-24T12:10:21.074863Z",
-     "start_time": "2023-11-24T12:10:20.730741Z"
-    }
+    "collapsed": false
    }
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
-   "outputs": [
-    {
-     "data": {
-      "text/plain": "100"
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "execution_count": null,
+   "outputs": [],
    "source": [
     "len(unique_ids)"
    ],
    "metadata": {
-    "collapsed": false,
-    "ExecuteTime": {
-     "end_time": "2023-11-24T12:10:21.156745Z",
-     "start_time": "2023-11-24T12:10:20.816147Z"
-    }
+    "collapsed": false
    }
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
-   "outputs": [
-    {
-     "data": {
-      "text/plain": "'70aec446c47486f3'"
-     },
-     "execution_count": 6,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "execution_count": null,
+   "outputs": [],
    "source": [
     "unique_id = unique_ids[0]\n",
     "unique_id"
    ],
    "metadata": {
-    "collapsed": false,
-    "ExecuteTime": {
-     "end_time": "2023-11-24T12:10:21.157656Z",
-     "start_time": "2023-11-24T12:10:20.851260Z"
-    }
+    "collapsed": false
    }
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": null,
    "outputs": [],
    "source": [
     "target, past_covariates, metadata = ds.get_data_by_unique_id(unique_id)"
    ],
    "metadata": {
-    "collapsed": false,
-    "ExecuteTime": {
-     "end_time": "2023-11-24T12:10:22.031467Z",
-     "start_time": "2023-11-24T12:10:20.884586Z"
-    }
+    "collapsed": false
    }
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "<class 'pandas.core.frame.DataFrame'>\n",
-      "DatetimeIndex: 24320 entries, 2012-10-09 08:30:00 to 2014-02-28 00:00:00\n",
-      "Data columns (total 1 columns):\n",
-      " #   Column  Non-Null Count  Dtype  \n",
-      "---  ------  --------------  -----  \n",
-      " 0   y       24317 non-null  float64\n",
-      "dtypes: float64(1)\n",
-      "memory usage: 380.0 KB\n"
-     ]
-    }
-   ],
+   "execution_count": null,
+   "outputs": [],
    "source": [
     "target.info()"
    ],
    "metadata": {
-    "collapsed": false,
-    "ExecuteTime": {
-     "end_time": "2023-11-24T12:10:22.032499Z",
-     "start_time": "2023-11-24T12:10:21.221214Z"
-    }
+    "collapsed": false
    }
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "<class 'pandas.core.frame.DataFrame'>\n",
-      "DatetimeIndex: 19862 entries, 2011-11-23 11:00:00 to 2014-02-28 00:00:00\n",
-      "Data columns (total 10 columns):\n",
-      " #   Column           Non-Null Count  Dtype  \n",
-      "---  ------           --------------  -----  \n",
-      " 0   temperature      19862 non-null  float32\n",
-      " 1   dew_point        19862 non-null  float32\n",
-      " 2   pressure         19862 non-null  float32\n",
-      " 3   wind_speed       19862 non-null  float32\n",
-      " 4   wind_gust        19862 non-null  float32\n",
-      " 5   wind_bearing     19862 non-null  float32\n",
-      " 6   precipitation    19862 non-null  float32\n",
-      " 7   snow             19862 non-null  float32\n",
-      " 8   cloud_cover      19862 non-null  float32\n",
-      " 9   solar_radiation  19862 non-null  float32\n",
-      "dtypes: float32(10)\n",
-      "memory usage: 931.0 KB\n"
-     ]
-    }
-   ],
+   "execution_count": null,
+   "outputs": [],
    "source": [
     "past_covariates.info()"
    ],
    "metadata": {
-    "collapsed": false,
-    "ExecuteTime": {
-     "end_time": "2023-11-24T12:10:22.075380Z",
-     "start_time": "2023-11-24T12:10:21.271554Z"
-    }
+    "collapsed": false
    }
   },
   {
@@ -217,10 +112,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": null,
    "outputs": [],
    "source": [
-    "from enfobench.dataset.utils import create_perfect_forecasts_from_covariates\n",
+    "from enfobench.datasets.utils import create_perfect_forecasts_from_covariates\n",
     "\n",
     "perfect_forecasts = create_perfect_forecasts_from_covariates(\n",
     "    past_covariates,\n",
@@ -229,102 +124,40 @@
     ")"
    ],
    "metadata": {
-    "collapsed": false,
-    "ExecuteTime": {
-     "end_time": "2023-11-24T12:10:26.189047Z",
-     "start_time": "2023-11-24T12:10:21.312034Z"
-    }
+    "collapsed": false
    }
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "<class 'pandas.core.frame.DataFrame'>\n",
-      "RangeIndex: 137928 entries, 0 to 137927\n",
-      "Data columns (total 12 columns):\n",
-      " #   Column           Non-Null Count   Dtype         \n",
-      "---  ------           --------------   -----         \n",
-      " 0   timestamp        137928 non-null  datetime64[ns]\n",
-      " 1   cutoff_date      137928 non-null  datetime64[ns]\n",
-      " 2   temperature      137928 non-null  float32       \n",
-      " 3   dew_point        137928 non-null  float32       \n",
-      " 4   pressure         137928 non-null  float32       \n",
-      " 5   wind_speed       137928 non-null  float32       \n",
-      " 6   wind_gust        137928 non-null  float32       \n",
-      " 7   wind_bearing     137928 non-null  float32       \n",
-      " 8   precipitation    137928 non-null  float32       \n",
-      " 9   snow             137928 non-null  float32       \n",
-      " 10  cloud_cover      137928 non-null  float32       \n",
-      " 11  solar_radiation  137928 non-null  float32       \n",
-      "dtypes: datetime64[ns](2), float32(10)\n",
-      "memory usage: 7.4 MB\n"
-     ]
-    }
-   ],
+   "execution_count": null,
+   "outputs": [],
    "source": [
     "perfect_forecasts.info()"
    ],
    "metadata": {
-    "collapsed": false,
-    "ExecuteTime": {
-     "end_time": "2023-11-24T12:10:26.448389Z",
-     "start_time": "2023-11-24T12:10:26.168822Z"
-    }
+    "collapsed": false
    }
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
-   "outputs": [
-    {
-     "data": {
-      "text/plain": "            timestamp         cutoff_date  temperature  dew_point  \\\n0 2011-11-23 12:00:00 2011-11-23 11:00:00     8.515167   6.053314   \n1 2011-11-23 13:00:00 2011-11-23 11:00:00     9.366150   6.898102   \n2 2011-11-23 14:00:00 2011-11-23 11:00:00     9.818268   7.336670   \n3 2011-11-23 15:00:00 2011-11-23 11:00:00     9.655426   7.545349   \n4 2011-11-23 16:00:00 2011-11-23 11:00:00     9.179779   7.419464   \n\n      pressure  wind_speed  wind_gust  wind_bearing  precipitation  snow  \\\n0  1018.570374    3.163105   7.092710    227.930420  -2.328306e-07   0.0   \n1  1018.497498    3.694861   7.186031    235.672974  -2.328306e-07   0.0   \n2  1018.578125    3.870760   6.684762    234.186523  -2.328306e-07   0.0   \n3  1018.738159    3.420094   5.839092    232.220032  -2.328306e-07   0.0   \n4  1018.985535    3.134620   5.201840    221.905396  -2.328306e-07   0.0   \n\n   cloud_cover  solar_radiation  \n0          1.0       247.303711  \n1          1.0       219.641495  \n2          1.0       151.042435  \n3          1.0        68.137222  \n4          1.0        15.961771  ",
-      "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>timestamp</th>\n      <th>cutoff_date</th>\n      <th>temperature</th>\n      <th>dew_point</th>\n      <th>pressure</th>\n      <th>wind_speed</th>\n      <th>wind_gust</th>\n      <th>wind_bearing</th>\n      <th>precipitation</th>\n      <th>snow</th>\n      <th>cloud_cover</th>\n      <th>solar_radiation</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>2011-11-23 12:00:00</td>\n      <td>2011-11-23 11:00:00</td>\n      <td>8.515167</td>\n      <td>6.053314</td>\n      <td>1018.570374</td>\n      <td>3.163105</td>\n      <td>7.092710</td>\n      <td>227.930420</td>\n      <td>-2.328306e-07</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>247.303711</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>2011-11-23 13:00:00</td>\n      <td>2011-11-23 11:00:00</td>\n      <td>9.366150</td>\n      <td>6.898102</td>\n      <td>1018.497498</td>\n      <td>3.694861</td>\n      <td>7.186031</td>\n      <td>235.672974</td>\n      <td>-2.328306e-07</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>219.641495</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>2011-11-23 14:00:00</td>\n      <td>2011-11-23 11:00:00</td>\n      <td>9.818268</td>\n      <td>7.336670</td>\n      <td>1018.578125</td>\n      <td>3.870760</td>\n      <td>6.684762</td>\n      <td>234.186523</td>\n      <td>-2.328306e-07</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>151.042435</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>2011-11-23 15:00:00</td>\n      <td>2011-11-23 11:00:00</td>\n      <td>9.655426</td>\n      <td>7.545349</td>\n      <td>1018.738159</td>\n      <td>3.420094</td>\n      <td>5.839092</td>\n      <td>232.220032</td>\n      <td>-2.328306e-07</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>68.137222</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>2011-11-23 16:00:00</td>\n      <td>2011-11-23 11:00:00</td>\n      <td>9.179779</td>\n      <td>7.419464</td>\n      <td>1018.985535</td>\n      <td>3.134620</td>\n      <td>5.201840</td>\n      <td>221.905396</td>\n      <td>-2.328306e-07</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>15.961771</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
-     },
-     "execution_count": 12,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "execution_count": null,
+   "outputs": [],
    "source": [
     "perfect_forecasts.head()"
    ],
    "metadata": {
-    "collapsed": false,
-    "ExecuteTime": {
-     "end_time": "2023-11-24T12:10:26.674093Z",
-     "start_time": "2023-11-24T12:10:26.227983Z"
-    }
+    "collapsed": false
    }
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
-   "outputs": [
-    {
-     "data": {
-      "text/plain": "                 timestamp         cutoff_date  temperature  dew_point  \\\n137923 2014-02-27 07:00:00 2014-02-20 11:00:00     6.506134   5.168854   \n137924 2014-02-27 08:00:00 2014-02-20 11:00:00     6.726654   5.417999   \n137925 2014-02-27 09:00:00 2014-02-20 11:00:00     7.474091   5.614899   \n137926 2014-02-27 10:00:00 2014-02-20 11:00:00     7.983521   4.091736   \n137927 2014-02-27 11:00:00 2014-02-20 11:00:00     8.863586   3.427490   \n\n          pressure  wind_speed  wind_gust  wind_bearing  precipitation  \\\n137923  995.314026    8.122930  15.462187    188.039062       1.058658   \n137924  995.132080    6.130317  11.876844    218.965332       0.727250   \n137925  996.073792    6.359778  12.091746    265.768311       0.092959   \n137926  996.695862    6.367825  12.284778    273.792236       0.002462   \n137927  997.391602    6.639326  13.095889    275.929749       0.002462   \n\n                snow  cloud_cover  solar_radiation  \n137923  2.910383e-08     1.000000         0.000000  \n137924  2.910383e-08     1.000000         5.516424  \n137925  2.910383e-08     0.962584        37.017605  \n137926  2.910383e-08     0.443670       203.822159  \n137927  2.910383e-08     0.358461       357.286163  ",
-      "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>timestamp</th>\n      <th>cutoff_date</th>\n      <th>temperature</th>\n      <th>dew_point</th>\n      <th>pressure</th>\n      <th>wind_speed</th>\n      <th>wind_gust</th>\n      <th>wind_bearing</th>\n      <th>precipitation</th>\n      <th>snow</th>\n      <th>cloud_cover</th>\n      <th>solar_radiation</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>137923</th>\n      <td>2014-02-27 07:00:00</td>\n      <td>2014-02-20 11:00:00</td>\n      <td>6.506134</td>\n      <td>5.168854</td>\n      <td>995.314026</td>\n      <td>8.122930</td>\n      <td>15.462187</td>\n      <td>188.039062</td>\n      <td>1.058658</td>\n      <td>2.910383e-08</td>\n      <td>1.000000</td>\n      <td>0.000000</td>\n    </tr>\n    <tr>\n      <th>137924</th>\n      <td>2014-02-27 08:00:00</td>\n      <td>2014-02-20 11:00:00</td>\n      <td>6.726654</td>\n      <td>5.417999</td>\n      <td>995.132080</td>\n      <td>6.130317</td>\n      <td>11.876844</td>\n      <td>218.965332</td>\n      <td>0.727250</td>\n      <td>2.910383e-08</td>\n      <td>1.000000</td>\n      <td>5.516424</td>\n    </tr>\n    <tr>\n      <th>137925</th>\n      <td>2014-02-27 09:00:00</td>\n      <td>2014-02-20 11:00:00</td>\n      <td>7.474091</td>\n      <td>5.614899</td>\n      <td>996.073792</td>\n      <td>6.359778</td>\n      <td>12.091746</td>\n      <td>265.768311</td>\n      <td>0.092959</td>\n      <td>2.910383e-08</td>\n      <td>0.962584</td>\n      <td>37.017605</td>\n    </tr>\n    <tr>\n      <th>137926</th>\n      <td>2014-02-27 10:00:00</td>\n      <td>2014-02-20 11:00:00</td>\n      <td>7.983521</td>\n      <td>4.091736</td>\n      <td>996.695862</td>\n      <td>6.367825</td>\n      <td>12.284778</td>\n      <td>273.792236</td>\n      <td>0.002462</td>\n      <td>2.910383e-08</td>\n      <td>0.443670</td>\n      <td>203.822159</td>\n    </tr>\n    <tr>\n      <th>137927</th>\n      <td>2014-02-27 11:00:00</td>\n      <td>2014-02-20 11:00:00</td>\n      <td>8.863586</td>\n      <td>3.427490</td>\n      <td>997.391602</td>\n      <td>6.639326</td>\n      <td>13.095889</td>\n      <td>275.929749</td>\n      <td>0.002462</td>\n      <td>2.910383e-08</td>\n      <td>0.358461</td>\n      <td>357.286163</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
-     },
-     "execution_count": 13,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "execution_count": null,
+   "outputs": [],
    "source": [
     "perfect_forecasts.tail()"
    ],
    "metadata": {
-    "collapsed": false,
-    "ExecuteTime": {
-     "end_time": "2023-11-24T12:10:26.677703Z",
-     "start_time": "2023-11-24T12:10:26.330902Z"
-    }
+    "collapsed": false
    }
   },
   {
@@ -338,23 +171,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": null,
    "outputs": [],
    "source": [
-    "from enfobench.dataset import Dataset\n",
+    "from enfobench import Dataset\n",
     "\n",
     "multivariate_dataset = Dataset(\n",
-    "    target=target.loc[\"2012-01-01\":\"2013-12-31\"],\n",
+    "    target=target,\n",
     "    past_covariates=past_covariates,\n",
     "    metadata=metadata,\n",
     ")"
    ],
    "metadata": {
-    "collapsed": false,
-    "ExecuteTime": {
-     "end_time": "2023-11-24T12:10:26.733067Z",
-     "start_time": "2023-11-24T12:10:26.426454Z"
-    }
+    "collapsed": false
    }
   },
   {
@@ -375,16 +204,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2023-11-24T12:10:26.877280Z",
-     "start_time": "2023-11-24T12:10:26.468246Z"
-    }
-   },
+   "execution_count": null,
+   "metadata": {},
    "outputs": [],
    "source": [
-    "from enfobench.evaluation import AuthorInfo, ModelInfo, ForecasterType\n",
+    "from enfobench import AuthorInfo, ModelInfo, ForecasterType\n",
     "from enfobench.evaluation.utils import create_forecast_index\n",
     "\n",
     "\n",
@@ -410,6 +234,7 @@
     "        history: pd.DataFrame,\n",
     "        past_covariates=None,\n",
     "        future_covariates=None,\n",
+    "        metadata: dict | None = None,\n",
     "        level=None,\n",
     "        **kwargs,\n",
     "    ):\n",
@@ -425,13 +250,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2023-11-24T12:10:26.877890Z",
-     "start_time": "2023-11-24T12:10:26.492833Z"
-    }
-   },
+   "execution_count": null,
+   "metadata": {},
    "outputs": [],
    "source": [
     "model = ExampleModel(1)"
@@ -448,30 +268,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2023-11-24T12:10:28.054510Z",
-     "start_time": "2023-11-24T12:10:26.523159Z"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "100%|██████████| 30/30 [00:00<00:00, 51.92it/s]\n"
-     ]
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "from enfobench.evaluation import cross_validate\n",
     "\n",
     "crossval_df = cross_validate(\n",
     "    model,\n",
     "    multivariate_dataset,\n",
-    "    start_date=pd.Timestamp(\"2013-01-01T00:00:00\"),\n",
-    "    end_date=pd.Timestamp(\"2013-02-01T00:00:00\"),\n",
+    "    start_date=pd.Timestamp(\"2022-01-01T10:00:00\"),\n",
+    "    end_date=pd.Timestamp(\"2022-02-01T00:00:00\"),\n",
     "    horizon=pd.Timedelta(\"38 hours\"),\n",
     "    step=pd.Timedelta(\"1 day\"),\n",
     ")"
@@ -479,61 +286,25 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2023-11-24T12:10:28.081914Z",
-     "start_time": "2023-11-24T12:10:27.267929Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": "  cutoff_date           timestamp      yhat      y\n0  2013-01-01 2013-01-01 00:30:00  1.164622  0.303\n1  2013-01-01 2013-01-01 01:00:00  1.164622  0.085\n2  2013-01-01 2013-01-01 01:30:00  1.164622  0.270\n3  2013-01-01 2013-01-01 02:00:00  1.164622  0.084\n4  2013-01-01 2013-01-01 02:30:00  1.164622  0.184",
-      "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>cutoff_date</th>\n      <th>timestamp</th>\n      <th>yhat</th>\n      <th>y</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>2013-01-01</td>\n      <td>2013-01-01 00:30:00</td>\n      <td>1.164622</td>\n      <td>0.303</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>2013-01-01</td>\n      <td>2013-01-01 01:00:00</td>\n      <td>1.164622</td>\n      <td>0.085</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>2013-01-01</td>\n      <td>2013-01-01 01:30:00</td>\n      <td>1.164622</td>\n      <td>0.270</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>2013-01-01</td>\n      <td>2013-01-01 02:00:00</td>\n      <td>1.164622</td>\n      <td>0.084</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>2013-01-01</td>\n      <td>2013-01-01 02:30:00</td>\n      <td>1.164622</td>\n      <td>0.184</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
-     },
-     "execution_count": 18,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "crossval_df.head()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
-   "outputs": [
-    {
-     "data": {
-      "text/plain": "<Axes: xlabel='timestamp'>"
-     },
-     "execution_count": 19,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "text/plain": "<Figure size 640x480 with 1 Axes>",
-      "image/png": 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"
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "execution_count": null,
+   "outputs": [],
    "source": [
-    "cutoff_date_to_plot = \"2013-01-02\"\n",
-    "crossval_df.loc[crossval_df.cutoff_date == cutoff_date_to_plot].set_index(\n",
-    "    \"timestamp\"\n",
-    ").drop(columns=[\"cutoff_date\"]).plot()"
+    "cutoff_date_to_plot = crossval_df.cutoff_date.unique()[0]\n",
+    "crossval_df.loc[crossval_df.cutoff_date == cutoff_date_to_plot].set_index(\"timestamp\").drop(\n",
+    "    columns=[\"cutoff_date\"]\n",
+    ").plot()"
    ],
    "metadata": {
-    "collapsed": false,
-    "ExecuteTime": {
-     "end_time": "2023-11-24T12:10:30.570378Z",
-     "start_time": "2023-11-24T12:10:27.308767Z"
-    }
+    "collapsed": false
    }
   },
   {
@@ -545,13 +316,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2023-11-24T12:10:30.570916Z",
-     "start_time": "2023-11-24T12:10:30.435709Z"
-    }
-   },
+   "execution_count": null,
+   "metadata": {},
    "outputs": [],
    "source": [
     "from enfobench.evaluation import evaluate_metrics\n",
@@ -560,51 +326,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2023-11-24T12:10:30.599530Z",
-     "start_time": "2023-11-24T12:10:30.484444Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": "        MAE       MBE  weight\n0  1.009067  1.008643     1.0",
-      "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>MAE</th>\n      <th>MBE</th>\n      <th>weight</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>1.009067</td>\n      <td>1.008643</td>\n      <td>1.0</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
-     },
-     "execution_count": 21,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
-    "evaluate_metrics(\n",
-    "    crossval_df, metrics={\"MAE\": mean_absolute_error, \"MBE\": mean_bias_error}\n",
-    ")"
+    "evaluate_metrics(crossval_df, metrics={\"MAE\": mean_absolute_error, \"MBE\": mean_bias_error})"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "100%|██████████| 30/30 [00:00<00:00, 71.59it/s]\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": "   cutoff_date       MAE       MBE  weight\n0   2013-01-01  0.961201  0.961201     1.0\n1   2013-01-02  0.942087  0.941597     1.0\n2   2013-01-03  0.943087  0.942621     1.0\n3   2013-01-04  0.999754  0.999754     1.0\n4   2013-01-05  0.985921  0.976416     1.0\n5   2013-01-06  1.019952  1.019952     1.0\n6   2013-01-07  1.054776  1.054776     1.0\n7   2013-01-08  1.025911  1.023660     1.0\n8   2013-01-09  1.053617  1.053617     1.0\n9   2013-01-10  1.012036  1.012036     1.0\n10  2013-01-11  0.988310  0.988310     1.0\n11  2013-01-12  0.984110  0.984110     1.0\n12  2013-01-13  0.980007  0.980007     1.0\n13  2013-01-14  1.023716  1.023716     1.0\n14  2013-01-15  1.028216  1.028216     1.0\n15  2013-01-16  0.992922  0.992922     1.0\n16  2013-01-17  1.000755  1.000755     1.0\n17  2013-01-18  1.009363  1.009363     1.0\n18  2013-01-19  1.025783  1.025783     1.0\n19  2013-01-20  1.007544  1.007544     1.0\n20  2013-01-21  1.024339  1.024339     1.0\n21  2013-01-22  1.019156  1.019156     1.0\n22  2013-01-23  1.025756  1.025756     1.0\n23  2013-01-24  1.055964  1.055964     1.0\n24  2013-01-25  0.989768  0.989768     1.0\n25  2013-01-26  0.997766  0.997766     1.0\n26  2013-01-27  1.011640  1.011640     1.0\n27  2013-01-28  1.035257  1.035257     1.0\n28  2013-01-29  1.045862  1.045862     1.0\n29  2013-01-30  1.027431  1.027431     1.0",
-      "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>cutoff_date</th>\n      <th>MAE</th>\n      <th>MBE</th>\n      <th>weight</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>2013-01-01</td>\n      <td>0.961201</td>\n      <td>0.961201</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>2013-01-02</td>\n      <td>0.942087</td>\n      <td>0.941597</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>2013-01-03</td>\n      <td>0.943087</td>\n      <td>0.942621</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>2013-01-04</td>\n      <td>0.999754</td>\n      <td>0.999754</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>2013-01-05</td>\n      <td>0.985921</td>\n      <td>0.976416</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>2013-01-06</td>\n      <td>1.019952</td>\n      <td>1.019952</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>2013-01-07</td>\n      <td>1.054776</td>\n      <td>1.054776</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>2013-01-08</td>\n      <td>1.025911</td>\n      <td>1.023660</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td>2013-01-09</td>\n      <td>1.053617</td>\n      <td>1.053617</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>2013-01-10</td>\n      <td>1.012036</td>\n      <td>1.012036</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>10</th>\n      <td>2013-01-11</td>\n      <td>0.988310</td>\n      <td>0.988310</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>11</th>\n      <td>2013-01-12</td>\n      <td>0.984110</td>\n      <td>0.984110</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>12</th>\n      <td>2013-01-13</td>\n      <td>0.980007</td>\n      <td>0.980007</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>13</th>\n      <td>2013-01-14</td>\n      <td>1.023716</td>\n      <td>1.023716</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>14</th>\n      <td>2013-01-15</td>\n      <td>1.028216</td>\n      <td>1.028216</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>15</th>\n      <td>2013-01-16</td>\n      <td>0.992922</td>\n      <td>0.992922</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>16</th>\n      <td>2013-01-17</td>\n      <td>1.000755</td>\n      <td>1.000755</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>17</th>\n      <td>2013-01-18</td>\n      <td>1.009363</td>\n      <td>1.009363</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>18</th>\n      <td>2013-01-19</td>\n      <td>1.025783</td>\n      <td>1.025783</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>19</th>\n      <td>2013-01-20</td>\n      <td>1.007544</td>\n      <td>1.007544</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>20</th>\n      <td>2013-01-21</td>\n      <td>1.024339</td>\n      <td>1.024339</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>21</th>\n      <td>2013-01-22</td>\n      <td>1.019156</td>\n      <td>1.019156</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>22</th>\n      <td>2013-01-23</td>\n      <td>1.025756</td>\n      <td>1.025756</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>23</th>\n      <td>2013-01-24</td>\n      <td>1.055964</td>\n      <td>1.055964</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>24</th>\n      <td>2013-01-25</td>\n      <td>0.989768</td>\n      <td>0.989768</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>25</th>\n      <td>2013-01-26</td>\n      <td>0.997766</td>\n      <td>0.997766</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>26</th>\n      <td>2013-01-27</td>\n      <td>1.011640</td>\n      <td>1.011640</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>27</th>\n      <td>2013-01-28</td>\n      <td>1.035257</td>\n      <td>1.035257</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>28</th>\n      <td>2013-01-29</td>\n      <td>1.045862</td>\n      <td>1.045862</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>29</th>\n      <td>2013-01-30</td>\n      <td>1.027431</td>\n      <td>1.027431</td>\n      <td>1.0</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
-     },
-     "execution_count": 22,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "execution_count": null,
+   "outputs": [],
    "source": [
     "metrics = evaluate_metrics(\n",
     "    crossval_df,\n",
@@ -614,40 +346,14 @@
     "metrics"
    ],
    "metadata": {
-    "collapsed": false,
-    "ExecuteTime": {
-     "end_time": "2023-11-24T12:10:31.460358Z",
-     "start_time": "2023-11-24T12:10:30.526285Z"
-    }
+    "collapsed": false
    }
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2023-11-24T12:10:32.644331Z",
-     "start_time": "2023-11-24T12:10:31.191623Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": "<Axes: >"
-     },
-     "execution_count": 23,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "text/plain": "<Figure size 640x480 with 1 Axes>",
-      "image/png": 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"
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "metrics[[\"MAE\", \"MBE\"]].plot()"
    ]
@@ -661,16 +367,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2023-11-24T12:10:42.457574Z",
-     "start_time": "2023-11-24T12:10:32.205599Z"
-    }
-   },
+   "execution_count": null,
+   "metadata": {},
    "outputs": [],
    "source": [
-    "from enfobench.dataset.utils import create_perfect_forecasts_from_covariates\n",
+    "from enfobench.datasets.utils import create_perfect_forecasts_from_covariates\n",
     "\n",
     "perfect_forecasts = create_perfect_forecasts_from_covariates(\n",
     "    past_covariates,\n",
@@ -681,107 +382,43 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "<class 'pandas.core.frame.DataFrame'>\n",
-      "RangeIndex: 275856 entries, 0 to 275855\n",
-      "Data columns (total 12 columns):\n",
-      " #   Column           Non-Null Count   Dtype         \n",
-      "---  ------           --------------   -----         \n",
-      " 0   timestamp        275856 non-null  datetime64[ns]\n",
-      " 1   cutoff_date      275856 non-null  datetime64[ns]\n",
-      " 2   temperature      275856 non-null  float32       \n",
-      " 3   dew_point        275856 non-null  float32       \n",
-      " 4   pressure         275856 non-null  float32       \n",
-      " 5   wind_speed       275856 non-null  float32       \n",
-      " 6   wind_gust        275856 non-null  float32       \n",
-      " 7   wind_bearing     275856 non-null  float32       \n",
-      " 8   precipitation    275856 non-null  float32       \n",
-      " 9   snow             275856 non-null  float32       \n",
-      " 10  cloud_cover      275856 non-null  float32       \n",
-      " 11  solar_radiation  275856 non-null  float32       \n",
-      "dtypes: datetime64[ns](2), float32(10)\n",
-      "memory usage: 14.7 MB\n"
-     ]
-    }
-   ],
+   "execution_count": null,
+   "outputs": [],
    "source": [
     "perfect_forecasts.info()"
    ],
    "metadata": {
-    "collapsed": false,
-    "ExecuteTime": {
-     "end_time": "2023-11-24T12:10:42.624388Z",
-     "start_time": "2023-11-24T12:10:42.436428Z"
-    }
+    "collapsed": false
    }
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
-   "outputs": [
-    {
-     "data": {
-      "text/plain": "            timestamp         cutoff_date  temperature  dew_point  \\\n0 2011-11-23 12:00:00 2011-11-23 11:00:00     8.515167   6.053314   \n1 2011-11-23 13:00:00 2011-11-23 11:00:00     9.366150   6.898102   \n2 2011-11-23 14:00:00 2011-11-23 11:00:00     9.818268   7.336670   \n3 2011-11-23 15:00:00 2011-11-23 11:00:00     9.655426   7.545349   \n4 2011-11-23 16:00:00 2011-11-23 11:00:00     9.179779   7.419464   \n\n      pressure  wind_speed  wind_gust  wind_bearing  precipitation  snow  \\\n0  1018.570374    3.163105   7.092710    227.930420  -2.328306e-07   0.0   \n1  1018.497498    3.694861   7.186031    235.672974  -2.328306e-07   0.0   \n2  1018.578125    3.870760   6.684762    234.186523  -2.328306e-07   0.0   \n3  1018.738159    3.420094   5.839092    232.220032  -2.328306e-07   0.0   \n4  1018.985535    3.134620   5.201840    221.905396  -2.328306e-07   0.0   \n\n   cloud_cover  solar_radiation  \n0          1.0       247.303711  \n1          1.0       219.641495  \n2          1.0       151.042435  \n3          1.0        68.137222  \n4          1.0        15.961771  ",
-      "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>timestamp</th>\n      <th>cutoff_date</th>\n      <th>temperature</th>\n      <th>dew_point</th>\n      <th>pressure</th>\n      <th>wind_speed</th>\n      <th>wind_gust</th>\n      <th>wind_bearing</th>\n      <th>precipitation</th>\n      <th>snow</th>\n      <th>cloud_cover</th>\n      <th>solar_radiation</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>2011-11-23 12:00:00</td>\n      <td>2011-11-23 11:00:00</td>\n      <td>8.515167</td>\n      <td>6.053314</td>\n      <td>1018.570374</td>\n      <td>3.163105</td>\n      <td>7.092710</td>\n      <td>227.930420</td>\n      <td>-2.328306e-07</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>247.303711</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>2011-11-23 13:00:00</td>\n      <td>2011-11-23 11:00:00</td>\n      <td>9.366150</td>\n      <td>6.898102</td>\n      <td>1018.497498</td>\n      <td>3.694861</td>\n      <td>7.186031</td>\n      <td>235.672974</td>\n      <td>-2.328306e-07</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>219.641495</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>2011-11-23 14:00:00</td>\n      <td>2011-11-23 11:00:00</td>\n      <td>9.818268</td>\n      <td>7.336670</td>\n      <td>1018.578125</td>\n      <td>3.870760</td>\n      <td>6.684762</td>\n      <td>234.186523</td>\n      <td>-2.328306e-07</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>151.042435</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>2011-11-23 15:00:00</td>\n      <td>2011-11-23 11:00:00</td>\n      <td>9.655426</td>\n      <td>7.545349</td>\n      <td>1018.738159</td>\n      <td>3.420094</td>\n      <td>5.839092</td>\n      <td>232.220032</td>\n      <td>-2.328306e-07</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>68.137222</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>2011-11-23 16:00:00</td>\n      <td>2011-11-23 11:00:00</td>\n      <td>9.179779</td>\n      <td>7.419464</td>\n      <td>1018.985535</td>\n      <td>3.134620</td>\n      <td>5.201840</td>\n      <td>221.905396</td>\n      <td>-2.328306e-07</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>15.961771</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
-     },
-     "execution_count": 26,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "execution_count": null,
+   "outputs": [],
    "source": [
     "perfect_forecasts.head()"
    ],
    "metadata": {
-    "collapsed": false,
-    "ExecuteTime": {
-     "end_time": "2023-11-24T12:10:42.786188Z",
-     "start_time": "2023-11-24T12:10:42.567047Z"
-    }
+    "collapsed": false
    }
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2023-11-24T12:10:43.142954Z",
-     "start_time": "2023-11-24T12:10:42.632856Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": "                 timestamp         cutoff_date  temperature  dew_point  \\\n275851 2014-02-27 19:00:00 2014-02-20 23:00:00     5.689819   1.813721   \n275852 2014-02-27 20:00:00 2014-02-20 23:00:00     4.810242   2.094910   \n275853 2014-02-27 21:00:00 2014-02-20 23:00:00     3.816071   2.081482   \n275854 2014-02-27 22:00:00 2014-02-20 23:00:00     3.409973   1.926727   \n275855 2014-02-27 23:00:00 2014-02-20 23:00:00     2.926239   1.651093   \n\n          pressure  wind_speed  wind_gust  wind_bearing  precipitation  \\\n275851  997.900513    3.469654   6.335038    234.681274            0.0   \n275852  997.599365    3.247617   5.910464    234.341431            0.0   \n275853  997.349792    2.962546   5.246741    227.134399            0.0   \n275854  996.695862    2.885335   4.526915    207.307251            0.0   \n275855  995.616455    3.008154   5.425984    196.426453            0.0   \n\n                snow  cloud_cover  solar_radiation  \n275851  2.910383e-08     0.187646              0.0  \n275852  2.910383e-08     0.198816              0.0  \n275853  2.910383e-08     0.053958              0.0  \n275854  2.910383e-08     0.091282              0.0  \n275855  2.910383e-08     0.096165              0.0  ",
-      "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>timestamp</th>\n      <th>cutoff_date</th>\n      <th>temperature</th>\n      <th>dew_point</th>\n      <th>pressure</th>\n      <th>wind_speed</th>\n      <th>wind_gust</th>\n      <th>wind_bearing</th>\n      <th>precipitation</th>\n      <th>snow</th>\n      <th>cloud_cover</th>\n      <th>solar_radiation</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>275851</th>\n      <td>2014-02-27 19:00:00</td>\n      <td>2014-02-20 23:00:00</td>\n      <td>5.689819</td>\n      <td>1.813721</td>\n      <td>997.900513</td>\n      <td>3.469654</td>\n      <td>6.335038</td>\n      <td>234.681274</td>\n      <td>0.0</td>\n      <td>2.910383e-08</td>\n      <td>0.187646</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>275852</th>\n      <td>2014-02-27 20:00:00</td>\n      <td>2014-02-20 23:00:00</td>\n      <td>4.810242</td>\n      <td>2.094910</td>\n      <td>997.599365</td>\n      <td>3.247617</td>\n      <td>5.910464</td>\n      <td>234.341431</td>\n      <td>0.0</td>\n      <td>2.910383e-08</td>\n      <td>0.198816</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>275853</th>\n      <td>2014-02-27 21:00:00</td>\n      <td>2014-02-20 23:00:00</td>\n      <td>3.816071</td>\n      <td>2.081482</td>\n      <td>997.349792</td>\n      <td>2.962546</td>\n      <td>5.246741</td>\n      <td>227.134399</td>\n      <td>0.0</td>\n      <td>2.910383e-08</td>\n      <td>0.053958</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>275854</th>\n      <td>2014-02-27 22:00:00</td>\n      <td>2014-02-20 23:00:00</td>\n      <td>3.409973</td>\n      <td>1.926727</td>\n      <td>996.695862</td>\n      <td>2.885335</td>\n      <td>4.526915</td>\n      <td>207.307251</td>\n      <td>0.0</td>\n      <td>2.910383e-08</td>\n      <td>0.091282</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>275855</th>\n      <td>2014-02-27 23:00:00</td>\n      <td>2014-02-20 23:00:00</td>\n      <td>2.926239</td>\n      <td>1.651093</td>\n      <td>995.616455</td>\n      <td>3.008154</td>\n      <td>5.425984</td>\n      <td>196.426453</td>\n      <td>0.0</td>\n      <td>2.910383e-08</td>\n      <td>0.096165</td>\n      <td>0.0</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
-     },
-     "execution_count": 27,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "perfect_forecasts.tail()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2023-11-24T12:10:43.228974Z",
-     "start_time": "2023-11-24T12:10:42.753025Z"
-    }
-   },
+   "execution_count": null,
+   "metadata": {},
    "outputs": [],
    "source": [
     "full_dataset = Dataset(\n",
-    "    target=target.loc[\"2012-01-01\":\"2013-12-31\"],\n",
+    "    target=target,\n",
     "    past_covariates=past_covariates,\n",
     "    future_covariates=perfect_forecasts,\n",
     ")"
@@ -789,28 +426,15 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2023-11-24T12:10:45.000323Z",
-     "start_time": "2023-11-24T12:10:42.913384Z"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "100%|██████████| 30/30 [00:01<00:00, 22.73it/s]\n"
-     ]
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "crossval_df = cross_validate(\n",
     "    ExampleModel(200),\n",
     "    full_dataset,\n",
-    "    start_date=pd.Timestamp(\"2013-01-01T00:00:00\"),\n",
-    "    end_date=pd.Timestamp(\"2013-02-01T00:00:00\"),\n",
+    "    start_date=pd.Timestamp(\"2022-01-01T10:00:00\"),\n",
+    "    end_date=pd.Timestamp(\"2022-02-01T00:00:00\"),\n",
     "    horizon=pd.Timedelta(\"38 hours\"),\n",
     "    step=pd.Timedelta(\"1 day\"),\n",
     ")"
@@ -818,37 +442,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 33,
-   "outputs": [
-    {
-     "data": {
-      "text/plain": "<Axes: xlabel='timestamp'>"
-     },
-     "execution_count": 33,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "text/plain": "<Figure size 640x480 with 1 Axes>",
-      "image/png": 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"
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "execution_count": null,
+   "outputs": [],
    "source": [
-    "cutoff_date_to_plot = \"2013-01-02\"\n",
-    "crossval_df.loc[crossval_df.cutoff_date == cutoff_date_to_plot].set_index(\n",
-    "    \"timestamp\"\n",
-    ").drop(columns=[\"cutoff_date\"]).plot()"
+    "cutoff_date_to_plot = crossval_df.cutoff_date.unique()[0]\n",
+    "crossval_df.loc[crossval_df.cutoff_date == cutoff_date_to_plot].set_index(\"timestamp\").drop(\n",
+    "    columns=[\"cutoff_date\"]\n",
+    ").plot()"
    ],
    "metadata": {
-    "collapsed": false,
-    "ExecuteTime": {
-     "end_time": "2023-11-24T12:11:09.150428Z",
-     "start_time": "2023-11-24T12:11:07.044798Z"
-    }
+    "collapsed": false
    }
   },
   {
@@ -860,51 +463,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 34,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2023-11-24T12:11:15.434413Z",
-     "start_time": "2023-11-24T12:11:14.589055Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": "          MAE         MBE  weight\n0  200.008643  200.008643     1.0",
-      "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>MAE</th>\n      <th>MBE</th>\n      <th>weight</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>200.008643</td>\n      <td>200.008643</td>\n      <td>1.0</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
-     },
-     "execution_count": 34,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
-    "evaluate_metrics(\n",
-    "    crossval_df, metrics={\"MAE\": mean_absolute_error, \"MBE\": mean_bias_error}\n",
-    ")"
+    "evaluate_metrics(crossval_df, metrics={\"MAE\": mean_absolute_error, \"MBE\": mean_bias_error})"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 35,
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "100%|██████████| 30/30 [00:00<00:00, 51.83it/s]\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": "   cutoff_date         MAE         MBE  weight\n0   2013-01-01  199.961201  199.961201     1.0\n1   2013-01-02  199.941597  199.941597     1.0\n2   2013-01-03  199.942621  199.942621     1.0\n3   2013-01-04  199.999754  199.999754     1.0\n4   2013-01-05  199.976416  199.976416     1.0\n5   2013-01-06  200.019952  200.019952     1.0\n6   2013-01-07  200.054776  200.054776     1.0\n7   2013-01-08  200.023660  200.023660     1.0\n8   2013-01-09  200.053617  200.053617     1.0\n9   2013-01-10  200.012036  200.012036     1.0\n10  2013-01-11  199.988310  199.988310     1.0\n11  2013-01-12  199.984110  199.984110     1.0\n12  2013-01-13  199.980007  199.980007     1.0\n13  2013-01-14  200.023716  200.023716     1.0\n14  2013-01-15  200.028216  200.028216     1.0\n15  2013-01-16  199.992922  199.992922     1.0\n16  2013-01-17  200.000755  200.000755     1.0\n17  2013-01-18  200.009363  200.009363     1.0\n18  2013-01-19  200.025783  200.025783     1.0\n19  2013-01-20  200.007544  200.007544     1.0\n20  2013-01-21  200.024339  200.024339     1.0\n21  2013-01-22  200.019156  200.019156     1.0\n22  2013-01-23  200.025756  200.025756     1.0\n23  2013-01-24  200.055964  200.055964     1.0\n24  2013-01-25  199.989768  199.989768     1.0\n25  2013-01-26  199.997766  199.997766     1.0\n26  2013-01-27  200.011640  200.011640     1.0\n27  2013-01-28  200.035257  200.035257     1.0\n28  2013-01-29  200.045862  200.045862     1.0\n29  2013-01-30  200.027431  200.027431     1.0",
-      "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>cutoff_date</th>\n      <th>MAE</th>\n      <th>MBE</th>\n      <th>weight</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>2013-01-01</td>\n      <td>199.961201</td>\n      <td>199.961201</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>2013-01-02</td>\n      <td>199.941597</td>\n      <td>199.941597</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>2013-01-03</td>\n      <td>199.942621</td>\n      <td>199.942621</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>2013-01-04</td>\n      <td>199.999754</td>\n      <td>199.999754</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>2013-01-05</td>\n      <td>199.976416</td>\n      <td>199.976416</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>2013-01-06</td>\n      <td>200.019952</td>\n      <td>200.019952</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>2013-01-07</td>\n      <td>200.054776</td>\n      <td>200.054776</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>2013-01-08</td>\n      <td>200.023660</td>\n      <td>200.023660</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td>2013-01-09</td>\n      <td>200.053617</td>\n      <td>200.053617</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>2013-01-10</td>\n      <td>200.012036</td>\n      <td>200.012036</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>10</th>\n      <td>2013-01-11</td>\n      <td>199.988310</td>\n      <td>199.988310</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>11</th>\n      <td>2013-01-12</td>\n      <td>199.984110</td>\n      <td>199.984110</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>12</th>\n      <td>2013-01-13</td>\n      <td>199.980007</td>\n      <td>199.980007</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>13</th>\n      <td>2013-01-14</td>\n      <td>200.023716</td>\n      <td>200.023716</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>14</th>\n      <td>2013-01-15</td>\n      <td>200.028216</td>\n      <td>200.028216</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>15</th>\n      <td>2013-01-16</td>\n      <td>199.992922</td>\n      <td>199.992922</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>16</th>\n      <td>2013-01-17</td>\n      <td>200.000755</td>\n      <td>200.000755</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>17</th>\n      <td>2013-01-18</td>\n      <td>200.009363</td>\n      <td>200.009363</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>18</th>\n      <td>2013-01-19</td>\n      <td>200.025783</td>\n      <td>200.025783</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>19</th>\n      <td>2013-01-20</td>\n      <td>200.007544</td>\n      <td>200.007544</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>20</th>\n      <td>2013-01-21</td>\n      <td>200.024339</td>\n      <td>200.024339</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>21</th>\n      <td>2013-01-22</td>\n      <td>200.019156</td>\n      <td>200.019156</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>22</th>\n      <td>2013-01-23</td>\n      <td>200.025756</td>\n      <td>200.025756</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>23</th>\n      <td>2013-01-24</td>\n      <td>200.055964</td>\n      <td>200.055964</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>24</th>\n      <td>2013-01-25</td>\n      <td>199.989768</td>\n      <td>199.989768</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>25</th>\n      <td>2013-01-26</td>\n      <td>199.997766</td>\n      <td>199.997766</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>26</th>\n      <td>2013-01-27</td>\n      <td>200.011640</td>\n      <td>200.011640</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>27</th>\n      <td>2013-01-28</td>\n      <td>200.035257</td>\n      <td>200.035257</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>28</th>\n      <td>2013-01-29</td>\n      <td>200.045862</td>\n      <td>200.045862</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>29</th>\n      <td>2013-01-30</td>\n      <td>200.027431</td>\n      <td>200.027431</td>\n      <td>1.0</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
-     },
-     "execution_count": 35,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "execution_count": null,
+   "outputs": [],
    "source": [
     "metrics = evaluate_metrics(\n",
     "    crossval_df,\n",
@@ -914,40 +483,14 @@
     "metrics"
    ],
    "metadata": {
-    "collapsed": false,
-    "ExecuteTime": {
-     "end_time": "2023-11-24T12:11:16.298683Z",
-     "start_time": "2023-11-24T12:11:15.126460Z"
-    }
+    "collapsed": false
    }
   },
   {
    "cell_type": "code",
-   "execution_count": 36,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2023-11-24T12:11:18.217802Z",
-     "start_time": "2023-11-24T12:11:16.602264Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": "<Axes: >"
-     },
-     "execution_count": 36,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "text/plain": "<Figure size 640x480 with 1 Axes>",
-      "image/png": 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WkjIAMPdIk854I0mOiEmtleovtTpTIZpBvYxbktXUdHfVvpCO7fX0ku9TW8Zzpi2kK0klV33NFSEdVwgxPL/PR4FXVULPnHwuAKZUNZNj9bgiFZaIEElyJpBbbrkFTdMGNdfsd+edd6JpGrfccsugY/s/MjIyWL169aCeVcCgY07+eO6558LxJZ2Rp17dLmpLnvThY4EdVsbm0O6wqjqyB4vmo11PxFk4DZ+9EABTm9TKESLSao8fIknrxaObyJ+iZnKsthwAkvpcEYxMRIIkORNMYWEhzz33HN3d3QOP9fT08Oyzz1JUNLi30urVq6mtraW2tpaNGzdiMpn42Mc+dto1n3zyyYHj+j/WrFkT6i/lrEwu1UbBlzZ14LGE/LkApHWE9rZR81G1s6raUoJmMGDOKAEgubs6pOMKIYbXcETtrDphKsRktgCQ5FDr5lL90qQz3kiDzpHQdfBGoOeJOQk0bVSnLFy4kKNHj/LSSy9xww03APDSSy9RVFRESUnJoGOtVitOp1pP4nQ6ueeee7jwwgtpbGwkKytr4DiHwzFwXLSwd1YAYHFOH3gsc/J82AwFfZX4+vowmkLz8vbW7gXAbZsGQIpT3SZL99Sd8RwhRHj0VKuCoK3JH/4BlJqRC4Bdb0f3+wducYuJT5KckfB2wY/ywj/uN2vAkjzq02677TaefPLJgSTn17/+NbfeeiubNm064zkdHR389re/ZerUqWRkZIw14rDQ/X6cfarCcFrROQOP5xXPokc3k6B5qao4SMHUOSEZP6k10M4hW42dma+SnSy9Ca+nF7PFGpJxhRDDswRuV/dlzhp4zJ6hbleZNR9udwv2tMyIxCbCT9LZCejGG2/knXfe4fjx4xw/fpx3332XG2+88bTjXn31VVJSUkhJSSE1NZU///nPPP/88xhO+Svn+uuvHziu/6OyMoyNME/R3FCNjS78ukZuyeyBx40mE1UmdUuu8djukI2f061ulaVOmg9AhrOQHt2MUdNpqJJu5EJEUmaXul2dWDhv4DFrQhIdeiIAbc1SnTyeyEzOSJiT1KxKJMYdg6ysLK666iqeeuopdF3nqquuIjPz9L9cLr30Un7xi18A0Nrayv/7f/+PK664gm3btjFp0ocLeh988EFWrlw56Ny8vAjMbAU0lO8lE6gzZJOXOHimqzVlCriP0lOzNyRju1ubcNIIQP70RQBoBgMNxmyK/NW0Vh8if/Kss11CCBEivT1d5PuqQQPntIWDnmsz2EjRu+lsrQfmRiZAEXaS5IyEpo3ptlEk3Xbbbaxbtw6ARx99dMhjkpOTmTr1w/vWv/rVr7Db7fzv//4v//mf/znwuNPpHHRcpHVUq8acTdYiTk21fBkzwf0PLM1lp58YBDWHdmAH6skgJ/3DdUsuSx5FPdV0NchMjhCRUnV4D1M0P20kk503eA1ih9EOffX0uKR/VTyR21UT1OrVq/F4PHi9XlatWjWiczRNw2AwDNqZFY38jYcA6LKVnPZcYoFaJ5PeGZodVm3HVTuHuoTJgx7vTs4HwNdyPCTjCiGG11quCoFWmUtOW1zcbXYA4G1vDHdYIoJkJmeCMhqNHDhwYODzofT29lJXp3YEtba28sgjj9DR0cHHP/7xQce5XK6B4/qlpqaSnByZ2a2ENtUjSsucdtpz2VPOhbch31cVmkXA9arQYFfa4FtSur0ImsHcLrVyhIiU/p2P7bbTZ549lnTohr4O6V8VT2QmZwKz2WzYbLYzPv/aa6+Rm5tLbm4uy5Yt47333uOFF17gkksuGXTcrbfeOnBc/8fPf/7zEEd/ZhmBxpzJeTNPe85ZOI1OPQGL5qPmaPDX5djaVLl4c+45gx43Z6qZnZTuCKzdEkIAJ+18zDnntOd8CapJp9YlrR3iiczkTCBPPfXUWZ9/+eWXBx073PEAuq6PL6gg8/T2kOuvAw2yik//RaYZDFSZJzGjr4ym8j1MmrUoaGPrfj/5XjWLlB4oF98v1amSnAyv7NwQIlKcPWpNnC2w8/FkerJaQ2fskSadw/H7fOz42fXompElX34mpusKxW7kIi7VVhzEpPnp0q2nLSzs505Rxfk8tcGdyamvOoqNLry6kYJpg3+JZhWqooRZtNLT3RnUcYUQw3O3NJKDmqXJm376HzfGZFX/y9IrSc5wdv39KZa4/85S1185tm9bpMMZF0lyRExpOa4SlxpTwRn/uvBnqfUy1pZDQR27PlAuvspYgMWaMOg5R0YOXbpa/9Nw4nBQxxVCDK/msGq3UkfmkMX+zIEmnQleVzjDijl+n4+M7Q8N/H/Dzj9HLpggkCRHxJTeepW4uJMmnfGY5AJV6TizO7g7rLpOqHLxzcmnL2rUDAbqjar1hav6SFDHFUIMr3/nY33i5CGfT3SoqscpPle4QopJu/7+NMX+D4u92qveimA04ydJjogphhaVQHgcU854TH8RsDxfbVBvHZkD5eK9macveAZwJ6iqPd2N5UEbUwgxQv07Hx1D/3wmp2UDYPO3hS2kWKNmcR4A4D3HFQBM9+zH3Rq7O9IkyTmDaFtwGytC/X1L7agAwJwz/YzHZDqLaCMZk+an+sgHQRs7o0PdhkosmDfk872BWjn+1oqgjSmEGJkPdz7OHvr5DPVHSKrWTW9PBBoux4D+WZw2kph+8yMcNxRi0vwcKX0l0qGNmSQ5pzCbzQB0dckPwVh4PB7gzLV5xivHq+rQOAqH/kUG6tZRtbkYgNaK94Myrqe3hwKfagrqnDb0ji3doW6hWdqrgjKmEGJkTt75mFZy7pDHpNrT6dPVW15bi1Q9PtXJszj7Cm/AnpZJbdYF6rlDf49kaOMiW8hPYTQacTgcNDSoH4KkpCQ0TYtwVLHB7/fT2NhIUlISJlPwX1rulkbSUVPNuZPP3mG8zTYNmvfhrd0XlLGrj7xPieajjSRyCoa+VWbJLIHDkNojtXKECKf66mM46aJPN1AwbeiZVoPRSItmIxMXbc11ZOUVhzfIKLf7H0+zMDCLM/uaewBImXMF1P+OElcpfp8PQ4j+eA0lSXKG4HSqBaT9iY4YOYPBQFFRUUgSw9pje7ADDaSTneo4+8FZM6EZEl3B2WHVfGw3JUC1uZhZZ9jVZc9VyU9mX92QzwshQqP+8E6cQJUxn+KEMzc27jDYyPS76HbVhy+4GOD3+Uh770EA9hV+lhWB3WnTlnyEztcTyNRcHNlbytT5F0QyzDGRJGcImqaRm5tLdnY2Xq830uHEFIvFgiFEhaPaq1SbigZrEdnDHJtSOA8OQlZ3cBpmegNdzdtsZ1kLFKiVk04bne0ukodLxIQQQdFd9eHOx+KzHNdpcoCnkp42+QP2ZGoW5zjteiKzP3nPwOPWhCT2Jy/k3K7NNO56VZKcicZoNIZsbYkYvb4GNSvTmVI87LG50xbABsjX6+nqcJOUYh/X2EkutbOK7DOvBbKnZdJGMjY6aThxmJLZS8Y1phBiZExN6g8gT8bQO6v69Voc4AFfe+zuFgq2k2dx9hbdwIr0rEHPe0pWwr7NpFXH5lZyWXgsYobVrWZl9IzT69ScKj07n2ZUYlN9ePyLj3MCM0K24gVnPa4hUCvHXRuaLuhCiNOldarSEon5Q6/H6ee1pgPg75Qkp9/uDb+hZIhZnH6Tll8NwDTPAdzNsXebT5IcETPSu48DkJh79r/W+tVZioHx77BytzbhRP1SHKpc/MnaE3IB6GkIzm0yIcTZeT29FPapXZdZU4beWdVPT1StHQzd0toBArM42z6cxbGfMosD4CycSrlhEkZN5/CW2Kt+LEmOiAm+vj5yfar5Zeaks++s6tdhnwaAv/7AuMauOaTaOZypXPzJelMK1SeuyrMeJ4QIjpqje7FofXTpVnInnXnNHADJ6ufX3COdyKF/FqfijLM4/eqz1Voc/dCGcIUWNGFJch599FGKi4tJSEhg2bJlbNt29oZfL7zwAjNnziQhIYG5c+fy17/+9YzHfuELX0DTNB566KEgRy2iSV3lYayal17djLNo2shOylY9rJLGucOqrWI3cOZy8YM4igCwdpwY15hCiJFpOrYLgBPm4mG3OJsC/ausHleow4p6J8/i7Cv87JCzOP1S514FwBS32koeS0Ke5Dz//PPcdddd3HfffezcuZP58+ezatWqM27P3rx5M9dffz233347u3btYs2aNaxZs4a9e0/vKP3HP/6RLVu2kJeXF+ovQ0RY83FV76bWmItxhDV47EXq/nx2zzjbLDTsB6ArbfjbZAnZKhGySa0cIcLCE9j56E4d/o8fq031r0rqc4c0pljw/uu/HZjFmfXJb5z12OlLVtKhJ5JOG0f3vBumCIMj5EnOAw88wB133MGtt97K7Nmzeeyxx0hKSuLXv/71kMc//PDDrF69mrvvvptZs2bxwx/+kIULF/LII48MOq66upovfelLPPPMMwNVisXE1VWrdje1JJ65MeepcqerHlZOmmhzjX162tamZoLMucPfJnPkqUXRWb7YW6AnRCxKaC0DwH+WnY/9khxqtiLV7wplSFHP7/Nh39pf3fiz2DNyznq82WLlUMpiAJp2vRry+IIppEmOx+Nhx44drFy58sMBDQZWrlxJaWnpkOeUlpYOOh5g1apVg473+/187nOf4+677+acc84ZNo7e3l7a2toGfYjYojWrvjS99hHcMgqwp2XSgNpNUXNo55jG1f1+CjxqJihj8oJhj88qUEmOjc6YbmonRKzI7lY7GVOKzr6zCiAlXb2Z2/V2dL8/pHFFs/df/y2TRziL069v8uUApNfE1lbykCY5TU1N+Hw+cnIGZ4k5OTnU1Q1dFbaurm7Y4//nf/4Hk8nEl7/85RHFsX79eux2+8BHYWHhKL8SEWnJgcacpuxhFhaeoi5BJUVtlWNr1Fl34jCpWjde3Uj+1PnDHp+c6qAFGwBNJ4JTbVkIMbTOdhf5upo1zZu2cNjj7RmqxINZ89Hmjs8dVifP4uwtvH7YWZx+xcvXADDNW0ZrY22owgu6mNtdtWPHDh5++GGeeuqpEbcOuPfee3G73QMfJ07IotBYk92rdiulFswa1XldAzus9o9p3PojalFjlbEAizVhROc0maRWjhDhUFWmZmibcJCenT/s8QmJyXTq6ue4vTk+26/sfv1ZJvsr6BhmR9WpsvNLOGoswaDpHI2hreQhTXIyMzMxGo3U1w9en1BfXz/QH+pUTqfzrMf/85//pKGhgaKiIkwmEyaTiePHj/P1r3+d4uLiIa9ptVqx2WyDPkTs6Gx3kY36qyu3ZGTbx/sZnOo+fUrb4TGN3V21B4DmlBHu6ALaE9RCeE/TOBc8CyHOyn1c1cCqtZaM/ByDKhLa0Rp/SY7f58O+7X4APhjFLE6/hpwL1SeH/xHs0EImpEmOxWJh0aJFbNy4ceAxv9/Pxo0bWbFixZDnrFixYtDxABs2bBg4/nOf+xx79uxh9+7dAx95eXncfffd/P3vsdsOXpxZ7TG1e6IV26h/KB2T1H16Z2/FmMY2N6kFz95hysWfzJOqtpFrruNjGlMIMTL+OrXrstMxY8TndBhVktPjbgxJTNFs9+vPMsVXPupZnH72eYGt5G3b8PX1BTu8kAh576q77rqLm2++mcWLF7N06VIeeughOjs7ufXWWwG46aabyM/PZ/369QB85Stf4eKLL+b+++/nqquu4rnnnmP79u08/vjjAGRkZJCRkTFoDLPZjNPpZMaMkb/QRexwnVC3murMBaSN8tz8aQsAyMRFa2MtaVm5ozo/I1AuPqlw7ojPMaQVQS1YO6pGNZYQYnRS3Wrdm8E5/AaUft1mB/SBN86adJ46i7NilH8wAkxfdBltryWRRhtlu99mxuLLgh1m0IV8Tc51113HT3/6U7773e+yYMECdu/ezWuvvTawuLiyspLa2g8XMZ133nk8++yzPP7448yfP58XX3yRl19+mTlzRnebQkwc3nr1i6w9uXjU5yanOqjRVM/ymsO7RnWup7eHAp9KVHKmnr2dw8kSA7Vy7L2xszhPiFiUG9j5mDZMT7mTeSzqTyVfR3ztfnx/4+/GNYsDYDJbOJKiGg+3vH/mIr3RJCxdyNetW8e6deuGfG7Tpk2nPXbttddy7bXXjvj6FRUVY4xMxAKLSy3g9acP35hzKA0Jk8nrbqDjxAfAlSM+r+rw+0zWfLSRRE7BlBGf58hTx+b46tD9fjRDzK3vFyLqNdWdIJM2/LpGwfSz96w6mS8hHdxAV/y0dtD9fmxbA7M4BZ8Z0yxOv74pK+H9t8iojY2t5PLbV0Q9e5da22J1ju12ZLcjsO28YXQ7rFoC5eKrzSWjSlSyC9Ui5SStF1cMdu0VIhbUBmpf1RicJCanjvg8PUktdzD2xM8WcrUW5xgdeiKzxjiL02/yMtWVfKr3MM310X9LXpIcEdV0v5+8PvWDlD7CxpynMueqHVap7aPb0u2tVYsa22wj31kFaptqY2D1UKPUyhEiJDpPqJ1VjUkjn2UFMKaoqseW3vhIck6dxXFkDr2zeaQy8yZxxDgFg6ZzLAa2kkuSI6JaY+1xkrRe+nQDucUj3+F0sv4dVnme8lFVOU1yqZ1V5Ix8UWO/ZrNa4Nxed2zU5wohhmdoPABAT/rofi+YA006E7yuYIcUlfpncTr1hHHP4vRrdF4EgHbk9aBcL5QkyRFRraFcVSquNThHXIzvVAXT5uPTNRx00Fw38kKQzkC5ePukBaMesyNR1crxNkmSI0QopHWo2lfWvNHN8CY61HqUFJ8r2CFFnZNncfYEYRanX9o8tbZxavvWqN9KLkmOiGqdNWo2pTmhaMzXSEhKocagZlZqj+we0TnulkZyUAsT82aMfGdVP2+qah2iuStHfa4Q4ux8fX0UeNVavcwpI190DJCcpnZb2vwTv4fh4Fmce4N23akLL8FNMg46OLxrU9CuGwqS5Iiopjepv9Z6bCOvaDqUpiS1rbszUMF4ONWHdgBQRxY2R8YwR5/OmF4MQGJn9ajPFUKcXW3FQRI1Dz26mfzJo5vJsaWr2YxUrZvenq5QhBcVdL+f1ECPqmDO4kBgK3nqUgBa3/9L0K4bCpLkiKiW1KbqYGiZo1v8e6reNLUzy9B4cETHtwfKxdcnjrzr+cmSslVS5vDUjOl8IcSZNRwN9JQzFWE0ja4SSqojkz5dvfW1tUzcgoDvb3yOqb6jQZ/F6eef8hEAMmvfDvq1g0mSHBHVMgONOVPyR9eY81T9O6zs7UdGdkKD2lnVlTa2betp+WrberavYVSLnYUQw+utVmv1WlNGXzvLYDTi1tSW8/aWiVviwbfn9wB8kLs2qLM4/UqWfwKAab4jNNVF7215SXJE1Orp7sTpV39p5YxySvpUGZMXAJDvPT6ipMMeaOhpzht5O4eTZRdMxqdrJGjeUS12FkIMz9qidlb5ssb2x097oEln1wRu0pnfrhLBlLlXhOT6mc5CDpvUDHt5FG8llyRHRK3a8v0YNJ12PZGM7IJxXStv8hy8upEUrZv6qrPXy9H9fvID5eIzJo9uUWM/s8VKo6a2qjZVj60DuhBiaJld6mc4qWDemM7vNKkkp7dtYjbprK86ipMmfLpGybwLQzZOc2ArueHIhpCNMV6S5Iio1VqpbhnVmgvH3RrBYk2g2pgPQP3R3Wc9tu7EYVK1bjy6kYKpY/slCtBsUVPEHXWjK0IohDiznq4O8n1qrVvu9NHvfAToDfSv6mufmElO1R61TqbcNJnkVEfIxnEsUF3Jp3W8R5/XE7JxxkOSHBG1PHWqWnBb0qSgXK85sMOqu+qDsx5XfyTQzsFYiNliHfN4XYkqqfI2l4/5GkKIwaoO78ao6bhIIdM5ttISXms6AP7Oidm/ynt8CwDNafNDOs60BRfTSio2Ojm8882QjjVWkuSIqGVsVYuEvWmjK9t+Jp4MVRnV2FR21uN6Tqht5s1jWNR4sj67+gVsdMuaHCGCpbVc7Xystoyup9zJ9ERVFsLQPTGTnLTm3QAYi5aFdByjycRRmxrDtSc6u5JLkiOilq1TFfuy5IytncOpEvLUDitHx9l3WJmb1aJGb+bscY1nSlczUEld0d/ETohY4atTt7E7bNPHfpFkleSYJmCTzp7uTkq8ah1g7pyLQz/g1JUAZNdF51ZySXJEVNL9fpx9agYkrWh8yUa/zMAOq4K+Svw+3xmPy+hUSVBSwdh2VvVLzlEzUA7PxN3BIUS4JbkCM7E5Y/+9YEpVTToTPK3BCCmqVHywGYvmoxk7ecVjK4ExGpOXfwK/rjHFd4zGmoqQjzdakuSIqNTaVIudTvy6Rt7k0TfIHEpeyTn06mYSNQ+1x4cuCujp7SHfp6oU50xbOK7x0gvU9spsf2PU93cRIlY4e1Q/OPuksa83sdpUkpPU5w5KTNHEdehdACqT5ox7w8ZIpGfnc8SsftdVbPlTyMcbLUlyRFSqL9+r/qtlkpCUEpRrGk0mqkyqp1RDYHHxqaoOv49Z89FGEjn5Y6t23C8rtxiPbsSi+WisrRjXtYQQ4G6uJxt1iyl/DD3l+iUFmnSm+l3BCCuqWGq3A9DrHPv3Z7Sa8y4BwHgs+rqSS5IjolJ7lVoX02QtDOp1W5PVLaTemv1DPt9ybCcAVebJ4/4ryGgy0WBQfzG2VEmtHCHGq6pM9ZSrJYtUe/qYr5OSrpIcu94+oSqS634/RZ1q96ht+vlhGzejfyt5+3t4Pb1hG3ckJMkRUcnfqLaPd6WOrzHnqbwZ6h61qXno21XeWjWD1G4fx6LGk7gCtXI666VWjhDj1RHY+VifOL4dl/YM9XNp1ny0uSfO4uPaysNk4sKrG5k874KwjTt1/oW0YCNV6+bwjjfCNu5ISJIjopI10JiTcTbmPFViYDFxeufQSUdSa2BRY3ZwFjt3JalKzX0tx4NyPSHiWoOage0eY0+5fgmJyXTqCQC0N0+cjQE1e98CoNw8JWi3+UfCYDRyLLCV3B1lW8klyRFRKaNHJQVJecHZPt4vu3+Hla9qyAqdwVjUeDK/XW0jN7mjt4GdELFioKdc7vg3I7gNNgA6JlD/Kt/xrQC0hLgI4JCmfxQAZ310bSWXJEdEHa+nl1yf+sWTVTy+xpynchZNo0u3YtH6qD42eF2Ou6WRHFRxsLxxLGo8mSkjUCunuzoo1xMiXg3uKbdg3NfrMDoA6HFPnNYO6S27ATAXLw/72FOXf0L1yvJXDNsfMJwkyRFRp+74Qcyaj27dQvY4dzidymA0UmVWiUfzscE7rKrL1K6EWrKwOTKCMl6KU60dyJBaOUKMS39POa9uJH/q+GcqugNNOj0TpH9Vd2c7xX0qCcyfG4YigKdwZDo5bFYz78ejaCu5JDki6jQfVzMsNaZ8DEZj0K/vCrRr6K3ZN+jx9kpVLn68ixpPlpmv1hRl6U1Rt+tAiFjS31OuyliAxZow7ut5+vtXTZAkp3zPO5g1Hw2kk1MQvN9ho9Gar5Irc/nGiIw/FElyRNTpqVM7n1yJwWnMeSp/plq0aG09pYdVkBY1nizDWUiPbsao6TRUHQvadYWIN91VgZ5yyePrKdfPlxDYgt41MfpXuQ+9A0BVSniKAA4l89yPATC9Yzue3p6IxHAqSXJE1DG0qPu5Xkdo/hrpb9eQ0TU46RhY1JgXvHVAmsFAgzEbgNbqQ0G7rhDxxtys/ijxZgZnM4KepG5JGydI/6qEelXjy5O7OGIxTJl7Hs3YSdZ6OLR9Q8TiOJkkOSLqpHRUAGDKDk6tmlM5A+0a8n019PZ0AWpRY0FgUWPmlPG1cziVy5IHQFdDeVCvG8/O1ntMTEwZgca6iQXzgnI9Y0omAObe2O9fpfv9TOpSNb4cM8JXH+dUBqORY/YVAHR88LeIxXEySXJE1Mn2qMactoJZIbl+Vu4k2kjCpPmpPqKqg9ZWHiZF68ajG8mfMr7GnKfqTs4HwNdSEdTrxqPuzna2P7CWjh8WUL7/vUiHI8LE6+kl36d+L2RPOTco1zQHmnQmemM/yak+tp902vDoJkrmrIhoLIbpHwHA2fDPiMbRT5IcEVXaXM1k4gIgN8jJRj/NYKDaXAxAS4VabNxwJNDOwViI2WIN6ni6vQgAc/uJoF433jTVVXLiwctY3PY6Nrqo3/mXSIckwqTqyB4smo8OPZHcouAUCE20q9vIyb7Yb9JZu0/VpjlmnoY1ISmisUxdcTU+XaPYX0ldZeTb2UiSI6JK3TE1s9KEY1y9aYbTlqoWL3pr1Q6rnio1bktKcBY1nsycqbbBp3TXBP3a8eLY3q30PXYZ0/s+XNektURPLQ4RWv3lHqrMxUFbVJvc37/KH/tJjr9SFQF0ZSyIbCCAPT2LQxY1C398a+S3kkuSI6KK+4Ta4VRvCW5jzlPpWeqHMKFVvWmam9W4fZnBaedwslSnSnIyvLVBv3Y8eP+N35Pzwidw0sgJLY/SwjsASOmQNU7xwluj1pu4bcFr82JLV/2rUrXuqNkJNFaZLjUjbSkJfxHAobjzLwXAEgVbySXJEVGlr0ElHR0pwW3MeaqUQrWDKqtb7bDK6AwsaiwMzqLGk2UVqgXUWbTS090Z9OtPZFt+9yPmvPV5krUe9lnmY1u3iYxzPw5AtqcqwtGJcEkMck85gFRHJn26egt0x3D/qo62Vor7KgAonHdJRGPpl3/B9Ww95zvkfuZnkQ5FkhwRXaxudQtCzwj+baOT5QZ2WOX563G3NlHgU20X+ndeBZMjI2egGWDDicjfo44FfV4PWx+5jeVl/4NR09nmuJJpX/8H9owcnJMDCSqtdLTF/qJRMbzsQE+5lKLg9WQyGI24tVQA2lvqg3bdcKt4/22Mmk4tWWTlFUc6HAAKp85l2bX/jjNI66fGQ5IcEVXSulQjy8Tc4BXkG0pGTgGt2DBoOofefAaT5qeNZLLzgj+DpGrlqPv/rhpZRzKcdncL++6/kmVNfwCgdPKXWfLlZwaq3NocGTThAKD22N5IhSnCpKOtlTy9AYD86cH9I6TdoFo7dMVwk872I5sBqEkNzUaNWCdJjogafp+P3MCMSkZRcBtzDqXGUgxAQtkfAaiyTA5ZpVB3gqqV090gVY/Ppq7yME0PX8r8nvfo1i3sXP4wK2764Wn/Lg3mAgDcVQciEWZEHNq5icM/XMT+La9FOpSwqtyvFtU2koYj0xnUa3cG+lf1tsVua4ekBrUz1JsXuSKA0UySHBE16k4cIUHz4tGNOCeFphDgyTps6pbY7J7dALQHcVHjqXoDtXL8rZUhGyPWHdq5CdOvV1Lir6AJB1Vr/sDC1bcMeWxHimr54a2PnyrSna//D9N8R/C+9UCkQwmb7s52rBvuBeBEcvBnKnrNaQD0xWj/Kr/PR3G32iGaMfPCCEcTnSTJEVGj+bj6Ya015mEyW0I/YGARo1HT1f/nnBOyoXSHelO2tEuSM5Sdf3uSoj99ikxcHDMU03f7Rqade9EZj/enqwTV7IqPmbGOtlZmd6rih9O7dtHT1RHhiEJP9/vZ99jNTPEdowUbeZ95MOhjeAP9q/ydsdm/6sSRD7DTSbduoficZZEOJypJkiOiRmeNaszZklAUlvFskwbvpLJPCt6ixlNZMtVan9QeqZVzMt3vp/T/vsnCrV8lQfPyfuIysr+6CWfh2ReeW3PUTJ+963g4woy4sndewqp5AUjUPJRtjY6S+aG09dkfsLh9I17dSN2qx4d9TYyFP1ElOYbu2Exy6verIoDl1hlBL2I6UUiSI6KG1qx2HvXYQ7t9vF/eKTup8mcsCtlY9lzVbDSzL3YXOAabp7eH7T/7LCvKHwVgS/anmfP1v5JiSxv23PQiNQuX11eF7veHNM6osP/PAHh0IwA9+8Kb5Lib69ny6L9Qtv2NsIz3wdt/YsnhhwDYOetuZq+4IiTjaMmqf5UpVpt0BooAujOC0+piIpIkR0SNpHZV3M2UFfr1OKAqczag/pKr0bJDWmE5M1ArJ502OttdIRsnVrib6zl8/0qWuP6GT9fYOutelv/b/2I0mUZ0fm7JbHy6RrLWQ3PdxG6X0dPdyaz2UgB2FN4MQGHzO2FN7va/tJ7ljS9Q9Mp17P1naKvY1pQfpPCNf8Oo6bznuIKln/5GyMYyBZp0Wr2ukI0RStnuPQAklMitqjORJEdEjaxe9WaVmh+axpxDqbcWA9CQOCWk49jTMmkjGYDGqiMhHSvadXW4cT96Ked4PqBDT2TfJb9i2XX3jOoaFmsCtQa1Lb++fGJvIz/4zp9I0nqpI5O5n/4uHt1Enl5P5eE9YYsht/Z1QN0qm/r67ezZ9IeQjNPV4abnN9fhoINDpunM/dcnQrbjEcDa378qBpMcd2sTxX61xq9o/iWRDSaKSZIjokJXhxsnTQDkTA5fvYeObHWLqjsndLeq+jUY1fZXV018Jzllm1+hyF9NCzYaP/0n5l36qTFdpzmwdqsjsJZrovLuVTMnFdmXkWJLoyxBrSWr3R6evkAnDr9Psf8EXt3InoQlJGheZr75ed5/4/dBHUf3+znwy5uZ7K+gGTv2W54jITE5qGOcqr9JZ0oM9q86/v5bAFRpTjJyCiIcTfSSJEdEhdpjameVixTSsnLDNu6513+PnSseYdFnvhPysdoT1NfV2xjfPZd6m9TXX5GygJJx7AjpTlVrt/SmiVtF2uvpZbr7nwDYFq4FoHPSZQCkVIZnfUx16YsAHEyYz8yvvcqu5AuwaH3MeusL7PrHb4M2ztZnvsei9jfx6kbqVz9OTkFoZ1cBUjPUHx52vS3m1nZ1HlW3MOukCOBZSZIjooIr0JizzhTaxpynSkhKYeGqzw1U0w2l3hT1temt8bEj6Ixc6rakJ1A7aKy0TLXbJqFt4iaNB7f8FTudNGNnxuKVAOQvXQPA9J4PwtLWIq3yHwB0TbkCizWBOV95iZ0pF2PRfMx598vsfO2pcY/xwVsvseSI6nO0c/Y3mL189bivORL2QJJj0Xy0x1iLkJSGHQD4CpZGOJLoJkmOiAqeQFG39pTiyAYSQlqaqpVj7ZjYC2WHY+1UVa1xjK9UQHLeTAAyeiZu7aGu91U17iMZlwwsyi6cOpcqLReL5uNQ6ashHb+p5jgz+tTtwMkXfBoAs8XKvK+8yHbbSsyaj3mlX2PHX3415jGqj+2j6M11qkdZ2lUsvfbuoMQ+EgmJyQN95dqaasM27nj5fT6Ke1S178yZF0Q4mugmSY6ICmaX6unUlxb6KepIsWb118qJnV+modD/9SdkFo/rOlnFqnij01+P19M73rCijq+vjynNat1F0rw1g56rylRvbH0HQ9vi4eg7at1NmWnGoOaPJrOFc7/8PO/ZV2HS/CzY9u9s//MvRn39rg43nt9+FjudlJlmMP9ffxXShcZDcRtsAHS4YqdJ5/GynaRq3XTpVibNknYOZyNJjogK9s4KAKzOmZENJIQceer2SrYvvmvlZPhUs8VU5+RxXScrt5gu3YpZ81F3fOItPi7b/jqZuGgjmZkrrhr0XPKcKwEobt0c0rUkicdUPZ6WolWnPWc0mVj4pWfZlnYVRk1n4Y572fbHn4/42rrfz8HHPjfQxiPt1uexJiQFLfaR6jA6AOhxNYR97LFq2KeKAB5LmBWe6vAxTJIcEXG6309eXxUAGYEibxNRVoFKcmx04m5tinA0kdHV4SaNdgAyC8ZXwdZgNFJrUut6misnXqPOtp0vAVBmv+C0arbTlq6iS7eSTQvH9m4Jyfju1iZmde8GoGDF0DvgjCYTi9f9hq0ZazBoOot3f4dtfxhZ+4Utv/kOCzvewqMbabrif8nOD08R0FN1B5p0emKof5WhWrX4aM+UIoDDCUuS8+ijj1JcXExCQgLLli1j27ZtZz3+hRdeYObMmSQkJDB37lz++te/Djzn9Xr5xje+wdy5c0lOTiYvL4+bbrqJmhoplx+rmutOkKz14NM1nCXhq5ETbsmpDlpQU+NNJ+KnseTJGk+o7fPteiL2tMxxX8+VqNb19NROrJkc3e+nuEHtnjLPWXPa8wmJyRxKVhW7G3eGZl3O4Xf+gFnzcdxQSOG0M7c8MRiNLL3zSbZmfQqDprP0g++x9fc/Puu197z5IsuOqUrXu+Z8k5nLPhrU2EfDY1UVtv0xlOTktKkaSUlTlkc4kugX8iTn+eef56677uK+++5j586dzJ8/n1WrVtHQMPTU4ObNm7n++uu5/fbb2bVrF2vWrGHNmjXs3asKfnV1dbFz506+853vsHPnTl566SXKysr4xCc+EeovRYRIfzG3OkN2RKarw6nJpHZzuGuPRjiSyHDVqYaaTcacoFzP61BruLSWifX9PPL+OzhppEu3MvP8oX+39RZfDoC9+s2QxGAoU8lTTe7lwx6rGQws/eL/siXnegCW7f8vtvzuv4Y8turIXorf+jIGTWdb+sdZdu2/By/oMfAlZKhPumKjf5WrqY4iv1q8P2neJZENJgaEPMl54IEHuOOOO7j11luZPXs2jz32GElJSfz6178e8viHH36Y1atXc/fddzNr1ix++MMfsnDhQh555BEA7HY7GzZs4NOf/jQzZsxg+fLlPPLII+zYsYPKyom7y2Ii66hRtxqarOFpzBlJ7Ql5AHiaJu6257PpaVLb591WZ1CuZ8qeBkBKR0VQrhctmt57AYADqStISEoZ8phJy9cAMN1zAHdzcBfN9nR3MrNd9UXKXLx2ROdoBgPL/vX/UZp7EwDLy37Mlt9+b9Axne0u+p69HhudHDTNYv7nHw9q3GOhJ6l2LsYY6V91fM8mACoN+Tgyg/NzNJGFNMnxeDzs2LGDlStXfjigwcDKlSspLS0d8pzS0tJBxwOsWrXqjMcDuN1uNE3D4XAM+Xxvby9tbW2DPkT06C/m1m0b30LUWOBJVYmc5orPWjn+VvWHSG9yXlCuZytQtzezPRNnW77u91MQaKPArI+f8Thn0TTKDZMwajqHS4Nb/fjg5ldI0nqpJ4Op80e+RVkzGFh+x8OUFtwGwPIjD1L6f98C1NdV9tjnKPZX0oSDjNuei4qZW2NKFgDm3tiok9PVXwTQduZbiOJDIU1ympqa8Pl85OQMnprOycmhrm7oHSZ1dXWjOr6np4dvfOMbXH/99dhstiGPWb9+PXa7feCjsDC8BefE2SW0qzd8LWPibh/vZ0hTSc5ArZg4Y+5QX7duD87PYE7JHACyaA1LYbxwqDi4g0K9hl7dzIwLzz6LUpdzofrk0D+CGoPng0AricxLRr2lWzMYWPEvD1Ja9K8ArCh/hNJf/wdbn/42CzvfVguNr3pi0Jb0SDKnqiQn0Rsbrx9b4y71ScGSyAYSI2J6d5XX6+XTn/40uq7zi1+cuUbDvffei9vtHvg4cWLi/NU3Edj666ZkT/yZnMTA12jvic+F8snd6us2Z0wKyvXsaZk0o3bH1B6bGI0667ao2jQHkheTYks767H2eR8DYErbVnx9fUEZ39fXxzTXOwAkL1gz5uusuO3HlJbcqT6v/CXLK9RC491zv83MJSvPdmpY9fevSvZFf/+qPq+Hkl61yD5r9oURjiY2hDTJyczMxGg0Ul8/+H5xfX09TufQ9xKdTueIju9PcI4fP86GDRvOOIsDYLVasdlsgz5E9Mj0q0XojnHWTYkFjjw1W5Xtq4+5XjnBkO5VP9sp2cHbLlxvVrNC7qqJsY08p0rNyninf2zYY6ctuow2kkijjSO73w7K+GXvbSCNNtwkM2Pp6fVxRmPFzT9iy9SvDvz/1oyrWfqpu8YZYXAlpakkx6ZH/zKGiv3vkaT10q4nMmnGwkiHExNCmuRYLBYWLVrExo0bBx7z+/1s3LiRFStWDHnOihUrBh0PsGHDhkHH9yc4hw8f5vXXXycjIyM0X4AIOXdrEza6AMgqHF/dlFiQXagWyiZrPbiCvFg02nk9vWTqanFnxjhr5JysI0XNCnkbYr9RZ9WRvUz2V+DVjUy/8NphjzdbrBxJUbctWncHZyt52y7VSuLQEPV5xmL5jd9nx9KHKC36V86NgoXGp7JnqMa5Nrrw9PZEOJqzaz6oZtjKE2djMBojHE1sMIV6gLvuuoubb76ZxYsXs3TpUh566CE6Ozu59dZbAbjpppvIz89n/fr1AHzlK1/h4osv5v777+eqq67iueeeY/v27Tz+uPrh8Hq9fOpTn2Lnzp28+uqr+Hy+gfU66enpWCxS/TGWNFUdwQ60kkpaij3S4YRcQmIyDaSTTQuNJw6FteN6pDVWl5On6fTqZtKzxtec82T+tCnQCubW2N9GXrX5eQpQHb/nZoxsm33flI/A+2+RUfvWuMfX/X6KGtWWdNM5wSvLsejKW4N2rWBLdWTSpxswaX7amuvJzAvOrdRQMAaKAHZmySzOSIV8Tc51113HT3/6U7773e+yYMECdu/ezWuvvTawuLiyspLa2g97+Zx33nk8++yzPP7448yfP58XX3yRl19+mTlz1ALD6upq/vznP1NVVcWCBQvIzc0d+Ni8eXOovxwRZO2BuinNxuwIRxI+LWZ167X/a48XrTUqCWkwZAX1r1CrcwYA9q7Y37GWXql6UXVNuXLE50xesQaAab4jNNWNr4zGsb1byNMb6NYtzDz/6nFdK1YYjEbcWioAbS3R3XIlt10VAUyZel6EI4kdIZ/JAVi3bh3r1q0b8rlNmzad9ti1117LtdcOPVVbXFyMruvBDE9EUE9TBQDtCfEzo9GRmAfe/Xib4ivJ6WpUtYFclhyCub8xvWg2bIa8vip0vz/sDR6Dpe7EEab3HcKva0y56LoRn5fpLOSwaRrT+g5zrPRlMj/55THH0PDeH5gCHExewrnJqWO+TqxpN9jI8LvpiuImnc31VeTr9fh1jUnzL450ODEjNn8biInD1V83JXi3L6KdN1W9xWvu+Cpe2deivt7upODUyOnnLJ6JT9dI1nporovdnZMV7zwPQJllNpnO0RXGbMpVb3qmoxvGFUNOtarP0zf9qmGOnFg6TQ4Aet3R26Sz8v1N6r/GQmwOWYc6UpLkiIiy9NeLccRP7SJjejEAiXFWK8fYphIQny24/9bWhCRqDer2d33FvqBeO5xSy1XHb3fJFaM+N+NcVTRwWvt7eD29Yxq/+tgBJvsr6NMNTL9w6IacE1WvWW3V7+uI3sa5PeWqCGCDQ4oAjoYkOSKiUnrUPXBrZmQ6EEdCUk6gVo6ndpgjJ5akQI0cU1rw23c0W1Xi1FEdm9vIm+urmNmr6vxMOv/Toz5/6vwLacFGqtbNoe0bhz9hCCc2q5mkgwnzsI9w0fNE4U1QrR38UZzk2Jt2q08Kl0Y0jlgjSY6IqIw+dQ88NSd+kpy0PLWNPCfOauU4POrfOimINXL69bcE6W8REmuO/vP3qj2DaRq5k2aM+nyD0chRu+pI3b5nbFvJ7cf/DkBnyeoxnR/L/IkqyTF0R2eTTk9vD5M9ZQA4z7kowtHEFklyRMR0d7aTgaoymhXEuinRLrtgMj5dI0HzxvQaktHw+3xk+xsBSMsLfvsOLVO9fhLaK4J+7XBIOPIXAJoKPzrma2jT1bnOhn+O+tymuhPM8KhZsEnnD1+fZ6LRktQaF1OUNums2LeVBM2LixQKp86LdDgxRZIcETENVWpLcaeegC0tK8LRhI/ZYqVRywSgqTo2Zx5Gq6W+CovWh0/XQtKzKDlXzX5kdMfeNnJ3axOzulU/ooLzRr6r6lTTVqzBp2sU+yupPV42qnOPvfMChsBMkjMOinKeyhToX2X1uiIbyBm0lKkigMcTZ8fs7sFIke+WiBh3rUpyGo3ZcfeD22xRtXI66mK/gN1INNUcAaBRywhKFd1TZQUadTr99WNeeBsph97+PWbNR4WhiMJpY19Uak/P4pBlNgCVW/88qnOtR/4KQHPB2GeSYpnVppKc5ChNcsw1qghgV86iCEcSe+LrnUVEle7GCgDc1qH7mE1kXYlqy3xfc+zNPIxFZ72qkdNqDs2C1qzcYrp0K2bNR93xgyEZI1RMZWoNTW3++BMMV8ElAFjLXx/xOe3uloGZpNxlZ+96PlElOtTrMsUfnU0689vVovRUKQI4apLkiIjxB2rk9AS5bkos6LOrHUYGd3wkOd5AMteZGJqijwajkVqTShybK2Nnh1Vnu4tZndsAyF46/rUwOYtUleIZXTvp6e4c0TmH3nkJi9bHCS2PohnnjjuGWJSSrpIcu94WdZsBGqrLcdKIT9comS+LjkdLkhwRMeb2KgD89uBvKY52pv5aOV3xUStHa1P/1t7UgpCN4UpUr6OeutGtR4mksnf+SILmpUpzMvmc8W8NLpm9hAbSSdQ8HNr62shOOqhmkqqcK+PutnE/R4aaTbZoPtrbWiMczWBVezYBUGEqITnVEdFYYlF8vqJFVEjuVnViLBnR2xAvVJIDtXLS4qRWTkKg8KHBEbqE1uNQ31Ot+UjIxgg2fb9aO1OVc3lQEgzNYKAi/XwAuvb9bdjje3u6mNG2BYC0RWvGPX6sSkhKoUtXa8Xao6x/ladiKwBNDtlVNRaS5IiISfOquinJcVQjp196gaqVk+1vxNfXF+FoQs/Wq944ErOKQzaGOXs6ACkdFSEbI5h6ujuZ2aaaCqctDl6FYfNMVeemoOmfw956OVj6F1K0bhpJY/q5lwQthljkMtiB6EtyHM27ATAULYtsIDFKkhwREV5PL1m6KryVmRd/W1azcovx6EYsmo/G2opIhxNSut9Plk/1BLLnBr9GTj9bwSwAsj2xUXvo4OZXSNZ6aCCdaecGr+Hi9BUfw6MbKdDrqDr6wVmP7f3gTwAcy7g4qJ3hY1GnUSU5Pe7GCEfyod6eLiZ7VZmJvLnSlHMsJMkREdFYXY5R0/HoJtJzQrdOI1oZTSYaDGrbakvVxK6V0+ZqJkXrBiC7IHRJTk5gG3kWrXRE2bqKoXgCCUZ51mVBTTCSUx2UJahbG9XvnXkrua+vjyktbwOQNO/qoI0fq7oCTTo9bdHTpLP8g81YtD5asJFXPCvS4cQkSXJERLTWqPowDYasuP0LstWidhp11h+LcCSh1RRI4lqwkZicGrJx7GmZNKP+Gq8rj+5GnV5PL9NdKsFIWXBN0K/fWXQZAMnH3zjjMYd3vEEGbtpIYsbyK4MeQ6zxWFWTzmjqX+XqLwKYNCduF4WPl3zXRER0Naq6KS5LfDUCPFl3UqBWTktFZAMJsbY6lcQ1m0L/b91gVrOCrhP7Qz7WeBzc8hoOOmjBxsxlq4J+/bwlnwBgRs8eOttdQx7j2vkSAIds52GxJgQ9hljjS1CtHeiMniTHUrsDgB7n4ghHErskyRER0deiauR0JcZfjZx+PofaVWZ2TeyZnN5AjZx2a2hq5JysPaUYAG9DdN8C7HpfJRhH0i7CaDIF/fqFU+dRpTmxaH0cKj29Yafu91PQ8CYAhlkfC/r4sUhPCjTp7ImOW526309BpyoCaJ8mRQDHSpIcERHGNrU41GcrjHAkkZM8SZVodwaqmU5YLvVv7UnJD/lQ/jS15sfcGr3tMvw+H1OaNwGQMO+TIRlDMxiozrwAAM/Bv5/2fMWB9yjQ6+jVzcy4IDQxxBpjsuonZ+mNjiadVUc/IJsWvLqRknkXRDqcmCVJjoiIpO4aAEzp8Vcjp9+k+Wq3RIFeR3N9VYSjCR1LR6DgoSP0Ca3VqRp12ruit5L0oe0bycRFG0nMPC90syiJs68AoLjl3dO2ktdt/QMAB5IXS4G5ALMtG4DEKOlfVbXpSQAOJC4M6Vq2iU6SHBERDo+qkZOUHX81cvrZ0zKpMKg3/sr3N0U2mBBK7VUFD61hKPqYXqQaVOb2VUddef5+rp0qwThkOz+ka2GmL7+Cbt1CDs2U739v0HNZ1aq3lXfqFSEbP9Yk2ANNOn2uyAZCYOdbtdp95513Q4SjiW2S5Iiw8/t8ZPtVLYq0vNBtKY4FDXbVdbqnfGuEIwmdjD6V0Nqck0M+lrN4Jj5dI0Xrprku+url6H4/k+o3AmA4J7TbthMSkylLWghA/Y4Pt5LXVJQx1XcUn64x5YLgFSGMdclpamG8TW+LcCSw758vk00LLlKYc9l1kQ4npkmSI8Kupb4Ki9aHT9fIyiuOdDgRpRWqfkX25l0RjiQ0ujvbSUe9aWQGqjyHkjUhiVqDerOqr4i+beRH9rxLLo106VZmXbAm5OP1llwOgL1q08BjlZt/D8BB6xzSs0O/TipW2NJV/yobXXh6eyIai3fHbwAoy1qNNSEporHEOklyRNg1VaudL41aBmaLNcLRRFb27AsBKOktw+vpjXA0wddQpRYAd+iJ2BwZYRmz2apuAXbWHAzLeKPR9N6LABxMWRaWdRZFy9Rs0XTPftwtavY0tUItRG4vDv7W9VhmS8vCp2sAtDXXRywOV1Mdc9tVfZzMC2+PWBwThSQ5Iuw6GlSNnFZz/NbI6Vc4bT5tJJOoeajYvy3S4QSdu1YlOY3G7LAVM+u2qXVe/sZDYRlvpHS/n/zaDQD4Z308LGPmTppBhaEIk+bnSOmfaGmoZmav2s1XdN6nwxJDrDAYjbg1lXi2RbB/1cENv8ai9XHUOJkp82Tr+HhJkiPCztusauR0Joa+bkq0MxiNVCSoxbItB9+JcDTB191YAUCb1Rm2MbVMdVssob0ibGOOxK5/PE2Rv1pt274wfGth6rLVbKH/0D84+s6LGDWdI8Yp5BXPCFsMsaIt0KSzyxW5mZzMI2q2r3HqtRGLYSKRJEeEncGtkhxvavz1rBpKZ7ZaHGqs2R7hSILP71L/1j3J4Vv7kZyr3rwzuqNnG3lHWysFW74PwK7Cm0i1p4dt7NS5VwEwxV2K5fBfAGjMXxm28WNJf/+q3rbINOk8umczU31H8egmZn7ktojEMNFIkiPCLqFL1cgxOIoiHEl0SJm6AoC89rN3jI5F5nZV/0e3hS+hzSw+BwCnvz5q1jntfeYesmmhSnOy4LM/COvY05espF1PJJ025nerXXzOZWvDGkOs6DGr/lV97ZFJchr/+WsA9qaejyMzfLOfE5kkOSLs7L3qfnc818g5WfH8i/HrGnl6PU11lZEOJ6iSu1WNHHMYauT0y84roUu3YtZ81FVGfl3O0T2bWVL3PAAtF/+IhKSUsI5vtlg5nLpk4P+rNCfFs5ac5Yz45U0INOnsbA772L09Xcxs/BsApkU3hX38iUqSHBFWut9Plq8BAHtu6OumxIJUezrHjWpWq/L9tyIcTXCledXahuSc8CW0BqORWpPqidZ8PLLbyP0+H31//ipGTWdnysXMuyQyMyh9Uz4y8HlV9qXS0foM/IlqB6ChK/xNOve+8TwOOmggnXMuXBP28ScqeaWLsGprbSRZUzUosgumRjia6NHgUEUBPeWlEY4keLyeXrJ09RdxZl54/61diWrmqKeuLKzjnuq9lx5kRl8ZHXoiBdc/HLE4Jq9YM/C5Y+E1EYsj2mlJKskx9Ya/SadpzzMAHM3/REiatsYrSXJEWDVWHQGgGXvYp+2j2YdFAXdHNpAgaqypwKjpeHQT6TnhXWTucahZQq35SFjHPVlzfRWz9j0AwN4Z68jOj9zt2UxnEVumfpUtzhuYvvjyiMUR7UypqrWD1RPeJKe+6ihzutXGg8JL7wjr2BOdpIsirNrqjgHQbMohPKXhYoPznAvhfSjxHMLT2xPSnkbh0lpzlDygwZBFgdEY1rFNWdOgClI6KsI67smOPXsXS+jkiHEKi6/9j4jF0W/5jd+PdAhRz2pTSU5SmJt0lr/+BDmazn7LXGZPnRPWsSc6mckRYeVprgCgI0F2DpyscOo8XKSQoHmp2Dcx+lh1BYo+uizhL/poL1S1h7I8kenuvu/dv7DE/Xf8uob/qgcwmS0RiUOMTqJDdSJP9bvDNqbu91Nw/CUAOmZJn6pgkyRHhJdLNU30hLFuSizQDAaOJwaKApZNjKKAfa1qp1hXYl7Yx84pUX8NZ9NCR1t4bz14entI2ahmbt7LvJrpCy8J6/hi7FIC/avselvYutgf2PYPCvRaOvUEzln5ubCMGU8kyRFhZe2sVp9IjZzTdAWKAponSFFAY5tKaH22wrCPbU/LpBlVvbauPLw7rHb+7gdM8lfRjJ2ZN/w0rGOL8XFkqCTHovloD1Ny3LnlKQD2pV9OcqojLGPGE0lyRFil9qi6KQlZUiPnVKlTVZ+aiVIUMDFQ9NGUHpmEtsGsFju7TuwP25g15QdZUP44AOUL78WenhW2scX4JSSl0KWrpsHtYehf1dHWyjmtbwBgW35LyMeLR5LkiLDKCNTIsTmlRs6piudfhE/XyKWRxpqKSIczbg6PqpGTFKGEtj1ZbSP3NhwOy3i630/j779EguZln2U+iz72r2EZVwSXW7MB4UlyDrz+NElaLye0PGYskVYboSBJjgibrg43abQDkCk1ck6TYkvjuFG9MZ/YsymywYyT3+cj269K46flTYlMDOnqNWZ2HQvLeLs3/Ib53dvw6EZS1v5MCu7FqI5A/6oed+hbO6QceA6AquJr5PUSIvJdFWHTeELVLGkjCZtDNpAPpTFtAQCe8i2RDWScWhqrsWpefLpGVoTqw1idqlGnvbMi5GN1tLWSV6q2aO8ouIlJMxaEfEwRGt2BJMfT1hDScU4cfp9Z3v34dI2pH5HaOKEiSY4IG1ftUQCaDNkRjiR6GQJFAR0t70c4kvFpDhR9bNLSMVusEYkhvXAWALl91SHfKbP3mXvJoZlqLYdzb/jPkI4lQqvXGuhf1RHa1g5Vb/4KgL1JS8nKKw7pWPFMkhwRNj1NFQC0JeRGNpAo5pxzEQAlnsN4ensiHM3YdQRq5LSYw18jp5+zZBY+XSNF66a5IXT1co7t3criQAPOpov+Syp5xzhfQrr6pDN0SU6f18PUmlfUePM/G7JxhCQ5Ioz8rWpLcW9S+OumxIqCyefQig2r5qX8g82RDmfMvM3HAeiMQI2cftaEJOoCs4b1x/aGZAy/z4fn5a9g0vzsTL6I+ZdeG5JxRPjogf5Vhp7QbSHf98+XyaKVVmzMufQzIRtHSJIjwsjSof6a1h3hr5sSK04uCth66N0IRzN2mlsltN6UyBZ9bLKq7eudNQdDcv3tf3yYmX0H6NQTyL/+oZCMIcLLmJwJgCWE/at8O34DQFn2FROihUs0kyRHhE1yt6qRY8kojmwgUa4nZxEQ20UBrZ2qRo4hLbJFH7ttatGzv/FQ0K/d0lDNjL2q2N8H0+8kpyAyu8hEcJltavYvMURJTmtjLXM61B8w2RfdHpIxxIckyRFhk96n6qak5EiNnLNJmbYCgPyO0NxiCQd7r6oxkphZHNE4tAy1jTyhvSLo1z76zF3Y6eSosYTFn74n6NcXkZFgVwUck32h6V9VtuEJLJqPw8apTJ6zLCRjiA9JkiPCwtPbQ6au/jLKyJe/eM+mZN6F+HQNJ03UVx2NdDhjkulTCa09N7IJbXLeTADSeyqDet39pX9jifs1/LqG94r7pQHnBJKcphbL2/TgJzm630/OkRcAaJn+6aBfX5xOkhwRFo3VRzFoOt26hfQsWXh8NsmpDipM6jZL1Z63IxzN6Llbm0jVugHIivAtnMzicwDI9dXh9fQG5Zqe3h6SNtwNwHsZH2fm4suDcl0RHWyBJp02uoL2mul3ZM+7lPgr6NXNzPzIbUG9thiaJDkiLFpr1IxEozFLKnuOQFOgKKD3eOwVBWwK1MhpxUZSij2isWTnldClWzFrPuoqg7MuZ8dzP6TYf4IWbNKAcwKypWXh0zUA3E3Bbe3Q8s6vAdhru1D6moVJWN5tHn30UYqLi0lISGDZsmVs27btrMe/8MILzJw5k4SEBObOnctf//rXQc/rus53v/tdcnNzSUxMZOXKlRw+HJ7+NGJsugN1U1wWZ4QjiQ3GIlUUMK15d2QDGYP2OtVGockY+aKPBqORWpOaOWw+Pv5u5DUVZSw4phpwHl1wD/aMyNUBEqFhMBpxa6kAtLcGL8np6e5kVtPfAbAs/lzQrivOLuRJzvPPP89dd93Ffffdx86dO5k/fz6rVq2ioWHoktmbN2/m+uuv5/bbb2fXrl2sWbOGNWvWsHfvh4swf/zjH/Ozn/2Mxx57jK1bt5KcnMyqVavo6Ynd4mkTXV+rWhPRIzVyRiS3vyig9wi9PV0RjmZ0+os+tkdJ0Ud3otrh1VNXNq7r6H4/Tc/9G4mah32WuSz+xBeDEZ6IQu0GNQPZ2VoftGvufeNZbHRSRyazz/9E0K4rzi7kSc4DDzzAHXfcwa233srs2bN57LHHSEpK4te//vWQxz/88MOsXr2au+++m1mzZvHDH/6QhQsX8sgjjwBqFuehhx7i29/+NldffTXz5s3j6aefpqamhpdffjnUX44YI1ObqpHjsxVEOJLYkFc8ixZsWLS+2CsK6FIJrSfCNXL69TrUuiCtZXyLuLf94UHm9WynRzeTsvYRue06gXUG+lf1tgWvSaflg98BUFFwNUaTKWjXFWcX0p9Sj8fDjh07WLnywxbyBoOBlStXUlpaOuQ5paWlg44HWLVq1cDx5eXl1NXVDTrGbrezbNmyM16zt7eXtra2QR9isIbqcnb9/f9C1uMnqVvVTTGnF4fk+hONZjBwPGkOAK4YKwpo6axWn9ijo+ijKWsaACnt5WO+Rk1FGXP2/hiA3dO/JA04J7geswOAvvbgJDl1lYeZ070TgMLLpBlnOIU0yWlqasLn85GTM/i+dU5ODnV1Q9/rrKurO+vx/f8dzTXXr1+P3W4f+CgsjI5fvtGk5rdf4NzSL/P+G8+H5PppXjXtm5wTmY7UsagnZyEAltrYKgqY0qN+Dq0RrpHTz5avtpFnecbWv8rv89H67L+QrPVwwHwOS677VjDDE1HIG+hf5e9sDsr1yjf+CoOms88yj/zJs4JyTTEycTHfeu+99+J2uwc+Tpw4EemQok56r/qe9BzeFPRr+/r6yPKrZndpeVIjZ6Rs084HoKBjb8i7aAdTRqDoY2qUJLTOyXMByKaFznbXqM/f9vv/4RzPHrp0K7br/1duNcQBfyDJMXSNv0mn3+ejqPKPAHSdc/24rydGJ6RJTmZmJkajkfr6wYu36uvrcTqH3mXjdDrPenz/f0dzTavVis1mG/QhBnP4VaG+tOZdQb92U91xzJoPr24kK7c46NefqErmnU+fbiCblpgpCtjT1UEGqohaVsHUCEej2NOzaEH9zNeOslHniSMfMP/ggwB8MPsu8iefE/T4RPTRAv2rTL3jb+1wYMtr5Ov1dOiJzFkpu6rCLaRJjsViYdGiRWzcuHHgMb/fz8aNG1mxYsWQ56xYsWLQ8QAbNmwYOL6kpASn0znomLa2NrZu3XrGa4qz6+nuxIbawVPiPUJPV0dQr99aHaiRY8iQv4JHISnFPlAUsPqDtyIczcg0BGrkdOoJ2NKipw5IvVndonZVHRjxOb6+Pjqf/zyJmoe91gUs+dTdoQpPRBlTikpyrEHoX9W17f8A2J+xksTk1HFfT4xOyG9X3XXXXfzv//4v//d//8eBAwf44he/SGdnJ7feeisAN910E/fee+/A8V/5yld47bXXuP/++zl48CDf+9732L59O+vWrQNA0zS++tWv8p//+Z/8+c9/5oMPPuCmm24iLy+PNWvWhPrLmZBaGz5cq2DRfJR/ENyFrh2BGjmtZqkpMlrNA0UBt0Y2kBFy16oaOY3G7KjafdSePAkAb/3ICwK+99wPmendT6eeQPr1j2MwGkMVnogyVruq8ZTkdY3rOu3uFua43gTAdt6t4w1LjEHI/6y+7rrraGxs5Lvf/S51dXUsWLCA1157bWDhcGVlJYaTfhmed955PPvss3z729/mm9/8JtOmTePll19mzpw5A8f8x3/8B52dnXz+85/H5XJxwQUX8Nprr5GQIC3rx6KtsZqTK5q4y96BZauCdv2+5uMAdCVKjZzRMk5aBk1/IL1ld6RDGZHuxgoA3NboKvroS58KLjC7jo3o+OMHdnDu4UdBg31zv8HS4hmhDVBElUSHSnJS/OPbiXtgw1Ms1TwcNxQwY+GlwQhNjFJY7h2sW7duYCbmVJs2bTrtsWuvvZZrr732jNfTNI0f/OAH/OAHPwhWiHGt+5Sqnta64O7m0drUomZfqtTIGa28ORfDDij2HqWnu5OExORIh3RWvigt+pjgnAHHwN51fNhj+7weev/wBayalz0JS1hyzVdDH6CIKimB/lUO3Y3u949pVrKjrZXCvf8PgNqStUyKopnNeCLfdUGvqxaAZlSVz0ldwd3Nk9ilauQY0oqCds14kTtpOk04sGg+KvZEf70cc7u69em3R9e/dXqh2rab660a9rX93jPfZXrfIdpIIufGX0bVbTcRHvZ0dafBovnoGMOOPIB9T99FLo3UaNnMXXNXEKMToyE/vQJ/u9qpdsy+nF7dTDptVB0bf5+ffnaPmilKyo6OLcWxRDMYOJGkdvS4Dkd/kpPUrRJmc0Z0JTnOkln4dI0UrZvmhjPXyzn6wRYWlaveVGULvk1OhLuoi8hITE6lS7cC0NZcO+rz9777CsuaXgKg5bKfkpzqCGZ4YhQkyREYOlWS05eazzGLqg5btzc4u3l0v59sn+pT5siVN4yx6HUuAsBSuyPCkQwvPVD0MSWrOLKBnMKakESdQa2zqD/DNnJPbw+8/AUsmo9dSedJb6o459ZU2YGOltH1r+rqcJP2+tcB2JpxNXMuvDrosYmRkyRHYO5RVT0NqTm4M84FwF8ZnN08rU21JGoeALIKJgflmvHGNl0VBSzq/CCqiwL2eT1k6uq1lJ4fHTVyTtZsVbNLnTUHh3x+52++yRRfOS5SKLxJblPFuw6jun3f7R66mfSZ7Pm/r5Ov16tGnDc9FILIxGjIT7EgyaPemMy2HKyTzwMg2/V+UK7d3F8jhzSsCUlBuWa8mTzvAry6kUxc1FYejnQ4Z9RYU45J8+PRjWQ6o+t2FUCXTd0u9TcdOe25w7veZvGJJwE4uuT7URm/CK9ucxoAnlE06dy/5TWWN74AQOOlPyHVnh6S2MTISZIjSO1TSU5ieh6F8y4GYJKvEnfr+Euat9erLbstJqmRM1YJSSlUmNUsWE2QbiOGQmtNoEaOITMqa8poGWp2KaFt8Dbynu5OzK/8GybNz86Ui1l01b9EIjwRZTwWBwD+jpElOd2d7dj+/lUAtqVdxdyLrwlRZGI0JMkRpPldAKRm5JPpLKRKc2LQdI6/P/43VE+gRk5HYu4wR4qz6S8K6IviooCdgaKPLnN01cjpl5SrGnWm91QOenzX0/9Bsf8EzdgpufmXkQhNRKG+xAwA9K6RNel8/+l/p0CvpZ4MZt7881CGJkZBkpw419nuIknrBSAtR9WxqbXNU88d3Tzu62su9YbiSckf97Ximal4GQDprcG5jRgKfS2Boo9RViOnX1aJ2qWW66vD61Gv+YPvvc7SmmcAqDzvR6RlSTIuAgJJjrG7ZdhDD27bwNK65wGou/h/sDkyQhqaGDlJcuJca73aTtulWwe2OfrzlwKQ0jD+3TyWzkCNHIescRiP/DkXAVDsPRb03mLBYmxTr6W+KC36mJ1XQrduwaz5qKs8RHdnO8l//RJGTWe77SOc+9EbIx2iiCKGQP8qyzD9q3q6Okj+25cxaDrv2Vcz/9IzF7IV4SdJTpxrb64GoNXgGHgsa7Z6Q53ccwBfX9+4rm/rVTUmErKkRs54OAun0UgaZs3HsT3vRDqcIfUXfTSmR2dCazAaqTGpGcWWyn28/39fp1CvoYF0pt3yiwhHJ6KNKVU1mE0cJsnZ/fR/UKjX0Ega029+JByhiVGQJCfOdbeqJKTd9OH06qSZi+jQE0nWeqg4ML4WD1mBGjn2XNk+Ph6awcCJZNW/re1QdBYFdPQXfYyyGjkncyeqBMy//WmW1v8egNqLf4w9PXo6povo0N+/KtnnPuMxZdvfYEntswBUX7BeXkdRSJKcOOd1qzembsuHWx2NJhPlCaoMftOBt8d87XZ3CzY6AcgqiL66KbHGk6uKAlrroq8ooO73k+VXu1DS8qK36GOvXSXb53a9i0HT2ZZ2ldxeEENKcqgdoTZ96CSnp7uThL9+eeB254KV14czPDFCkuTEuf6WDp6EzEGPd2SrN1Rj1bYxX7upStV0cZEiZc2DwDEtUBQwyL3FgqG5oZoEzYtf18jKj94kx5Q9feDzOrKYdYvcXhBDs2eoXYI2ugYWqp9s12/uZZL/BE04mHbzo+EOT4yQJDlxztil/vr2Jw+uY5MyVRUFzG3fM+Zru+vUluImo9TICYbieefj0Y1k4KamoizS4QzSUqOKPjZpaVisCRGO5szshbMHPm+6/H4p1ibOyJaWhU/XAHCf0trh8K63WVL9GwBOnPdf2DPkd1y0kiQnzll6VME/Q+rgH9JJ8y/Gr2vk6/U01VUOdeqwepsqAGi3RmfdlFiTkJhMhVnNktRGWVHA9kBCG+1FH6ctuIgtzhvYes53pKeQOCuD0YhbSwWg/aQmnb09XZheWYdJ87Mj9TLZlRflJMmJc/0tHSz2wW9ONkcGx42TAKgcY1FAvVUlR71SIydoWtJVbzFfkHqLBYu3pQKAzigv+qgZDCz/wv9j2bX/HulQRAxoN6j+VZ2uD/tX7fzttyjxH6cFG5Nv+n+RCk2MkCQ5cc7WpwpdJaWfXsCtIW0+AJ7y0jFd29Kh6qZgLxxbcOI05kBRwIwoKwqouU4A4EmJzho5QoxFZ6BJZ2+gSeeR999lyYmnAKhY9gMpHhkDJMmJY7rfT7ruAsCWdfpsi6FQvaE6mneN6fopPWqK15IpNXKCJX+u6i1W3FdOV8eZt7aGmzVQI8eQJgmtmDh6LKpJZ197E57eHrQ/3xnocXYRC6+4NcLRiZGQJCeOtbmasWiq2F9a9ul/gefOUW+okz2H6O3pGvX10/vUXz82pyQ5weIsnEoD6Zg0P+V7oqdeji2Q0CZkFkc2ECGCyGtVSY6/s4kdz3yHKb5yWkll0uekeGSskCQnjrka1C2GNpJISEw+7fn8ybNpxo5F66P8g9H1serp7iQTFwCZ+VIjJ5iqUgJFAQ9HT5KT6Zeij2Li8Qf6V9nqSll8/AkAji65j4wcuS0bKyTJiWMdgR0DLkPakM9rBgOVSeoN1VX2z1Fdu7HqCKB6YtnTs8cRpTiVJ3cxAIn10VEUsM3VjA010ydFH8VEoiWrJOcczweYNR+7ki9g0RW3RzgqMRqS5MSxbpdaR9FuOnOtkN7AG6q1dnTtHVy1xwBoMGajGeRlFkyO6aoo4KSufVFRFLApkNBK0Ucx0ZhSPmzT4CKFws/9Qn6fxRj514pjfYGWDj2WjDMe45h+AQCFnaOrstvdWAGA2yI1coKtZO55eHQTabRRfWx/pMOhrU4ltFL0UUw0FtuHSc6Rhd8m0xmdzWfFmUmSE8f0drWOwpt45qZy/VV2M3GNqsquL1AjpydZauQEmzUhiXKzui1Uu2/svcWCRYo+iomqeMElHDcUsC394yz62L9GOhwxBpLkxDFTt2rpoKecec1MQmIy5eZpANTu3TTia5vb1KJmv00W6IVCa4YqCuiPgqKAeqBGjhR9FBONzZHBpO/uY+mXfyu3qWKU/KvFMWugpYMp9ey3GfrfUH3Ht4z42kndalGzWbYUh4QlUBQw0zX23mLBIkUfhRDRSpKcOJbsDbR0cJy9aqelZDkAma6RV9lN96qGdinZUiMnFArmXQKoooD1VUcjGkt/0Udr5qSIxiGEEKeSJCeO2XytACRnnN7S4WSFA2+oFbS7W4a9bp/XQ6auEqgMqZETEtn5JZQbJmHUdIy/uowDW/8esVj6iz6m5EiNHCFEdJEkJ075+vpI01VbAHvW2dfNZOUVU6NlY9R0Kt4fvl5OY005Js2PRzeRkSO3MELFcuPvKDdMIhMXU/96PVuf/++wbymXoo9CiGgmSU6ccjXXYdLUG6Ijc/gmczWp8wDoODJ8ld3WmkCNHEMmBqNxHFGKs8mffA7ZX3ubHSmXYNZ8LDuwnu0PX09PV0fYYji56KMjQ7aQCyGiiyQ5ccrdWA1AKzbMFuuwx/vylwCQ3DB8ld3OhnIAXFIjJ+SSUx0svOuPbJn6VXy6xhL3a5y4/2Jqj498u/94uGorAGiUoo9CiCgkv5XiVGeLqnZ8ppYOp8qYeSEAJd378Pt8Zz22r+U4AF2JZ1/rI4JDMxhYfuP3ObDy/2gllWm+IyQ8uZK97/w55GN3N6qEVoo+CiGikSQ5caqnVe2I6TSPLMkpnr2ELt1KqtbN8bKdZz3W2Ka2FPukRk5Yzbnwanpu3cgR4xTSaGPWhpvY8tvvhXSdjj9Q9LE7WRJaIUT0kSQnTvnaAi0drJkjOt5ktnAsYRYADcNU2U3sUrfCjGlSAj3ccifNoODrb/OefRVGTWf5kQfZ+eBaujrcIRnP2K4SWin6KISIRpLkxKsOte237ywtHU7VnrUQAEPVtrMe5/CoGjnJ2bKlOBISklJY/JXn2DrzHry6kUXtb1D/wEVUH9sX9LGSutVtT3O61MgRQkQfSXLiVH9LB4apdnyypCnnAeBsO3OVXb/PR7ZfXTstb8rYAxTjohkMLPvMvRy+4lmacFDiryD16Y/w/psvBHWcNCn6KISIYpLkxKmEXtXSwTiKJKd4/iUAFOo1tDRUD3lMS2M1Vs2LT9fIypc3vkibvXw1/js2UWaaiY1O5m66g9Kn7hl28fhI+Pr6yPKroo9p+ZLQCiGijyQ5cSrFqyoXJ6QNXyOnnz09iwqDKu5X+f5bQx7THKib0qSlj2hrugi97PwSiv/9TbZmXI1B01lR8Qvev//jI6pefTaNtRWYNR9e3UimU25XCSGijyQ5ccrudwGQMkxLh1M12OcD0H2sdMjnOwI1clrMUhgumlgTklj2pafZNvd7eHQT53a9S8vDF3K8bPeYr9lao3pmNRoyMZpMQYpUCCGCR5KcOOT19JJGGwC2zPzRnVykul87moYuCuhtVjVyOqVGTlRauvZrlH/8BRpIZ5K/isxnV7Hjr0+O6Vqd9SqhbZWEVggRpSTJiUOtjWpHTJ9uwJExuiJuznMuAmCy5xCe3p7TntfcJwDwpowyeRJhM2PxZRi+8Db7LPNI1npYtO2rbPnFv+L19I7qOn0tqkaOFH0UQkQrSXLiUFtToKWDZh/1bYbCqfNoJRWr5qX8g82nPZ/Qqa5tkBo5US3TWciMuzdSmnsjAMvrn+PITy6lqeb4iK+htamEtk9q5AghopQkOXGos1nN5LiN6aM+VzMYOJ40B4DWsndOe97Wq4oMJmbJzqpoZzJbWPGvj7JzxSN06InM8u6Dxy9if+nfRnR+Ypd6HZkkoRVCRClJcuKQx6VaOnSZR5/kAHTnLALAUvveoMd1v58snyoyaM+VLcWxYuGqz9F6498pN0wiExfTX/ssW35737DtIOye/oS2OAxRCiHE6EmSE4d8baqAW2/CyFo6nMo+/QIACjr2DnojbHM1k6J1A5BdIElOLCmcNp+cu/7JdttHMGl+lh95iF33f+KM28x1v5/sQELrkIRWCBGlJMmJR52Blg5JI2/pcLKSeRfg1Y1k00LdicMDjzdVqc9bsJGYnDr+OEVYJaXYWfTV37N19rfw6EYWdv4T10PnU77/vdOObW2qJVHzAJBVIO07hBDRSZKcOGQOtHTQUrLHdH5icirlZvXXe/UHHxYFbKs7BkCzSbYUxyrNYGDZp/+D8o+/SD0ZFOo15Dx/Fdtf+eWg45qrAzVySMOakBSJUIUQYliS5MShRI8qxW+yj277+Mla0hcA4KvYMvBYb6BGTrt15FWURXSasfgyzP/2Tz6wnkuS1sviHf/B1kduGygb0F6vEtoWSWiFEFEspElOS0sLN9xwAzabDYfDwe23305HR8dZz+np6eHOO+8kIyODlJQU1q5dS319/cDz77//Ptdffz2FhYUkJiYya9YsHn744VB+GRNOSp9aZ5HoGHt9E3PxcgAyWnd/+KBLbSn2SI2cCSE9O5/Zd79Oaf6tACxr+gPlP7mIuhNH8AQS2o5ESWiFENErpEnODTfcwL59+9iwYQOvvvoqb7/9Np///OfPes7XvvY1XnnlFV544QXeeustampquOaaawae37FjB9nZ2fz2t79l3759fOtb3+Lee+/lkUceCeWXMqE4/K0ApIy22vFJCuZdAkBxXzmd7S4ALB2Bpp2OwvGEJ6KI0WRixR0PsfvCX9JGMjP6yrA+cSm2in8AktAKIaJbyBrOHDhwgNdee4333nuPxYsXA/Dzn/+cK6+8kp/+9Kfk5Z0+i+B2u3niiSd49tlnueyyywB48sknmTVrFlu2bGH58uXcdtttg86ZPHkypaWlvPTSS6xbty5UX86E0dPVgY0uABxZY3+DyimYQh2ZOLUmDu75J3PO/zipPapuSkKm1MiZaBZc/hmqS+bT+MxnmeI7Rpp3LwAGh9TIEUJEr5DN5JSWluJwOAYSHICVK1diMBjYunXrkOfs2LEDr9fLypUrBx6bOXMmRUVFlJYO3RASVHKUnn7mmi+9vb20tbUN+ohXLQ1qtsWjm7A5MsZ1rerUuQC0H34XgMzAluJUp+y2mYjyJ88i/+v/ZJvjyoHHErNl+7gQInqFLMmpq6sjO3vw7h2TyUR6ejp1dXVnPMdiseBwOAY9npOTc8ZzNm/ezPPPP3/W22Dr16/HbrcPfBQWxu/tlPZAS4cWLQ3NML5/fm/eEgCS6nfQ1eEeaPqZWTB1fEGKqJWQlMLSr/6O7Yt+zJac65l1/scjHZIQQpzRqN/l7rnnHjRNO+vHwYMHQxHrafbu3cvVV1/Nfffdx0c/+tEzHnfvvffidrsHPk6cOBGW+KJRV4u6pdRmShv3tTJmXghAcfc+Gk8cAaBdT8SeNrYigyJ2LP74v7L8i49htlgjHYoQQpzRqNfkfP3rX+eWW2456zGTJ0/G6XTS0NAw6PG+vj5aWlpwOofeuux0OvF4PLhcrkGzOfX19aeds3//fi6//HI+//nP8+1vf/us8VitVqxW+WUM4HGrGbEuy/huVQEUn7OM7pct2LVODuz8C5OAJmMOUgZQCCFENBh1kpOVlUVW1vCVclesWIHL5WLHjh0sWqR6Hb3xxhv4/X6WLVs25DmLFi3CbDazceNG1q5dC0BZWRmVlZWsWLFi4Lh9+/Zx2WWXcfPNN/Nf//Vfo/0S4pq/XSWenjG2dDiZ2WLlkHUG53g+IL38FQDc1rHX3hFCCCGCKWRrcmbNmsXq1au544472LZtG++++y7r1q3jM5/5zMDOqurqambOnMm2bdsAsNvt3H777dx11128+eab7Nixg1tvvZUVK1awfLmqy7J3714uvfRSPvrRj3LXXXdRV1dHXV0djY2NofpSJhRDp6o55E8aW7XjU7VlLgRget8hAHqTx157RwghhAimkNbJeeaZZ5g5cyaXX345V155JRdccAGPP/74wPNer5eysjK6uroGHnvwwQf52Mc+xtq1a7noootwOp289NJLA8+/+OKLNDY28tvf/pbc3NyBjyVLloTyS5kwLD1NAGipwUlyEqecN+j/dXv8LuoWQggRXTRd1/VIBxFubW1t2O123G43Npst0uGE1cH/XM7MvgPsXP4wC1ffMu7rtTbWkvbozIH/37H0IRZdeeu4ryuEEEKcarTv39K7Ks6k+lRLh6S04NxWSsvKpdLwYVHBVKcUAhRCCBEdJMmJI7rfT5rfBUDqOKodn6rONn/g8/Q8KQ4nhBAiOkiSE0c6O9wkab3A+Fo6nKZwKQA9upmMbOllJIQQIjpIkhNHXIGWDl26leRUR9CuW7TkY3ToiRxKnD/uKspCCCFEsISsQaeIPu3NgZYOhjSSgnhdZ9E0Wu7cwbTk+FrELYQQIrpJkhNHugMtHTpMZ25mOlbpcptKCCFElJF7C3HEG8SWDkIIIUS0kyQnjvg7VEsHb+LwbTmEEEKIWCdJThwxdqokx58cnGrHQgghRDSTJCeO9Ld0MKRIkiOEEGLikyQnjiR5VbVjqyM3wpEIIYQQoSdJThyx9QVaOmRIp3AhhBATnyQ5cUL3+0nXWwFIzZCZHCGEEBOfJDlxoq21EYvmAyAtuyDC0QghhBChJ0lOnHA1qmrHbSSTkJgc4WiEEEKI0JMkJ070t3RoNaRFOBIhhBAiPCTJiRM9rbUAdJgkyRFCCBEfJMmJE31t9QD0WDMjHIkQQggRHpLkxAm9XVo6CCGEiC+S5MQJU5dKcvRkSXKEEELEB0ly4oS1V7V0MNqcEY5ECCGECA9JcuJEsrR0EEIIEWckyYkTdp9KcpLTpaWDEEKI+CBJThzw9fWRprsBsGflRzgaIYQQIjwkyYkDruY6jJqOX9dwZMrtKiGEEPFBkpw44A60dHBpqZgt1ghHI4QQQoSHJDlxoDPQ0sEtLR2EEELEEUly4sBASwdzeoQjEUIIIcJHkpw44GtXLR16paWDEEKIOCJJTjzoUNWO+5KyIxyIEEIIET6S5MQBU3ej+iRFWjoIIYSIH5LkxIGEQEsHk7R0EEIIEUckyYkDKQMtHaTasRBCiPghSU4csPtbAUjJkJkcIYQQ8UOSnAnO6+kljXYA7FkFEY5GCCGECB9Jcia41sYaAPp0Aw6ZyRFCCBFHJMmZ4NyNVQC0aA4MRmOEoxFCCCHCR5KcCa6rRbV0aDNKSwchhBDxRZKcCc7jUtWOO6WlgxBCiDgjSc4E52sLtHRIkEKAQggh4oskOROc1qmSHF+S9K0SQggRXyTJmeDMgZYOWkpOhCMRQgghwkuSnAkuwaOqHZvtsn1cCCFEfJEkZ4JL9TYDkJAmLR2EEELEF0lyJjjHQEsHSXKEEELEF0lyJrCerg5StW4AHNnS0kEIIUR8kSQniHS/n+MHd1J5aHekQwGgpUEVAuzVzdjsUidHCCFEfAlpktPS0sINN9yAzWbD4XBw++2309HRcdZzenp6uPPOO8nIyCAlJYW1a9dSX18/5LHNzc0UFBSgaRoulysEX8HobHnqG0x67lLq/vo/kQ4FgLYm1dKhVXOgGSSfFUIIEV9C+s53ww03sG/fPjZs2MCrr77K22+/zec///mznvO1r32NV155hRdeeIG33nqLmpoarrnmmiGPvf3225k3b14oQh+TlCkrAJjk2oru90c4GuhuqQWgzSQtHYQQQsSfkCU5Bw4c4LXXXuNXv/oVy5Yt44ILLuDnP/85zz33HDU1NUOe43a7eeKJJ3jggQe47LLLWLRoEU8++SSbN29my5Ytg479xS9+gcvl4t///d9D9SWM2rQlH6VXN5NDM5WH90Q6HDzuOgA6LVIIUAghRPwJWZJTWlqKw+Fg8eLFA4+tXLkSg8HA1q1bhzxnx44deL1eVq5cOfDYzJkzKSoqorS0dOCx/fv384Mf/ICnn34awwhuw/T29tLW1jboIxQSklI4lDAHgNqdfw3JGKPhb1e3+TwJkuQIIYSIPyFLcurq6sjOzh70mMlkIj09nbq6ujOeY7FYcDgcgx7PyckZOKe3t5frr7+en/zkJxQVFY0olvXr12O32wc+CgsLR/8FjVBn/oUAJJx4O2RjjJShswEAPUn6VgkhhIg/o05y7rnnHjRNO+vHwYMHQxErAPfeey+zZs3ixhtvHNU5brd74OPEiRMhiy9z/moApnXuwuvpDdk4I2HpaQJAS5WWDkIIIeKPabQnfP3rX+eWW2456zGTJ0/G6XTS0NAw6PG+vj5aWlpwOoduMeB0OvF4PLhcrkGzOfX19QPnvPHGG3zwwQe8+OKLAOi6DkBmZibf+ta3+P73v3/ada1WK1ardaRf4rhMnrOclj/aSNfa2L/zTWYvXx2WcYeS2KuqHVsc0tJBCCFE/Bl1kpOVlUVW1vC3P1asWIHL5WLHjh0sWrQIUAmK3+9n2bJlQ56zaNEizGYzGzduZO3atQCUlZVRWVnJihVq59If/vAHuru7B8557733uO222/jnP//JlClTRvvlBJ3BaKQ8dTHp7W/QtvcfEMEkx+ZTfasSHbkRi0EIIYSIlFEnOSM1a9YsVq9ezR133MFjjz2G1+tl3bp1fOYznyEvT7UYqK6u5vLLL+fpp59m6dKl2O12br/9du666y7S09Ox2Wx86UtfYsWKFSxfvhzgtESmqalpYLxT1/JEir/kEtjzBml170YsBt3vJ83fChqkZuVHLA4hhBAiUkJaJ+eZZ55h5syZXH755Vx55ZVccMEFPP744wPPe71eysrK6OrqGnjswQcf5GMf+xhr167loosuwul08tJLL4UyzKArXHIVAFO9ZbhbmyISQ2eHm0TNA4BDkhwhhBBxSNP7F7XEkba2Nux2O263G5vNFpIxKn8wmyJ/NTtXPMLCVZ8LyRhnc+LIBxT+9gI69QSSvz90xWghhBAiloz2/Vtq/YdIbYZaQ+Q99HpExm9vUn2rWg2OiIwvhBBCRJokOSFinaEKGua3DF34MNR6WlVLh3aTNOYUQggRnyTJCZGpS1fj1Y0U6LXUlIeubtCZeAMtHbqlpYMQQog4JUlOiKTY0jhimQnAie1/Cfv4/S0dvImS5AghhIhPkuSEkCv3AgDMxzeFfWxjlyrE6E/OHuZIIYQQYmKSJCeE0uZ+FIApHTvw9fWFdWxLj6p2bJSWDkIIIeKUJDkhNHXBRbSRhJ1Oju4Jb2HAZG9/SwepdiyEECI+SZITQiazhaNJ5wLQsufvYR3b1qdaOiSlS5IjhBAiPkmSE2KeSRcDkFrzz7CNqfv9pOkuAGyZUu1YCCFEfJIkJ8TyF6kWD9N699HV4Q7LmG2tjVg0HwBp2ZLkCCGEiE+S5IRY/uTZ1GjZWDQfh9/7R1jGdDVUAdBGMtaEpLCMKYQQQkQbSXJCTDMYqEpbBkD3gQ1hGbO9pQaAVkNaWMYTQgghopEkOWFgnHo5AM6m0rCM19/SoUNaOgghhIhjkuSEwZSlV+DXNYr9lTTWVIR8vL5AS4ceq1Q7FkIIEb8kyQkDR6aTo+apAFRsC32LB71DWjoIIYQQkuSESVP2eQBox94M+VimriYAdGnpIIQQIo5JkhMmqed8BIDitvfQ/f6QjmXtVUmO0eYM6ThCCCFENJMkJ0ymLbqcLt1KJi4qDrwX0rH6WzpYHZLkCCGEiF+S5ISJNSGJw4nzAKjf/VpIx7L7WgFITs8L6ThCCCFENJMkJ4y6Cy8CIOnE2yEbw9fXR5quKis7sgpCNo4QQggR7STJCaOcc68AYFr3Hnq6O0MyRmtTLUZNx69rOLKkOacQQoj4JUlOGBXPXEQjaSRqHo7s2BiSMdyN1QC4tFRMZktIxhBCCCFigSQ5YaQZDFTYlwDQvj80LR66Ai0d3AapdiyEECK+SZITbpMvBSCzfnNILj/Q0sEsSY4QQoj4JklOmJUsuQqAKX1HcTXVBf36vjZ1zV5rRtCvLYQQQsQSSXLCLDNvEuWGSRg0naOhaPHQ2QhAX5JUOxZCCBHfJMmJgPqsFQD4Dr8R9GubulWSQ4okOUIIIeKbJDkRkDRTtXgoat0a9BYPCYGWDiZp6SCEECLOSZITAdOWrsKjm3DSSNXRD4J67ZSBlg5SI0cIIUR8kyQnAhKTUzlsPQeAmp1/C+q17X4XACmZ0tJBCCFEfJMkJ0La8i8AwHL8raBd09PbQxrtgLR0EEIIISTJiZDMeasBmNq5iz6vJyjXbA1UO+7TDdjTZeGxEEKI+CZJToRMnnseLlJI1bo5sis4szltTaracYvmwGA0BuWaQgghRKySJCdCjCYTx1IWAdC69x9BuWZXi5rJaTOmBeV6QgghRCyTJCeC+oovAcBR+05Qrtfbqqodd1qk2rEQQgghSU4EFSxWLR6meQ7S7m4Z9/V87f0tHTLHfS0hhBAi1kmSE0F5xTM4oeVh0vwc2frXcV/PEGjp4EvKGve1hBBCiFgnSU6E1WQsB8BzaOO4r2UOtHTQUnPGfS0hhBAi1kmSE2GW6ZcDkNe8ZdzXSuhV1Y7NNklyhBBCCElyImzK0ivo0w0U6jXUVR4e17VS+9S6noQ0qXYshBBCSJITYTZHBkfMMwCofO8v47qWw98KQKq0dBBCCCEkyYkGrbnnA2Cs2DTma3R3tpOqdQPgyC4MRlhCCCFETJMkJwo45nwUgMntO/D7fGO6RmuDKgTYq5tJtUkxQCGEEEKSnCgw9dxL6NATSaONY3tLR3Wur6+P4wd3cvyfzwCqpYNmkH9WIYQQwhTpAASYLVb2JS9gQVcpjbv/xtT5Fwx5nKe3h8qDO2g58h567fvYXQco8h5jktbLpMAxrRYnueELXQghhIhakuREid6ii+FgKak1qsVDV4ebyv3bcB/bjla3h4z2gxT2HWeqdsrtLA26dCuVlim47TPJufzOCEQvhBBCRB9JcqJE7sIr4OB/M73nA47/4BwKfdXM1PTBB2ngJpkT1ml0pJ2DqWABWdOWUjBlDjNN8k8phBBCnEzeGaNE4dR51Gg55FHPJH8VaNBIGjWJ0+nKOAdr4UJyZy7FWTiNObLmRgghhBhWyN4tW1pauOGGG7DZbDgcDm6//XY6OjrOek5PTw933nknGRkZpKSksHbtWurr60877qmnnmLevHkkJCSQnZ3NnXfG/i0azWCga82TbJn6Nd6/+Fc0feEDsr5Xwfxv/IMV//IgC1d9jtxJM2RRsRBCCDFCmq7r+vCHjd4VV1xBbW0tv/zlL/F6vdx6660sWbKEZ5999oznfPGLX+Qvf/kLTz31FHa7nXXr1mEwGHj33XcHjnnggQe4//77+clPfsKyZcvo7OykoqKCT3ziEyOOra2tDbvdjtvtxmazjevrFEIIIUR4jPb9OyRJzoEDB5g9ezbvvfceixcvBuC1117jyiuvpKqqiry80yvyut1usrKyePbZZ/nUpz4FwMGDB5k1axalpaUsX76c1tZW8vPzeeWVV7j88svHHJ8kOUIIIUTsGe37d0jufZSWluJwOAYSHICVK1diMBjYunXrkOfs2LEDr9fLypUrBx6bOXMmRUVFlJaq2jEbNmzA7/dTXV3NrFmzKCgo4NOf/jQnTpw4azy9vb20tbUN+hBCCCHExBaSJKeuro7s7OxBj5lMJtLT06mrqzvjORaLBYfDMejxnJycgXOOHTuG3+/nRz/6EQ899BAvvvgiLS0tfOQjH8Hj8ZwxnvXr12O32wc+Cgul7YEQQggx0Y0qybnnnnvQNO2sHwcPHgxVrPj9frxeLz/72c9YtWoVy5cv53e/+x2HDx/mzTffPON59957L263e+BjuJkfIYQQQsS+UW0h//rXv84tt9xy1mMmT56M0+mkoaFh0ON9fX20tLTgdDqHPM/pdOLxeHC5XINmc+rr6wfOyc1VtXxnz5498HxWVhaZmZlUVlaeMSar1YrVaj1r3EIIIYSYWEaV5GRlZZGVlTXscStWrMDlcrFjxw4WLVoEwBtvvIHf72fZsmVDnrNo0SLMZjMbN25k7dq1AJSVlVFZWcmKFSsAOP/88wceLygoANRW9aamJiZNmjTkdYUQQggRn0K6hby+vp7HHntsYAv54sWLB7aQV1dXc/nll/P000+zdOlSQG0h/+tf/8pTTz2FzWbjS1/6EgCbN28euO6aNWs4cuQIjz/+ODabjXvvvZdjx46xe/duzGbziGKT3VVCCCFE7ImK3VUAzzzzDDNnzuTyyy/nyiuv5IILLuDxxx8feN7r9VJWVkZXV9fAYw8++CAf+9jHWLt2LRdddBFOp5OXXnpp0HWffvppli1bxlVXXcXFF1+M2WzmtddeG3GCI4QQQoj4ELKZnGgmMzlCCCFE7ImamRwhhBBCiEiSJEcIIYQQE5IkOUIIIYSYkCTJEUIIIcSENKo6ORNF/1pr6WElhBBCxI7+9+2R7pmKyySnvb0dQHpYCSGEEDGovb0du90+7HFxuYXc7/dTU1NDamoqmqYF9dptbW0UFhZy4sQJ2Z4+QvI9Gxv5vo2NfN/GRr5voyffs7E52/dN13Xa29vJy8vDYBh+xU1czuQYDIaBthChYrPZ5EU9SvI9Gxv5vo2NfN/GRr5voyffs7E50/dtJDM4/WThsRBCCCEmJElyhBBCCDEhSZITZFarlfvuuw+r1RrpUGKGfM/GRr5vYyPft7GR79voyfdsbIL5fYvLhcdCCCGEmPhkJkcIIYQQE5IkOUIIIYSYkCTJEUIIIcSEJEmOEEIIISYkSXKC6NFHH6W4uJiEhASWLVvGtm3bIh1SVPve976HpmmDPmbOnBnpsKLO22+/zcc//nHy8vLQNI2XX3550PO6rvPd736X3NxcEhMTWblyJYcPH45MsFFkuO/bLbfcctrrb/Xq1ZEJNkqsX7+eJUuWkJqaSnZ2NmvWrKGsrGzQMT09Pdx5551kZGSQkpLC2rVrqa+vj1DE0WEk37dLLrnktNfbF77whQhFHHm/+MUvmDdv3kDBvxUrVvC3v/1t4Plgvc4kyQmS559/nrvuuov77ruPnTt3Mn/+fFatWkVDQ0OkQ4tq55xzDrW1tQMf77zzTqRDijqdnZ3Mnz+fRx99dMjnf/zjH/Ozn/2Mxx57jK1bt5KcnMyqVavo6ekJc6TRZbjvG8Dq1asHvf5+97vfhTHC6PPWW29x5513smXLFjZs2IDX6+WjH/0onZ2dA8d87Wtf45VXXuGFF17grbfeoqamhmuuuSaCUUfeSL5vAHfccceg19uPf/zjCEUceQUFBfz3f/83O3bsYPv27Vx22WVcffXV7Nu3Dwji60wXQbF06VL9zjvvHPh/n8+n5+Xl6evXr49gVNHtvvvu0+fPnx/pMGIKoP/xj38c+H+/3687nU79Jz/5ycBjLpdLt1qt+u9+97sIRBidTv2+6bqu33zzzfrVV18dkXhiRUNDgw7ob731lq7r6rVlNpv1F154YeCYAwcO6IBeWloaqTCjzqnfN13X9Ysvvlj/yle+ErmgYkBaWpr+q1/9KqivM5nJCQKPx8OOHTtYuXLlwGMGg4GVK1dSWloawcii3+HDh8nLy2Py5MnccMMNVFZWRjqkmFJeXk5dXd2g157dbmfZsmXy2huBTZs2kZ2dzYwZM/jiF79Ic3NzpEOKKm63G4D09HQAduzYgdfrHfR6mzlzJkVFRfJ6O8mp37d+zzzzDJmZmcyZM4d7772Xrq6uSIQXdXw+H8899xydnZ2sWLEiqK+zuGzQGWxNTU34fD5ycnIGPZ6Tk8PBgwcjFFX0W7ZsGU899RQzZsygtraW73//+1x44YXs3buX1NTUSIcXE+rq6gCGfO31PyeGtnr1aq655hpKSko4evQo3/zmN7niiisoLS3FaDRGOryI8/v9fPWrX+X8889nzpw5gHq9WSwWHA7HoGPl9fahob5vAJ/97GeZNGkSeXl57Nmzh2984xuUlZXx0ksvRTDayPrggw9YsWIFPT09pKSk8Mc//pHZs2eze/fuoL3OJMkREXPFFVcMfD5v3jyWLVvGpEmT+P3vf8/tt98ewchEPPjMZz4z8PncuXOZN28eU6ZMYdOmTVx++eURjCw63Hnnnezdu1fWyY3Smb5vn//85wc+nzt3Lrm5uVx++eUcPXqUKVOmhDvMqDBjxgx2796N2+3mxRdf5Oabb+att94K6hhyuyoIMjMzMRqNp638rq+vx+l0Riiq2ONwOJg+fTpHjhyJdCgxo//1Ja+98Zs8eTKZmZny+gPWrVvHq6++yptvvklBQcHA406nE4/Hg8vlGnS8vN6UM33fhrJs2TKAuH69WSwWpk6dyqJFi1i/fj3z58/n4YcfDurrTJKcILBYLCxatIiNGzcOPOb3+9m4cSMrVqyIYGSxpaOjg6NHj5KbmxvpUGJGSUkJTqdz0Guvra2NrVu3ymtvlKqqqmhubo7r15+u66xbt44//vGPvPHGG5SUlAx6ftGiRZjN5kGvt7KyMiorK+P69Tbc920ou3fvBojr19up/H4/vb29wX2dBXdtdPx67rnndKvVqj/11FP6/v379c9//vO6w+HQ6+rqIh1a1Pr617+ub9q0SS8vL9ffffddfeXKlXpmZqbe0NAQ6dCiSnt7u75r1y59165dOqA/8MAD+q5du/Tjx4/ruq7r//3f/607HA79T3/6k75nzx796quv1ktKSvTu7u4IRx5ZZ/u+tbe36//+7/+ul5aW6uXl5frrr7+uL1y4UJ82bZre09MT6dAj5otf/KJut9v1TZs26bW1tQMfXV1dA8d84Qtf0IuKivQ33nhD3759u75ixQp9xYoVEYw68ob7vh05ckT/wQ9+oG/fvl0vLy/X//SnP+mTJ0/WL7rooghHHjn33HOP/tZbb+nl5eX6nj179HvuuUfXNE3/xz/+oet68F5nkuQE0c9//nO9qKhIt1gs+tKlS/UtW7ZEOqSodt111+m5ubm6xWLR8/Pz9euuu04/cuRIpMOKOm+++aYOnPZx880367qutpF/5zvf0XNycnSr1apffvnlellZWWSDjgJn+751dXXpH/3oR/WsrCzdbDbrkyZN0u+44464/6NkqO8XoD/55JMDx3R3d+v/9m//pqelpelJSUn6Jz/5Sb22tjZyQUeB4b5vlZWV+kUXXaSnp6frVqtVnzp1qn733Xfrbrc7soFH0G233aZPmjRJt1gselZWln755ZcPJDi6HrzXmabruj7GmSUhhBBCiKgla3KEEEIIMSFJkiOEEEKICUmSHCGEEEJMSJLkCCGEEGJCkiRHCCGEEBOSJDlCCCGEmJAkyRFCCCHEhCRJjhBCCCEmJElyhBBCCDEhSZIjhBBCiAlJkhwhhBBCTEiS5AghhBBiQvr/O7jzDvYHbroAAAAASUVORK5CYII="
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "metrics[[\"MAE\", \"MBE\"]].plot()"
    ]
diff --git a/notebooks/03. ForecastClient.ipynb b/notebooks/03. ForecastClient.ipynb
index f23e601..4407312 100644
--- a/notebooks/03. ForecastClient.ipynb	
+++ b/notebooks/03. ForecastClient.ipynb	
@@ -2,32 +2,8 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {
-    "pycharm": {
-     "is_executing": true
-    },
-    "ExecuteTime": {
-     "end_time": "2023-11-24T11:43:15.425217Z",
-     "start_time": "2023-11-24T11:43:15.207527Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "import os\n",
-    "\n",
-    "os.chdir(\"..\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2023-11-24T11:43:16.864992Z",
-     "start_time": "2023-11-24T11:43:15.221358Z"
-    }
-   },
+   "execution_count": null,
+   "metadata": {},
    "outputs": [],
    "source": [
     "import pandas as pd"
@@ -42,77 +18,40 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2023-11-24T11:43:18.870451Z",
-     "start_time": "2023-11-24T11:43:16.868394Z"
-    }
-   },
+   "execution_count": null,
+   "metadata": {},
    "outputs": [],
    "source": [
-    "from enfobench.dataset import DemandDataset\n",
+    "from enfobench.datasets import ElectricityDemandDataset\n",
     "\n",
-    "ds = DemandDataset(\"data\")"
+    "ds = ElectricityDemandDataset(\"../data/electricity-demand\")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": null,
    "outputs": [],
    "source": [
     "unique_ids = ds.metadata_subset.list_unique_ids()"
    ],
    "metadata": {
-    "collapsed": false,
-    "ExecuteTime": {
-     "end_time": "2023-11-24T11:43:18.925705Z",
-     "start_time": "2023-11-24T11:43:18.871955Z"
-    }
+    "collapsed": false
    }
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2023-11-24T11:43:18.940817Z",
-     "start_time": "2023-11-24T11:43:18.934191Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": "100"
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "len(unique_ids)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2023-11-24T11:43:18.982604Z",
-     "start_time": "2023-11-24T11:43:18.944480Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": "'70aec446c47486f3'"
-     },
-     "execution_count": 6,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "unique_id = unique_ids[0]\n",
     "unique_id"
@@ -120,214 +59,36 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": null,
    "outputs": [],
    "source": [
     "target, past_covariates, metadata = ds.get_data_by_unique_id(unique_id)"
    ],
    "metadata": {
-    "collapsed": false,
-    "ExecuteTime": {
-     "end_time": "2023-11-24T11:43:19.096547Z",
-     "start_time": "2023-11-24T11:43:18.953954Z"
-    }
+    "collapsed": false
    }
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "<class 'pandas.core.frame.DataFrame'>\n",
-      "DatetimeIndex: 24320 entries, 2012-10-09 08:30:00 to 2014-02-28 00:00:00\n",
-      "Data columns (total 1 columns):\n",
-      " #   Column  Non-Null Count  Dtype  \n",
-      "---  ------  --------------  -----  \n",
-      " 0   y       24317 non-null  float64\n",
-      "dtypes: float64(1)\n",
-      "memory usage: 380.0 KB\n"
-     ]
-    }
-   ],
+   "execution_count": null,
+   "outputs": [],
    "source": [
     "target.info()"
    ],
    "metadata": {
-    "collapsed": false,
-    "ExecuteTime": {
-     "end_time": "2023-11-24T11:43:19.163797Z",
-     "start_time": "2023-11-24T11:43:19.098606Z"
-    }
+    "collapsed": false
    }
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "<class 'pandas.core.frame.DataFrame'>\n",
-      "DatetimeIndex: 19862 entries, 2011-11-23 11:00:00 to 2014-02-28 00:00:00\n",
-      "Data columns (total 10 columns):\n",
-      " #   Column           Non-Null Count  Dtype  \n",
-      "---  ------           --------------  -----  \n",
-      " 0   temperature      19862 non-null  float32\n",
-      " 1   dew_point        19862 non-null  float32\n",
-      " 2   pressure         19862 non-null  float32\n",
-      " 3   wind_speed       19862 non-null  float32\n",
-      " 4   wind_gust        19862 non-null  float32\n",
-      " 5   wind_bearing     19862 non-null  float32\n",
-      " 6   precipitation    19862 non-null  float32\n",
-      " 7   snow             19862 non-null  float32\n",
-      " 8   cloud_cover      19862 non-null  float32\n",
-      " 9   solar_radiation  19862 non-null  float32\n",
-      "dtypes: float32(10)\n",
-      "memory usage: 931.0 KB\n"
-     ]
-    }
-   ],
+   "execution_count": null,
+   "outputs": [],
    "source": [
     "past_covariates.info()"
    ],
-   "metadata": {
-    "collapsed": false,
-    "ExecuteTime": {
-     "end_time": "2023-11-24T11:43:19.165733Z",
-     "start_time": "2023-11-24T11:43:19.124800Z"
-    }
-   }
-  },
-  {
-   "cell_type": "markdown",
    "metadata": {
     "collapsed": false
-   },
-   "source": [
-    "# Make external forecasts based on covariates"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {
-    "collapsed": false,
-    "pycharm": {
-     "is_executing": true
-    },
-    "ExecuteTime": {
-     "end_time": "2023-11-24T11:43:20.604799Z",
-     "start_time": "2023-11-24T11:43:19.144050Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "from enfobench.dataset.utils import create_perfect_forecasts_from_covariates\n",
-    "\n",
-    "perfect_forecasts = create_perfect_forecasts_from_covariates(\n",
-    "    past_covariates,\n",
-    "    horizon=pd.Timedelta(\"7 days\"),\n",
-    "    step=pd.Timedelta(\"24 hour\"),\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {
-    "collapsed": false,
-    "ExecuteTime": {
-     "end_time": "2023-11-24T11:43:20.671981Z",
-     "start_time": "2023-11-24T11:43:20.608265Z"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "<class 'pandas.core.frame.DataFrame'>\n",
-      "RangeIndex: 137928 entries, 0 to 137927\n",
-      "Data columns (total 12 columns):\n",
-      " #   Column           Non-Null Count   Dtype         \n",
-      "---  ------           --------------   -----         \n",
-      " 0   timestamp        137928 non-null  datetime64[ns]\n",
-      " 1   cutoff_date      137928 non-null  datetime64[ns]\n",
-      " 2   temperature      137928 non-null  float32       \n",
-      " 3   dew_point        137928 non-null  float32       \n",
-      " 4   pressure         137928 non-null  float32       \n",
-      " 5   wind_speed       137928 non-null  float32       \n",
-      " 6   wind_gust        137928 non-null  float32       \n",
-      " 7   wind_bearing     137928 non-null  float32       \n",
-      " 8   precipitation    137928 non-null  float32       \n",
-      " 9   snow             137928 non-null  float32       \n",
-      " 10  cloud_cover      137928 non-null  float32       \n",
-      " 11  solar_radiation  137928 non-null  float32       \n",
-      "dtypes: datetime64[ns](2), float32(10)\n",
-      "memory usage: 7.4 MB\n"
-     ]
-    }
-   ],
-   "source": [
-    "perfect_forecasts.info()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {
-    "collapsed": false,
-    "pycharm": {
-     "is_executing": true
-    },
-    "ExecuteTime": {
-     "end_time": "2023-11-24T11:43:20.692912Z",
-     "start_time": "2023-11-24T11:43:20.629576Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": "            timestamp         cutoff_date  temperature  dew_point  \\\n0 2011-11-23 12:00:00 2011-11-23 11:00:00     8.515167   6.053314   \n1 2011-11-23 13:00:00 2011-11-23 11:00:00     9.366150   6.898102   \n2 2011-11-23 14:00:00 2011-11-23 11:00:00     9.818268   7.336670   \n3 2011-11-23 15:00:00 2011-11-23 11:00:00     9.655426   7.545349   \n4 2011-11-23 16:00:00 2011-11-23 11:00:00     9.179779   7.419464   \n\n      pressure  wind_speed  wind_gust  wind_bearing  precipitation  snow  \\\n0  1018.570374    3.163105   7.092710    227.930420  -2.328306e-07   0.0   \n1  1018.497498    3.694861   7.186031    235.672974  -2.328306e-07   0.0   \n2  1018.578125    3.870760   6.684762    234.186523  -2.328306e-07   0.0   \n3  1018.738159    3.420094   5.839092    232.220032  -2.328306e-07   0.0   \n4  1018.985535    3.134620   5.201840    221.905396  -2.328306e-07   0.0   \n\n   cloud_cover  solar_radiation  \n0          1.0       247.303711  \n1          1.0       219.641495  \n2          1.0       151.042435  \n3          1.0        68.137222  \n4          1.0        15.961771  ",
-      "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>timestamp</th>\n      <th>cutoff_date</th>\n      <th>temperature</th>\n      <th>dew_point</th>\n      <th>pressure</th>\n      <th>wind_speed</th>\n      <th>wind_gust</th>\n      <th>wind_bearing</th>\n      <th>precipitation</th>\n      <th>snow</th>\n      <th>cloud_cover</th>\n      <th>solar_radiation</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>2011-11-23 12:00:00</td>\n      <td>2011-11-23 11:00:00</td>\n      <td>8.515167</td>\n      <td>6.053314</td>\n      <td>1018.570374</td>\n      <td>3.163105</td>\n      <td>7.092710</td>\n      <td>227.930420</td>\n      <td>-2.328306e-07</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>247.303711</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>2011-11-23 13:00:00</td>\n      <td>2011-11-23 11:00:00</td>\n      <td>9.366150</td>\n      <td>6.898102</td>\n      <td>1018.497498</td>\n      <td>3.694861</td>\n      <td>7.186031</td>\n      <td>235.672974</td>\n      <td>-2.328306e-07</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>219.641495</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>2011-11-23 14:00:00</td>\n      <td>2011-11-23 11:00:00</td>\n      <td>9.818268</td>\n      <td>7.336670</td>\n      <td>1018.578125</td>\n      <td>3.870760</td>\n      <td>6.684762</td>\n      <td>234.186523</td>\n      <td>-2.328306e-07</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>151.042435</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>2011-11-23 15:00:00</td>\n      <td>2011-11-23 11:00:00</td>\n      <td>9.655426</td>\n      <td>7.545349</td>\n      <td>1018.738159</td>\n      <td>3.420094</td>\n      <td>5.839092</td>\n      <td>232.220032</td>\n      <td>-2.328306e-07</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>68.137222</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>2011-11-23 16:00:00</td>\n      <td>2011-11-23 11:00:00</td>\n      <td>9.179779</td>\n      <td>7.419464</td>\n      <td>1018.985535</td>\n      <td>3.134620</td>\n      <td>5.201840</td>\n      <td>221.905396</td>\n      <td>-2.328306e-07</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>15.961771</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
-     },
-     "execution_count": 12,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "perfect_forecasts.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {
-    "collapsed": false,
-    "ExecuteTime": {
-     "end_time": "2023-11-24T11:43:20.694911Z",
-     "start_time": "2023-11-24T11:43:20.677990Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": "                 timestamp         cutoff_date  temperature  dew_point  \\\n137923 2014-02-27 07:00:00 2014-02-20 11:00:00     6.506134   5.168854   \n137924 2014-02-27 08:00:00 2014-02-20 11:00:00     6.726654   5.417999   \n137925 2014-02-27 09:00:00 2014-02-20 11:00:00     7.474091   5.614899   \n137926 2014-02-27 10:00:00 2014-02-20 11:00:00     7.983521   4.091736   \n137927 2014-02-27 11:00:00 2014-02-20 11:00:00     8.863586   3.427490   \n\n          pressure  wind_speed  wind_gust  wind_bearing  precipitation  \\\n137923  995.314026    8.122930  15.462187    188.039062       1.058658   \n137924  995.132080    6.130317  11.876844    218.965332       0.727250   \n137925  996.073792    6.359778  12.091746    265.768311       0.092959   \n137926  996.695862    6.367825  12.284778    273.792236       0.002462   \n137927  997.391602    6.639326  13.095889    275.929749       0.002462   \n\n                snow  cloud_cover  solar_radiation  \n137923  2.910383e-08     1.000000         0.000000  \n137924  2.910383e-08     1.000000         5.516424  \n137925  2.910383e-08     0.962584        37.017605  \n137926  2.910383e-08     0.443670       203.822159  \n137927  2.910383e-08     0.358461       357.286163  ",
-      "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>timestamp</th>\n      <th>cutoff_date</th>\n      <th>temperature</th>\n      <th>dew_point</th>\n      <th>pressure</th>\n      <th>wind_speed</th>\n      <th>wind_gust</th>\n      <th>wind_bearing</th>\n      <th>precipitation</th>\n      <th>snow</th>\n      <th>cloud_cover</th>\n      <th>solar_radiation</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>137923</th>\n      <td>2014-02-27 07:00:00</td>\n      <td>2014-02-20 11:00:00</td>\n      <td>6.506134</td>\n      <td>5.168854</td>\n      <td>995.314026</td>\n      <td>8.122930</td>\n      <td>15.462187</td>\n      <td>188.039062</td>\n      <td>1.058658</td>\n      <td>2.910383e-08</td>\n      <td>1.000000</td>\n      <td>0.000000</td>\n    </tr>\n    <tr>\n      <th>137924</th>\n      <td>2014-02-27 08:00:00</td>\n      <td>2014-02-20 11:00:00</td>\n      <td>6.726654</td>\n      <td>5.417999</td>\n      <td>995.132080</td>\n      <td>6.130317</td>\n      <td>11.876844</td>\n      <td>218.965332</td>\n      <td>0.727250</td>\n      <td>2.910383e-08</td>\n      <td>1.000000</td>\n      <td>5.516424</td>\n    </tr>\n    <tr>\n      <th>137925</th>\n      <td>2014-02-27 09:00:00</td>\n      <td>2014-02-20 11:00:00</td>\n      <td>7.474091</td>\n      <td>5.614899</td>\n      <td>996.073792</td>\n      <td>6.359778</td>\n      <td>12.091746</td>\n      <td>265.768311</td>\n      <td>0.092959</td>\n      <td>2.910383e-08</td>\n      <td>0.962584</td>\n      <td>37.017605</td>\n    </tr>\n    <tr>\n      <th>137926</th>\n      <td>2014-02-27 10:00:00</td>\n      <td>2014-02-20 11:00:00</td>\n      <td>7.983521</td>\n      <td>4.091736</td>\n      <td>996.695862</td>\n      <td>6.367825</td>\n      <td>12.284778</td>\n      <td>273.792236</td>\n      <td>0.002462</td>\n      <td>2.910383e-08</td>\n      <td>0.443670</td>\n      <td>203.822159</td>\n    </tr>\n    <tr>\n      <th>137927</th>\n      <td>2014-02-27 11:00:00</td>\n      <td>2014-02-20 11:00:00</td>\n      <td>8.863586</td>\n      <td>3.427490</td>\n      <td>997.391602</td>\n      <td>6.639326</td>\n      <td>13.095889</td>\n      <td>275.929749</td>\n      <td>0.002462</td>\n      <td>2.910383e-08</td>\n      <td>0.358461</td>\n      <td>357.286163</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
-     },
-     "execution_count": 13,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "perfect_forecasts.tail()"
-   ]
+   }
   },
   {
    "cell_type": "markdown",
@@ -338,21 +99,15 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2023-11-24T11:43:20.849864Z",
-     "start_time": "2023-11-24T11:43:20.704929Z"
-    }
-   },
+   "execution_count": null,
+   "metadata": {},
    "outputs": [],
    "source": [
-    "from enfobench.dataset import Dataset\n",
+    "from enfobench import Dataset\n",
     "\n",
     "dataset = Dataset(\n",
-    "    target=target.loc[\"2012-01-01\":\"2013-12-31\"],\n",
+    "    target=target,\n",
     "    past_covariates=past_covariates,\n",
-    "    future_covariates=perfect_forecasts,\n",
     "    metadata=metadata,\n",
     ")"
    ]
@@ -366,13 +121,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2023-11-24T11:43:20.885761Z",
-     "start_time": "2023-11-24T11:43:20.732620Z"
-    }
-   },
+   "execution_count": null,
+   "metadata": {},
    "outputs": [],
    "source": [
     "from enfobench.evaluation import ForecastClient\n",
@@ -382,54 +132,28 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
-   "outputs": [
-    {
-     "data": {
-      "text/plain": "ModelInfo(name='Darts.Theta', authors=[{'name': 'Attila Balint', 'email': 'attila.balint@kuleuven.be'}], type='point', params={})"
-     },
-     "execution_count": 16,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "execution_count": null,
+   "outputs": [],
    "source": [
     "client.info()"
    ],
    "metadata": {
-    "collapsed": false,
-    "ExecuteTime": {
-     "end_time": "2023-11-24T11:43:20.914210Z",
-     "start_time": "2023-11-24T11:43:20.739029Z"
-    }
+    "collapsed": false
    }
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2023-11-24T11:44:27.440701Z",
-     "start_time": "2023-11-24T11:44:23.555380Z"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "100%|██████████| 30/30 [00:03<00:00,  7.80it/s]\n"
-     ]
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "from enfobench.evaluation import cross_validate\n",
     "\n",
     "crossval_df = cross_validate(\n",
     "    client,\n",
     "    dataset,\n",
-    "    start_date=pd.Timestamp(\"2013-01-01T00:00:00\"),\n",
-    "    end_date=pd.Timestamp(\"2013-02-01T00:00:00\"),\n",
+    "    start_date=pd.Timestamp(\"2023-01-01T00:00:00\"),\n",
+    "    end_date=pd.Timestamp(\"2023-02-01T00:00:00\"),\n",
     "    horizon=pd.Timedelta(\"38 hours\"),\n",
     "    step=pd.Timedelta(\"1 day\"),\n",
     ")"
@@ -437,61 +161,25 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2023-11-24T11:44:28.476867Z",
-     "start_time": "2023-11-24T11:44:28.442537Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": "     cutoff_date           timestamp      yhat      y\n0     2013-01-01 2013-01-01 00:30:00  0.194672  0.303\n1     2013-01-01 2013-01-01 01:00:00  0.168339  0.085\n2     2013-01-01 2013-01-01 01:30:00  0.110898  0.270\n3     2013-01-01 2013-01-01 02:00:00  0.090181  0.084\n4     2013-01-01 2013-01-01 02:30:00  0.082136  0.184\n...          ...                 ...       ...    ...\n2275  2013-01-30 2013-01-31 12:00:00  0.217432  0.086\n2276  2013-01-30 2013-01-31 12:30:00  0.265756  0.095\n2277  2013-01-30 2013-01-31 13:00:00  0.227152  0.024\n2278  2013-01-30 2013-01-31 13:30:00  0.222331  0.023\n2279  2013-01-30 2013-01-31 14:00:00  0.245075  0.055\n\n[2280 rows x 4 columns]",
-      "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>cutoff_date</th>\n      <th>timestamp</th>\n      <th>yhat</th>\n      <th>y</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>2013-01-01</td>\n      <td>2013-01-01 00:30:00</td>\n      <td>0.194672</td>\n      <td>0.303</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>2013-01-01</td>\n      <td>2013-01-01 01:00:00</td>\n      <td>0.168339</td>\n      <td>0.085</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>2013-01-01</td>\n      <td>2013-01-01 01:30:00</td>\n      <td>0.110898</td>\n      <td>0.270</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>2013-01-01</td>\n      <td>2013-01-01 02:00:00</td>\n      <td>0.090181</td>\n      <td>0.084</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>2013-01-01</td>\n      <td>2013-01-01 02:30:00</td>\n      <td>0.082136</td>\n      <td>0.184</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>2275</th>\n      <td>2013-01-30</td>\n      <td>2013-01-31 12:00:00</td>\n      <td>0.217432</td>\n      <td>0.086</td>\n    </tr>\n    <tr>\n      <th>2276</th>\n      <td>2013-01-30</td>\n      <td>2013-01-31 12:30:00</td>\n      <td>0.265756</td>\n      <td>0.095</td>\n    </tr>\n    <tr>\n      <th>2277</th>\n      <td>2013-01-30</td>\n      <td>2013-01-31 13:00:00</td>\n      <td>0.227152</td>\n      <td>0.024</td>\n    </tr>\n    <tr>\n      <th>2278</th>\n      <td>2013-01-30</td>\n      <td>2013-01-31 13:30:00</td>\n      <td>0.222331</td>\n      <td>0.023</td>\n    </tr>\n    <tr>\n      <th>2279</th>\n      <td>2013-01-30</td>\n      <td>2013-01-31 14:00:00</td>\n      <td>0.245075</td>\n      <td>0.055</td>\n    </tr>\n  </tbody>\n</table>\n<p>2280 rows × 4 columns</p>\n</div>"
-     },
-     "execution_count": 19,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "crossval_df"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
-   "outputs": [
-    {
-     "data": {
-      "text/plain": "<Axes: xlabel='timestamp'>"
-     },
-     "execution_count": 26,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "text/plain": "<Figure size 640x480 with 1 Axes>",
-      "image/png": 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"
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "execution_count": null,
+   "outputs": [],
    "source": [
-    "cutoff_date_to_plot = \"2013-01-02\"\n",
-    "crossval_df.loc[crossval_df.cutoff_date == cutoff_date_to_plot].set_index(\n",
-    "    \"timestamp\"\n",
-    ").drop(columns=[\"cutoff_date\"]).plot()"
+    "cutoff_date_to_plot = crossval_df.cutoff_date.unique()[0]\n",
+    "crossval_df.loc[crossval_df.cutoff_date == cutoff_date_to_plot].set_index(\"timestamp\").drop(\n",
+    "    columns=[\"cutoff_date\"]\n",
+    ").plot()"
    ],
    "metadata": {
-    "collapsed": false,
-    "ExecuteTime": {
-     "end_time": "2023-11-24T11:45:42.992331Z",
-     "start_time": "2023-11-24T11:45:42.647244Z"
-    }
+    "collapsed": false
    }
   },
   {
@@ -503,13 +191,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2023-11-24T11:44:31.063371Z",
-     "start_time": "2023-11-24T11:44:31.050061Z"
-    }
-   },
+   "execution_count": null,
+   "metadata": {},
    "outputs": [],
    "source": [
     "from enfobench.evaluation import evaluate_metrics\n",
@@ -518,18 +201,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
-   "outputs": [
-    {
-     "data": {
-      "text/plain": "      MAE       MBE  weight\n0  0.1004  0.017568     1.0",
-      "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>MAE</th>\n      <th>MBE</th>\n      <th>weight</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>0.1004</td>\n      <td>0.017568</td>\n      <td>1.0</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
-     },
-     "execution_count": 21,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "execution_count": null,
+   "outputs": [],
    "source": [
     "evaluate_metrics(\n",
     "    crossval_df,\n",
@@ -537,31 +210,14 @@
     ")"
    ],
    "metadata": {
-    "collapsed": false,
-    "ExecuteTime": {
-     "end_time": "2023-11-24T11:44:31.238005Z",
-     "start_time": "2023-11-24T11:44:31.215670Z"
-    }
+    "collapsed": false
    }
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2023-11-24T11:44:31.592840Z",
-     "start_time": "2023-11-24T11:44:31.421100Z"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "100%|██████████| 30/30 [00:00<00:00, 193.83it/s]\n"
-     ]
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "metrics = evaluate_metrics(\n",
     "    crossval_df,\n",
@@ -572,69 +228,28 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
-   "outputs": [
-    {
-     "data": {
-      "text/plain": "   cutoff_date       MAE       MBE  weight\n0   2013-01-01  0.094230  0.021399     1.0\n1   2013-01-02  0.142394  0.054963     1.0\n2   2013-01-03  0.150956 -0.014586     1.0\n3   2013-01-04  0.105359 -0.090180     1.0\n4   2013-01-05  0.138043  0.025005     1.0\n5   2013-01-06  0.096615  0.034839     1.0\n6   2013-01-07  0.100207  0.094516     1.0\n7   2013-01-08  0.082602 -0.043714     1.0\n8   2013-01-09  0.082081  0.050025     1.0\n9   2013-01-10  0.107549  0.001221     1.0\n10  2013-01-11  0.126615  0.022922     1.0\n11  2013-01-12  0.097166 -0.024386     1.0\n12  2013-01-13  0.123135  0.036482     1.0\n13  2013-01-14  0.083344  0.023064     1.0\n14  2013-01-15  0.075436  0.031968     1.0\n15  2013-01-16  0.101842 -0.035673     1.0\n16  2013-01-17  0.086418 -0.003678     1.0\n17  2013-01-18  0.135311  0.084602     1.0\n18  2013-01-19  0.083080  0.036888     1.0\n19  2013-01-20  0.097200  0.016727     1.0\n20  2013-01-21  0.059232 -0.008563     1.0\n21  2013-01-22  0.099850  0.048781     1.0\n22  2013-01-23  0.111657  0.061347     1.0\n23  2013-01-24  0.078152  0.058105     1.0\n24  2013-01-25  0.108309 -0.029089     1.0\n25  2013-01-26  0.110318 -0.003294     1.0\n26  2013-01-27  0.088046 -0.015784     1.0\n27  2013-01-28  0.078470  0.013201     1.0\n28  2013-01-29  0.063541  0.003157     1.0\n29  2013-01-30  0.104846  0.076787     1.0",
-      "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>cutoff_date</th>\n      <th>MAE</th>\n      <th>MBE</th>\n      <th>weight</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>2013-01-01</td>\n      <td>0.094230</td>\n      <td>0.021399</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>2013-01-02</td>\n      <td>0.142394</td>\n      <td>0.054963</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>2013-01-03</td>\n      <td>0.150956</td>\n      <td>-0.014586</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>2013-01-04</td>\n      <td>0.105359</td>\n      <td>-0.090180</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>2013-01-05</td>\n      <td>0.138043</td>\n      <td>0.025005</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>2013-01-06</td>\n      <td>0.096615</td>\n      <td>0.034839</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>2013-01-07</td>\n      <td>0.100207</td>\n      <td>0.094516</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>2013-01-08</td>\n      <td>0.082602</td>\n      <td>-0.043714</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td>2013-01-09</td>\n      <td>0.082081</td>\n      <td>0.050025</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>2013-01-10</td>\n      <td>0.107549</td>\n      <td>0.001221</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>10</th>\n      <td>2013-01-11</td>\n      <td>0.126615</td>\n      <td>0.022922</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>11</th>\n      <td>2013-01-12</td>\n      <td>0.097166</td>\n      <td>-0.024386</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>12</th>\n      <td>2013-01-13</td>\n      <td>0.123135</td>\n      <td>0.036482</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>13</th>\n      <td>2013-01-14</td>\n      <td>0.083344</td>\n      <td>0.023064</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>14</th>\n      <td>2013-01-15</td>\n      <td>0.075436</td>\n      <td>0.031968</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>15</th>\n      <td>2013-01-16</td>\n      <td>0.101842</td>\n      <td>-0.035673</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>16</th>\n      <td>2013-01-17</td>\n      <td>0.086418</td>\n      <td>-0.003678</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>17</th>\n      <td>2013-01-18</td>\n      <td>0.135311</td>\n      <td>0.084602</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>18</th>\n      <td>2013-01-19</td>\n      <td>0.083080</td>\n      <td>0.036888</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>19</th>\n      <td>2013-01-20</td>\n      <td>0.097200</td>\n      <td>0.016727</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>20</th>\n      <td>2013-01-21</td>\n      <td>0.059232</td>\n      <td>-0.008563</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>21</th>\n      <td>2013-01-22</td>\n      <td>0.099850</td>\n      <td>0.048781</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>22</th>\n      <td>2013-01-23</td>\n      <td>0.111657</td>\n      <td>0.061347</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>23</th>\n      <td>2013-01-24</td>\n      <td>0.078152</td>\n      <td>0.058105</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>24</th>\n      <td>2013-01-25</td>\n      <td>0.108309</td>\n      <td>-0.029089</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>25</th>\n      <td>2013-01-26</td>\n      <td>0.110318</td>\n      <td>-0.003294</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>26</th>\n      <td>2013-01-27</td>\n      <td>0.088046</td>\n      <td>-0.015784</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>27</th>\n      <td>2013-01-28</td>\n      <td>0.078470</td>\n      <td>0.013201</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>28</th>\n      <td>2013-01-29</td>\n      <td>0.063541</td>\n      <td>0.003157</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>29</th>\n      <td>2013-01-30</td>\n      <td>0.104846</td>\n      <td>0.076787</td>\n      <td>1.0</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
-     },
-     "execution_count": 23,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "execution_count": null,
+   "outputs": [],
    "source": [
     "metrics"
    ],
    "metadata": {
-    "collapsed": false,
-    "ExecuteTime": {
-     "end_time": "2023-11-24T11:44:31.969798Z",
-     "start_time": "2023-11-24T11:44:31.951304Z"
-    }
+    "collapsed": false
    }
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2023-11-24T11:44:34.162283Z",
-     "start_time": "2023-11-24T11:44:32.821678Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": "<Axes: >"
-     },
-     "execution_count": 24,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "text/plain": "<Figure size 640x480 with 1 Axes>",
-      "image/png": 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"
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "metrics[[\"MAE\", \"MBE\"]].plot()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2023-11-16T15:00:48.958286Z",
-     "start_time": "2023-11-16T15:00:48.949665Z"
-    }
-   },
+   "execution_count": null,
+   "metadata": {},
    "outputs": [],
    "source": []
   }