sktime-intro.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src=\"https://github.com/alan-turing-institute/sktime/blob/main/docs/source/images/sktime-logo-no-text.jpg?raw=true\" alt=\"drawing\" width=\"400\"/>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# An brief introduction to sktime\n",
    "Find out more at: https://github.com/alan-turing-institute/sktime"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "## A––Cross-sectional classification with scikit-learn\n",
    "\n",
    "### A.1––Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.datasets import load_wine\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(178, 13) (178,)\n"
     ]
    }
   ],
   "source": [
    "wine = load_wine()\n",
    "X = wine[\"data\"] \n",
    "y = wine[\"target\"]\n",
    "print(X.shape, y.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>alcohol</th>\n",
       "      <th>malic_acid</th>\n",
       "      <th>ash</th>\n",
       "      <th>alcalinity_of_ash</th>\n",
       "      <th>magnesium</th>\n",
       "      <th>total_phenols</th>\n",
       "      <th>flavanoids</th>\n",
       "      <th>nonflavanoid_phenols</th>\n",
       "      <th>proanthocyanins</th>\n",
       "      <th>color_intensity</th>\n",
       "      <th>hue</th>\n",
       "      <th>od280/od315_of_diluted_wines</th>\n",
       "      <th>proline</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>14.23</td>\n",
       "      <td>1.71</td>\n",
       "      <td>2.43</td>\n",
       "      <td>15.6</td>\n",
       "      <td>127.0</td>\n",
       "      <td>2.80</td>\n",
       "      <td>3.06</td>\n",
       "      <td>0.28</td>\n",
       "      <td>2.29</td>\n",
       "      <td>5.64</td>\n",
       "      <td>1.04</td>\n",
       "      <td>3.92</td>\n",
       "      <td>1065.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>13.20</td>\n",
       "      <td>1.78</td>\n",
       "      <td>2.14</td>\n",
       "      <td>11.2</td>\n",
       "      <td>100.0</td>\n",
       "      <td>2.65</td>\n",
       "      <td>2.76</td>\n",
       "      <td>0.26</td>\n",
       "      <td>1.28</td>\n",
       "      <td>4.38</td>\n",
       "      <td>1.05</td>\n",
       "      <td>3.40</td>\n",
       "      <td>1050.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>13.16</td>\n",
       "      <td>2.36</td>\n",
       "      <td>2.67</td>\n",
       "      <td>18.6</td>\n",
       "      <td>101.0</td>\n",
       "      <td>2.80</td>\n",
       "      <td>3.24</td>\n",
       "      <td>0.30</td>\n",
       "      <td>2.81</td>\n",
       "      <td>5.68</td>\n",
       "      <td>1.03</td>\n",
       "      <td>3.17</td>\n",
       "      <td>1185.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>14.37</td>\n",
       "      <td>1.95</td>\n",
       "      <td>2.50</td>\n",
       "      <td>16.8</td>\n",
       "      <td>113.0</td>\n",
       "      <td>3.85</td>\n",
       "      <td>3.49</td>\n",
       "      <td>0.24</td>\n",
       "      <td>2.18</td>\n",
       "      <td>7.80</td>\n",
       "      <td>0.86</td>\n",
       "      <td>3.45</td>\n",
       "      <td>1480.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>13.24</td>\n",
       "      <td>2.59</td>\n",
       "      <td>2.87</td>\n",
       "      <td>21.0</td>\n",
       "      <td>118.0</td>\n",
       "      <td>2.80</td>\n",
       "      <td>2.69</td>\n",
       "      <td>0.39</td>\n",
       "      <td>1.82</td>\n",
       "      <td>4.32</td>\n",
       "      <td>1.04</td>\n",
       "      <td>2.93</td>\n",
       "      <td>735.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   alcohol  malic_acid   ash  alcalinity_of_ash  magnesium  total_phenols  \\\n",
       "0    14.23        1.71  2.43               15.6      127.0           2.80   \n",
       "1    13.20        1.78  2.14               11.2      100.0           2.65   \n",
       "2    13.16        2.36  2.67               18.6      101.0           2.80   \n",
       "3    14.37        1.95  2.50               16.8      113.0           3.85   \n",
       "4    13.24        2.59  2.87               21.0      118.0           2.80   \n",
       "\n",
       "   flavanoids  nonflavanoid_phenols  proanthocyanins  color_intensity   hue  \\\n",
       "0        3.06                  0.28             2.29             5.64  1.04   \n",
       "1        2.76                  0.26             1.28             4.38  1.05   \n",
       "2        3.24                  0.30             2.81             5.68  1.03   \n",
       "3        3.49                  0.24             2.18             7.80  0.86   \n",
       "4        2.69                  0.39             1.82             4.32  1.04   \n",
       "\n",
       "   od280/od315_of_diluted_wines  proline  \n",
       "0                          3.92   1065.0  \n",
       "1                          3.40   1050.0  \n",
       "2                          3.17   1185.0  \n",
       "3                          3.45   1480.0  \n",
       "4                          2.93    735.0  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(X, columns=wine[\"feature_names\"]).head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([59, 71, 48])"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "labels, counts = np.unique(y, return_counts=True)\n",
    "counts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1)\n",
    "ax.scatter(X[:, 0], X[:, 1], c=y)\n",
    "ax.set(xlabel=wine[\"feature_names\"][0], ylabel=wine[\"feature_names\"][1]);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### A.2––Learning process\n",
    "\n",
    "Common interface for:\n",
    "* Model specification\n",
    "* Fitting\n",
    "* Prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import accuracy_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(133, 13) (45, 13) (133,) (45,)\n"
     ]
    }
   ],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y)\n",
    "print(X_train.shape, X_test.shape, y_train.shape, y_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier(n_estimators=10, n_jobs=10)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "classifier = RandomForestClassifier(n_estimators=10, n_jobs=10)\n",
    "classifier.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = classifier.predict(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### A.3––Evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9777777777777777"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy_score(y_test, y_pred)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### A.3––Composition\n",
    "* Pipelining & transformers\n",
    "* Tuning\n",
    "* Ensembling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.pipeline import make_pipeline\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.preprocessing import StandardScaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pipeline(steps=[('standardscaler', StandardScaler()),\n",
       "                ('kneighborsclassifier', KNeighborsClassifier())])"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pipeline = make_pipeline(StandardScaler(), KNeighborsClassifier())\n",
    "pipeline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9777777777777777"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pipeline.fit(X_train, y_train)\n",
    "pipeline.predict(X_test)\n",
    "accuracy_score(y_test, y_pred)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "## B––Scientific typing\n",
    "\n",
    "* How can we generalize the design principles of scikit-learn and other state-of-the-art toolboxes for cross-sectional data to the time series domain?  \n",
    "* What are meaningful categories for algorithms in ML toolboxes?\n",
    "* How do we encapsulate them? \n",
    "* How do we define their interfaces?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# cross-sectional classification algorithm\n",
    "from sklearn.base import is_classifier\n",
    "is_classifier(RandomForestClassifier())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### B.1––Learning tasks \n",
    "\n",
    "* Data (relational structure and assumed statistical data-generative setting)\n",
    "* Learning process (at what point does the algorithm take in what data and what does it return?)\n",
    "* Evaluation (how do we know we have solved the task)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### B.2––Scientific types for learning algorithms\n",
    "* counterparts to learning tasks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "import inspect\n",
    "\n",
    "def _is_public_method(method):\n",
    "    return inspect.ismethod(method) and not method.__name__.startswith(\"_\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['apply',\n",
       " 'decision_path',\n",
       " 'fit',\n",
       " 'get_params',\n",
       " 'predict',\n",
       " 'predict_log_proba',\n",
       " 'predict_proba',\n",
       " 'score',\n",
       " 'set_params']"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "[name for name, method in inspect.getmembers(RandomForestClassifier(), predicate=_is_public_method)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['RandomForestClassifier',\n",
       " 'ForestClassifier',\n",
       " 'ClassifierMixin',\n",
       " 'BaseForest',\n",
       " 'MultiOutputMixin',\n",
       " 'BaseEnsemble',\n",
       " 'MetaEstimatorMixin',\n",
       " 'BaseEstimator',\n",
       " 'object']"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# inheritance\n",
    "[klass.__name__ for klass in inspect.getmro(RandomForestClassifier)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['get_params', 'set_params']"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.base import BaseEstimator\n",
    "[name for name, method in inspect.getmembers(BaseEstimator(), predicate=_is_public_method)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Find out more: https://arxiv.org/pdf/2101.04938.pdf\n",
    "\n",
    "---\n",
    "## C––Time series classification with sktime\n",
    "\n",
    "### C.1––Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(158, 1, 251) (158,) (53, 1, 251) (53,)\n"
     ]
    }
   ],
   "source": [
    "from sktime.datasets import load_arrow_head\n",
    "from sktime.utils.data_processing import from_nested_to_3d_numpy\n",
    "\n",
    "X, y = load_arrow_head(return_X_y=True)\n",
    "X = from_nested_to_3d_numpy(X)\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y)\n",
    "print(X_train.shape, y_train.shape, X_test.shape, y_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "labels, counts = np.unique(y_train, return_counts=True)\n",
    "fig, ax = plt.subplots(1, figsize=plt.figaspect(0.25))\n",
    "for label in labels:\n",
    "    ax.plot(X_train[y_train == label, 0, :][0], label=f\"class {label}\")\n",
    "ax.set(ylabel=\"Scaled distance from midpoint\", xlabel=\"Index\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### C.2––Learning process"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sktime.classification.kernel_based import ROCKETClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "# time-series classification algorithm \n",
    "classifier = ROCKETClassifier()\n",
    "classifier.fit(X_train, y_train)\n",
    "y_pred = classifier.predict(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### C.3––Evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9622641509433962"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy_score(y_test, y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Arsenal',\n",
       " 'BOSSEnsemble',\n",
       " 'CanonicalIntervalForest',\n",
       " 'Catch22ForestClassifier',\n",
       " 'ColumnEnsembleClassifier',\n",
       " 'ComposableTimeSeriesForestClassifier',\n",
       " 'ContractableBOSS',\n",
       " 'DrCIF',\n",
       " 'ElasticEnsemble',\n",
       " 'HIVECOTEV1',\n",
       " 'IndividualBOSS',\n",
       " 'IndividualTDE',\n",
       " 'KNeighborsTimeSeriesClassifier',\n",
       " 'MUSE',\n",
       " 'MrSEQLClassifier',\n",
       " 'ProximityForest',\n",
       " 'ProximityStump',\n",
       " 'ProximityTree',\n",
       " 'ROCKETClassifier',\n",
       " 'RandomIntervalSpectralForest',\n",
       " 'ShapeDTW',\n",
       " 'ShapeletTransformClassifier',\n",
       " 'SignatureClassifier',\n",
       " 'SupervisedTimeSeriesForest',\n",
       " 'TemporalDictionaryEnsemble',\n",
       " 'TimeSeriesForestClassifier',\n",
       " 'WEASEL']"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sktime.utils import all_estimators\n",
    "[name for name, klass in all_estimators(\"classifier\")]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "## D––Forecasting with sktime\n",
    "\n",
    "### D.1––Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sktime.datasets import load_airline, load_longley\n",
    "from sktime.utils.plotting import plot_series"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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HA792jp8HPG2MMdZa66vrEhERETkR1lpKq9wNhNpDQdfbXlbl/s6x8ZEhJEaHk9KpPWNSYo8Itd7H4SccVuMjQlk4dRTj690DPP/WUcxbs4eHP/mGmct38+crBnDtkK4KaE3AWsvTX+7kZ+9soGuHMD6/90xOT4rxd1kiAcuXI8C9gEJgpjFmKLASuB/4EbDYGPNnwAWMdt7fHe8I7yE5Tlut8/jo9kPH7Aaw1rqNMaVALFBUvxBjzDS8I8j07Nmzaa5ORERE2rzvWy3ZWktxRe2RI7WlVezZX8Xuevfdlh81vdgYSOgQSmJ0OGlxkZzfN+7wFOXE6HB6dAynW3QoocFNPxLrchkGJUSxdMaYI65rePdoLkyL50cLNzBx1krO7R3L4+MHMaRb1PefVBpUWlnLHXPX8tb6PC7v34WXrh9GJ015FvErXwbgYGAEMN1au8wY8wTwIN5R3x9ba98yxkwEXgQuABr6EaNtpJ3vee3bBmufB54HSE9P1+iwiIiIfC+Px5KZX3bESOmcKSP4z5YCPttRfHgEt8rtOeI4l4FuUd7R2cFdoxjXL947Yltv5LZbdBjtglx+ujJvCG5owatz+3Qm40dn8Y9lu/jl+5sZ8dhn3HlGMr+9OI3YCAW3E7EqZz+TZq1kZ0klj17Wn5+e01urbIu0AL4MwDlAjrV2mfN8Ht4APAbvSDDAm3gXyTr0/h71jk/EOz06x3l8dHv9Y3KMMcF4w3Vx016GiIiIBKLcsqrD4Re8C0XdMHsVf7tmMB9uK2JkYjRXDuxyxHTkHh3D6BIZSrAfw+2pCg5ycffoZCYN68b/vr+F55bu5I01e/jtxf2YdnrPVn1tzcFay7NfZfOTRRuIiwzh07tHc2ZKJ3+XJSIOnwVga22+MWa3MSbNWrsFGAtsxDs1+hzgU+B8YJtzyCJgjjHmr3gXweoLLLfW1hljDhhjTgeWATcDT9U75hZgKTAB+Fj3/4qIiMipmr8+j/jIkAZXSx6Y0IEv7hvjp8qaT6f2ITx9zWDuPCOJH72dyX0L1vP3r3fyxPhBnNuns7/La5HKqmq5c9463liTy7i0OF65YTidI0L9XZaI1OPrVaCnA7OdFaB3AFOBhcATzohtFc69udbaDcaYuXhDshu411kBGuBuvt0G6T3nC7zTp2c5C2YV411FWkREROSk5JdVMX1BJm+tz+P9H/6gwdWSQ4MDawR0cNcoPrzrDOavz+Nn72zk/OeWMmFIV/50+QCSOrX3d3ktxtrcUia+spJv9pXz+0v68cB5fTTlWaQFMoE2YJqenm4zMjL8XYaIiIi0INZaXlqxm5++s5HK2jp+dVEqPzm7F5sLDh5xD/DCqaMYlBAVsMGmsraOP33yDY9+sg1r4b/O68MD5/WmfYivx1RaLmstLyzbxYy3M+nUvh2vTRnJ2b1j/V2WSEAzxqy01qY3+JoCsIiIiASyHfvKuWveOj7cVsRZKZ14/rqhpMVHAt+/CnSg2lVSwQPvbuKNNbn06BjGny4fyHVDA2/bpIPVbu5+ax2zV+3hwtTOzLp+BPEdNOVZxN8UgOtRABYRERGAOo/lySU7+OV7W3C54NHLBnDn6UkKuCfg82/2cf/CTNbmlnFOr1gev2ogQ7tF+7usZpGZV8bEWSvZWniQX12Uxn+P7UuQ/u6ItAgKwPUoAIuIiEhmXhk/fHMty3bt57L+8Txz7RB6dAz3d1mtUp3H8o+vs/nl+5spqaxl2ulJ/HZcWpte/Gnm8l3ct2A9UWHtmDNlBOdpUTCRFkUBuB4FYBERkcBV4/bw8Mfb+MNH24gKDeaJqwZx/fDuATd11xeKK2r49X+28uxXO4kKDeY3F6dx1xlJbWrbpPJqN/ctWM/LGTmc1zuW2VNGkBD13f2URcS/FIDrUQAWEREJTMuyS7hj7lo27D3ADcO789j4gcRFtt1RSn/JzCvjx4s28NG2IgYldODx8YM4v2/rHyHdmH+AibMy2FRwkF9ckMr/XpiqKc8iLZQCcD0KwCIiIoGlvNrNL97fzJNLsugeFcaz1w7hsgFd/F1Wm2at5e3MfH66aAM7Syq5dnBX/nTFAJJb6bZJs1bu5u5564kICeLVKSO4MDXO3yWJSCMaC8CBu2a9iIiItHkfbi1k2ptr2VlSyV1nJPHIZf2JCmvn77LaPGMMVw/uyrh+8fzls2945KPtvLtpLz87tzcPnNeHiNDW8RG0sraO6Qsy+efyXZzdqxNzpoykW7SmPIu0ZhoBFhERkTanpKKGn76zkZdW7KZv5wj+cd1Q7c3qRzn7K3ng3U28tnoPidFh/PHyAUwa1q1F33u9peAgE2dlsD7vAA+N7cNvLkprU/czi7RlmgJdjwKwiIhI2/bWulymL8iksLyGn53bm/+9MJXwdkH+LkuAL3bs4/63M1mTW8ZZKZ144qpBDOve8rZNmrMqhzvnrSMs2MWsG0Ywrl+8v0sSkROgAFyPArCIiEjblFdWxfQF65m/Pp/h3aN4YeIwhrfAcBXo6jyWF5fv4hfvbaa4ooY7fpDE7y5pGdsmVdXW8aOFG3j+62zOTI7htRtHkqjtsURaHQXgehSARURE2hZrLTNX7OZn72yksraOX1+Uxk/O6UU7TVdt0UoqavjNB1v525c76RAazK8vSuXu0cl+67dthQeZOGsla3PL+K9ze/O7S/rp75BIK6UAXI8CsIiISNuxY185d85bx0fbijgrpRP/mDiU1LhIf5clJ2Bj/gF+tDCTD7cVMaBLJI+PH8QFzbzK8tw1ufzwzbW0CzK8NHk4l2uVcJFWTQG4HgVgERGR1q/OY3lyyQ5++d4WglyGRy/rz7TTk3BpX9ZWyVrLwg35/HTRRrKKK7h6UAJ/vmIgKbG+3Tap2l3HTxdt5JmvdnJ6Ugyv3ziCnjGtc6smEfmWAnA9CsAiIiKtW2ZeGXfMXcvy3fu5rH88z1w7hB66T7NNqKqt46+f7+APH26jzlp+dm5vHvTRtkk79pUz8ZWVrNpTyo/P7sXDl/YnJFhTnkXaAgXgehSARUREWqdqdx1/+Gg7j3y8jeiwdjxx1SAmt/CtdOTk7Cmt5MF3NzF7lXfbpEcvH9CkfT1/fR63v7EGYwwzJw1j/KCEJjmviLQMCsD1KACLiIi0Pl9nl3DH3DVs3HuQKSO689j4gS1i1WDxrS+zirn/7UxW7SlljLNt0qms7F3j9vDzdzfy5BdZjOrRkddvHOnzadYi0vwUgOtRABYREWk9Dla7+cX7m3lqSRbdo8J49tohXKYFigJKnccyc8Uu/uffmymqqOH203ry+0v6ERd5Yj8A2VlcweRZK1m+ez8zzkrhj5cN0JRnkTZKAbgeBWAREZHW4YOthdz55lp2llRy9+hkHr60H1Fh7fxdlvjJ/spafvOfLfzty51EhATx64vTuOc4t01amJnP1DfW4LGWFycO5doh3ZqhYhHxFwXgehSARUREWrbiihp+tmgjL2XsJjUugn9cN5SzesX6uyxpITbtPcCPF27gP1sLGdAlksfGD+LCY2ybVFvn4cF3N/HY5zsY0T2aN24aSe/OEc1csYg0NwXgehSARUREWq631uVy34JMispr+K9ze/O/F6YS1i7I32VJC2Ot5Z2Ne/nJog3s2FfB+IFd+MuVA0mOaU9BeTXVbg8G+O0HW/nn8t3cPTqZv1wxQH+XRAKEAnA9CsAiIiItT15ZFffNX8+CzHyGd4/ihYnDTmmxIwkM1e46Hvt8B7//cBvDukXxx8sHcMPsVWSXVJIUE86LE4fh9ni4KC3e36WKSDNSAK5HAVhERKTlsNbyz+W7+dk7G6h2e/jVRWn89JxeBB/HfZ0ih+SWVrFjXzk3vbaa7JLKw+1JMeEsnTGGhA5hfqxORJpbYwG46XcVFxERETkO3xSVc+e8dXy8vYize3Xi+euGkhoX6e+ypBXqFh1GrcdzRPgFyC6ppNrt8VNVItISKQCLiIhIs6rzWB7/fAf/u3gzwS4Xz1wzmGmnJ+FyGX+XJq1YaLCLpJjw74wAh2qrIxGpRwFYREREms36vDLumLuWFbv3c3n/Ljxz7WASO4b7uyxpA+IjQlk4dRTjZ644fA/wwqmjiI84sf2CRaRtazQAG2PigCRgu7V2f7NUJCIiIm1OtbuOP3y0nYc/2kbH8HbMmTKCScO6YYxGfaVpuFyGQQlRLJ0xhmq3h9BgF/ERoZpZICJHOGYANsbcAfwB+AZIMcZMs9YuarbKREREpE1YurOYO+auZVPBQaaM6M5j4wfSWaNy4gMul9GCVyLSqMZGgH8EDLTWFhpjegGzAQVgEREROS4Hq938z3ubefrLLBKjw/jX7adxaf8u/i5LREQCWGMBuMZaWwhgrd1hjNGPakVEROS4/GdLAXfOW0d2SSX3jE7m4Uv70yFMS4+IiIh/NfadKNEY8+SxnltrZ3zfyY0xHYEXgEGABW6z1i41xkwH7gPcwLvW2p87738IuB2oA2ZYaxc77SOBl4Bw4N/A/dZa64TyV4CRwD5gkrV253Fct4iIiDQhj8dSUF5NZU0de8qq+NXiLYQFu/j83tGMSYn1d3kiIiJA4wH4v456vvIkzv8E8L61doIxJgRob4w5DxgPDLHWVhtj4gGMMQOAycBAoBvwoTEm1VpbBzwLTAO+xhuAxwHv4Q3LJdbaPsaYycCjwKSTqFNEREROksdjycwvO2L13TlTRjC8WxRhIRr1FRGRluOY35WstS+fyomNMVHA2cCtzvlqgBpjzN3AI9baaqe9wDlkPPC6055ljNkOnGaM2QlEWWuXOud9BbgKbwAeD/zaOX4e8LQxxlhr7anULiIiIsdv78Hqw+EXILukkhtmr2LpjDEkKACLiEgL0tgq0I0ueGWtvfJ7zt0LKARmGmOG4h1Bvh9IBc4yxvweqAJ+Zq1dAXTHO8J7SI7TVus8Prod59fdTj1uY0wpEAsUHXUt0/COINOzZ8/vKVtERESOV25pFbv2Vx4Ov4dkl1RS7fb4qSoREZGGNfZj2TPwhsvXgGXAiW6iFgyMAKZba5cZY54AHnTaY4DTgVHAXGeV6YbObxtp53te+7bB2ueB5wHS09M1OiwiItIElmTt47pXVvL3CUNIigk/IgQnxYQTGuzyY3UiIiLf1dh3pgTgv/EuYPUEcCFQZK39zFr72XGcOwfIsdYuc57PwxuIc4D51ms54AE6O+096h2fCOQ67YkNtFP/GGNMMBANFB9HbSIiInKSrLX87csszn92KR1CgxkQH8nCqaNIigkHvOF34dRRxGuvXxERaWEauwe4DngfeN9Zbfl64FNjzG+ttU9934mttfnGmN3GmDRr7RZgLLAR+AY43zlXKhCCd8ryImCOMeaveBfB6gsst9bWGWMOGGNOxzsSfTNw6PdfBNwCLAUmAB/r/l8RERHfqayt45631vFyRg6X9Y9n1g0j6BjeDo/HsnTGGKrdHkKDXcRHhOJynejkMREREd9qdGUKJ/hehjf8JgNPAvNP4PzTgdnOCtA7gKlAOfBPY0wmUAPc4oTWDcaYuXhDshu41wnhAHfz7TZI7zlfAC8Cs5wFs4rxriItIiIiPrCrpIJrX85gZU4pv7wwlV9dmHo45LpchoQOYX6uUEREpHHmWAOmxpiX8U5/fg/v6syZzVmYr6Snp9uMjAx/lyEiItKqfLK9iEmzVlLt9jDrhuFcOTDB3yWJiIg0yBiz0lqb3tBrjY0A34R3tDYVmGHM4WlMBrDW2qgmrVJERERaHGstj32+gwfe3UTfzhEsuHUUafGR/i5LRETkpDR2D7CWbhQREQlgFTVufvjmOl5bvYerByUwc/IwosLa+bssERGRk3bMkGuMOb/e45SjXrvGl0WJiIiIf+3YV87op77k9TV7+N0l/Xjz5nSFXxERafUaG+X9c73Hbx312i98UIuIiIi0AP/ZUsCox79g1/5K/nXbafz32L5a0VlERNqExu4BNsd43NBzERERaeWstTzy8XZ+8f5mBiV0YP4to+jdOcLfZYmIiDSZxgKwPcbjhp6LiIhIK3agys1tb6zhrfV5TB7WjX9cN5SI0EZ3SxQREWl1GvvO1ssYswjvaO+hxzjPU459mIiIiLQmWwsPcs1LK9hccJA/XT6An5zTi3q7P4iIiLQZjQXg8fUe//mo145+LiIiIq3Qvzbu5cY5q2jnMiyedjpj+8b5uyQRERGfaSwAT7XW3tpchYiIiEjz8Xgs//fhVn7zn60M7x7F/FtGkdSpvb/LEhER8anGAvCQZqtCREREmk1pZS03v7aadzbu5eaRiTw7YQjh7YL8XZaIiIjPNRaA2xtjhnOMFZ+ttat8U5KIiAQ6j8dSUF5NtdtDaLCL+IhQbcPTRDbmH+Cal1ewY18FT1w1iPvOTNb9viIiEjAaC8Ddgb/QcAC2wPk+qUhERAKax2PJzC9j/MwVZJdUkhQTzsKpoxiUEKUQfIrmr8/j1tdX075dEB/eeQZn9471d0kiIiLNqrEAvN1aq5ArIiLNqqC8+nD4BcguqWT8zBUsnTGGhA5hfq6udarzWH75/mYe+Xg7P+jZkXm3pNM9OtzfZYmIiDQ7bfAnIiItRp3HUlxRezj8HpJdUsn+ylq6RIZquu4JKq6oYcrsVSzeUsgdP+jJU1cPIjRY9/uKiEhgcjXy2gMAxpgwY8wgY8xAY4x+9C4iIk2uxu3hn8t3MfBPn7Cl4CBJMUeOTibFhLNp70EG/ulTnv1qJwer3X6qtHVZm1vKqMe/4OPtRTw3YQjPXzdU4VdERAJaYwH4E2PMH4Ec4GXgVWC3MeaPxph2zVKdiIi0aeXVbp74Ygd9Hv6IO+auJSIkiA6hQbw9ddThEJwUE87bU0cRGuwiMiSIe+evp8f/fcDP3tlA1r4KP19By/Xa6j2MfmoJ1W4Pn949mmmnJ/m7JBEREb8z1tqGXzDmMaAD8GNr7QGnLQr4M1Bprb2/2apsQunp6TYjI8PfZYiIBLT9lbX87cssnvgii6LyGs7u1YmHxvblotQ4jDHHXAXaWsvS7BKe/CKLt9bnYa3lyoEJzBiTwjm9YzU9GnDXeXjg3U089vkOxqR0Yu5NI0mI0gQuEREJHMaYldba9AZfayQAbwNS7VFvMMYEAZuttX2bvNJmoAAsIuI/ew9U89jnO3j2q50cqHZzWf94Hjy/L2emdDrhc+Xsr+SZr3byj6+z2VdRy5CuUUwfk8INI7oH7J62hQeruf7VVXy8vYh7z0zmL1cMJCS4scleIiIibc/JBuCt1trUE32tpVMAFhFpfjuLK/jTp98wc/kuauo8TBzajQfO78PQbtGnfO7K2jrmrNrDk0t2sD7vALHt2/HD05O4Z3QyiR0DZ6XjlTn7ufalDPYerOa5a4dwy6ge/i5JRETEL042AL8NzLfWvnJU+43ARGvtlU1daHNQABYRaT4b8w/w6CfbmbN6Dy4DN6f34Ofn9qZvXGST/17WWj77Zh9PLsli0YZ8jDFMGNKV6WNSOCMppk1Pj355xW7uemsdXSJDeevWdEYmdvR3SSIiIn5zsgG4BzAPqARWAhYYBYQDV1tr9/imXN9SABYR8b0Vu/bz8MfbeDszn/btgph2RhI/ObtXs43IZu2r4G9fZfHisl2UVrlJT4xm+lkpTBzarU2tglzj9vCTRRt45qudnNc7ltdvGklcZKi/yxIREfGrkw3Aq6y1I4wxY4EBgAE2WGs/8l2pvqcALCLiG9ZaPtm+j0c+3saH24roGN6O6WNSmD4mmc4R/gllB6vdzFqZw1NLsthccJAuHUK564wk7jojmS4dWndQzC+rYuKslSzJKuYn5/TikUv7Exyk+31FRERONgCvttYO92llfqAALCLStDwey7827eXhj7axbNd+EjqE8uOze3HXGcl0CAv2d3mAt8YPthXy1BdZ/HtzASFBLiYN68aMs1Ja5XThr7NLmPByBiWVNbwwcRjXD+/u75JERERajJMNwDnAX491UmvtMV9ryRSARUSahrvOwxtrc3nko+1s2HuAlE7t+a9ze3PrqB6EteBVmLcWHuTpL3fy0opdHKyuY3RyDDPG9OLqwQm0awUjqM9/nc30BetJjA5n/q3pTbKQmIiISFtysgE4D3gW79Tn77DW/qbJKmxGCsAiIqemqraOl1bs5k+ffkNWcQUDu3TgwbF9mDS0W6uagltaWcvMFbt5+sssduyrIDE6jLtHJzPt9CRiI0L8Xd53VLvrmL4gkxeW7eLitDhmTxlBp/Ytr04RERF/O6V7gH1amR8oAIuInJwDVW6eW7qTxz7fQf6Bak7r0ZH/vqAvl/fvgsvVeldYrvNY/r1pL08uyeKjbUWEBbuYMjKRGWNSGNw1yt/lAbCntJIJL2ewbNd+Hhrbh99e3I+gVvxnLiIi4kuNBeDGbs7Sd1YREWFfeQ1PLsni6SVZlFTWckHfzrx6wwjO6xPbJrYWCnIZrhiYwBUDE9iQf4Anl+zg1ZU5vLhsF+f1jmX6WSlcMSDBb4Hzix37mDhrJeU1bubdks41g7v6pQ4REZG2oLER4E7W2uJmrsfnNAIsInJ89pRW8pfPdvD80mwqauu4elACD57fl1E9O/q7NJ8rrqjhhWW7+NuXWezeX0VKp/bce2Yyt53Wk47h7ZqlBmstf/tyJz9ZtIGUTu1ZcOsoBiR0aJbfW0REpDU7qSnQbZUCsIhI47YXlfPoJ9t5JWM3Hgs3DO/Oz8/rw8AADF/uOg9vZ+bz1JIsvsgqJiIkiJvTezB9TDL94n3351FZW8fd89bxysocrhjQhVeuH050MwVvERGR1s5vAdgY0xF4ARgEWOA2a+1S57WfAX8C4qy1RU7bQ8DtQB0ww1q72GkfCbwEhAP/Bu631lpjTCjwCjAS2AdMstbubKwmBWARkYatzS3l0Y+3M3dtLu2CXNx2Wk/+69zeJHdq7+/SWoTVe0p56oss5qzeQ02dh4vT4pgxJoWL0+Kb9B7o7OIKrn05g1V7SvnVRan88oLUVn2PtYiISHPzZwB+GfjCWvuCMSYEaG+t3W+M6YE3GPcDRlpri4wxA4DXgNOAbsCHQKq1ts4Ysxy4H/gabwB+0lr7njHmHmCItfYuY8xk4Gpr7aTGalIAFhE50pdZxTzy8Tbe3VRAh9Bg7h6dzI/OSiEhKszfpbVIBQeq+fvX2Tz71U7yD1STGhfBfWemcEt6j1Pe9/jjbUVMmpVBrcfy6g0juHxAlyaqWkREJHD4JQAbY6KAtUAve9RvYoyZB/wfsBBIdwLwQwDW2oed9ywGfg3sBD6x1vZz2q8HzrXW3nnoPdbapcaYYCAf74jyMS9KAVhExHt/6X+2FvLwR9v4fEcxnSNCmHFWCveOTiZGW+sclxq3h3nrcnnyiyyW795PVFgwt53Wk/vOTKZXbMQJnctay18/28ED726kX3wk828dRWpcpI8qFxERadtOdhXoU9ULKARmGmOGAivxjuKOBfZYa9cetXpod7wjvIfkOG21zuOj2w8dsxvAWus2xpQCsUBR/RMbY6YB0wB69uzZFNcmItIq1XksC9bn8fDH21i9p4zE6DAeGz+QO07rSUSoL78ltD0hwS5uGJHIDSMSWZZdcnil7Ce+2MEVA7owfUwK5/fp/L0rZZdXu/nhm2t5fU0u1w7uyj8nDTvlkWQRERFpmC+/wwYDI4Dp1tplxpgn8I7ong1c1MD7G/qEYBtpb+yYIxusfR54HrwjwN9buYhIG1Pj9jB7VQ5//GQ7WwrL6ds5ghcmDuXGEYmEBLv8XV6r94OkGGYnxfDHy/vz3NJs/r40m0Ub9jIooQPTx6QwZUR32od891vuN0XlXPPyCjLzD/CHS/vxwHl92sTWUiIiIi2VLwNwDpBjrV3mPJ+HNwCnAIdGfxOBVcaY05z396h3fCKQ67QnNtBOvWNynCnQ0UCb27pJRORkVdS4eXH5bv786XZ2769iWLco3rhpJNcM7uq3fW3bsu7R4fzfuH78z9i+vL4mlye/2MGd89bx0L83cccPkrj/rBSMgWq3h8paD9PnrydnfxX/vuMHXJwW7+/yRURE2jyfBWBrbb4xZrcxJs1auwXv1OdV1tqxh95jjNnJt/cALwLmGGP+incRrL7AcmcRrAPGmNOBZcDNwFPOKRYBtwBLgQnAx43d/ysiEij2V9byzFc7eeLzHRSW13BWSif+PmEoF6fFaYSxGYS1C+LWUT24JT2RL3YU89SSLD7/pogLUztzx9y1ZJdUkhQTzkuTh9Ertj09OmqlbRERkebg65uMpgOznRWgdwBTj/VGa+0GY8xcYCPgBu611tY5L9/Nt9sgved8AbwIzDLGbMc78jvZFxchItJSeTyWgvJqqt0eQoNdBLsMf/lsB89+tZOyKjeX9IvnwfP7cFavWH+XGpCMMZzdO5aze8eSXVzBuc9+RXZJJQDZJZXc+voals4Y4+cqRUREAodPA7C1dg3Q4OpbzuvJRz3/PfD7Bt6XgXcv4aPbq4DrTrVOEZHWyOOxZOaXMX7misMjii9OHMqn24sYlxbPA+f3YXj3aH+XKYcYDoffQ7JLKql2e/xUkIiISODRMpMiIq3IgSo324oOsrWwnD6dI7julYwjRhRvn7uWT+4eTXInTaltaUKDXSTFhB8RgpNiwgnVImQiIiLNRgFYRKSFqaqtY8e+CrY6QXdbUTnbCr2P8w9UH37fJ3ePbnBEUbf4tkzxEaEsnDrqiBH7hVNHER8R6u/SREREAoYCsIiIH7jrPGSXVLK18CDbisqdoHuQbYXlZO+vpP5yfvGRIaTGRXJJ/3j6do4gNS6S1M4RdGofohHFVsTlMgxKiGLpjDGH79mOjwjFpdW4RUREmo0CsIiIj1hryS2rYmth+eGgu815vKO4gtq6b1NuVFgwqZ0jGJ3ciVviIg4H3b6dI4gOb9fg+T0eqxHFVsblMiR0CPN3GSIiIgFLAVhE5BRYa9lXUeME23K2Fh1ke2E5W52wW1Fbd/i9YcEu+sZFMDChA1cN7kpq5wj6xkWQ2jmSuMiQE96eSCOKIiIiIidGAVhE5DjUX3yq/j2524rKKamsPfy+IJehV6f29O0cwbm9Yw+P4qbGRZAYHd7k4VQjiiIiIiLHTwFYRALC0fvlNjRSWu2u45ui7198CqBHxzBS4yKZOKwbqc4obt+4CFI6taddkO7BFREREWmJFIBFpM1raL/cN29OZ3vRQZZklbC9yHtf7tGLT8VFhJAaF8G4fvGHR3FT4yLpHdue9iH671NERESktdEnOBFp8wrKqw+HX/BuFXTdKxn89cqBzFqZQ2pcBGckd+Lmevfk9o2LoOMxFp8SERERkdZJAVhE2rzSSneD++UO7RbF/t+NO+HFp0RERESkddKNaiLSZtXWefjvf29i494DJMWEH/FaUkw47UOCFH5FREREAogCsIi0SduLyhnz9Jc88vF2Nuwt4+1bRx0OwdovV0RERCQwaQq0iLQp1lpezshhxtvrCXa5eOOmkVw3tBsej9V+uSIiIiIBTgFYRNqM/ZW13DVvHXPX5nJOr1heuWE4PTp6R321X66IiIiIKACLSJvwxY593DRnNXvKqvjdJf144Lw+BGmEV0RERETqUQAWkVatts7Dbz/YysMfbSOlU3u+vO9MTusZ4++yRERERKQFUgAWkVZrx75ypsxexbJd+7klPZEnrxpMhzD9tyYiIiIiDdMnRRFpday1vLoqh/vmZ+Iy8NqNI5g0rLu/yxIRERGRFk4BWERaldLKWu6Zv57XVu9hTEonZl0/nKRO7f1dloiIiIi0AgrAItJqfJlVzE1zVrG7tIrfjkvjofP7aqErERERETluCsAi0uK56zz87sNt/O7DrSTFtOfze0ZzRnInf5clIiIiIq2MArCItGhZ+yq46bVVfLWzhJtGJvLU1YOICmvn77JEREREpBVSABaRFmvOqhzumb8egFdvGM4NIxL9XJGIiIiItGYKwCLS4pRV1XLf/PW8umoPo5NjmHX9CFJitdCViIiIiJwaBWARaVGW7izmxjmryS6p4FcXpfI/Y/sSHOTyd1kiIiIi0gYoAItIi1Dnsfzho2389oOtJEaH8dk9Z3Jmiha6EhEREZGmowAsIn6XXVzBTa+tZklWMdcP784z1wwmOlwLXYmIiIhI01IAFhG/en31Hu5+ax0eCy9fP4wbRyRijPb2FREREZGmpwAsIn5xoMrNjLfX83JGDqcnxfDqDcPpFRvh77JEREREpA3z6coyxpiOxph5xpjNxphNxpgzjDF/cp6vM8YsMMZ0rPf+h4wx240xW4wxF9drH2mMWe+89qRxhoeMMaHGmDec9mXGmGRfXo+INI3lu0oY8dhnzFqZwy8u6Mtn94xW+BURERERn/P10qpPAO9ba/sBQ4FNwAfAIGvtEGAr8BCAMWYAMBkYCIwDnjHGBDnneRaYBvR1vsY57bcDJdbaPsBjwKM+vh4ROQWHFroa8/SX1NZZPrl7NL8d1492WuVZRERERJqBzz51GmOigLOBFwGstTXW2v3W2v9Ya93O274GEp3H44HXrbXV1tosYDtwmjGmKxBlrV1qrbXAK8BV9Y552Xk8DxhrdPOgSIu0e38lFzy3lF+8t5lrh3RlzU/P4axesf4uS0REREQCiC/vAe4FFAIzjTFDgZXA/dba8nrvuQ14w3ncHW8gPiTHaat1Hh/dfuiY3QDWWrcxphSIBYrqF2KMmYZ3BJmePXue8oWJyIl5c20ud85bR22dh5mThnFzuha6EhEREZHm58t5h8HACOBZa+1woBx48NCLxpj/AdzA7ENNDZzDNtLe2DFHNlj7vLU23VqbHhcXd/xXICKn5GC1m9vnrmHSrJX07RzB6p+cwy2jeij8ioiIiIhf+HIEOAfIsdYuc57PwwnAxphbgMuBsc605kPv71Hv+EQg12lPbKC9/jE5xphgIBoobvpLEZETlbF7P1Nmr2L7vnIeGtuHX1+Upnt9RURERMSvfPZp1FqbD+w2xqQ5TWOBjcaYccADwJXW2op6hywCJjsrO6fgXexqubU2DzhgjDndub/3ZmBhvWNucR5PAD6uF6hFxA88HsujH29n9FNLqKyt46M7z+D3l/RX+BURERERv/P1PsDTgdnGmBBgBzAVWAGEAh840yC/ttbeZa3dYIyZC2zEOzX6XmttnXOeu4GXgHDgPecLvAtszTLGbMc78jvZx9cjIo3I2V/JLa+t5pNv9jFhSFeemzCETu1D/F2WiIiIiAgAJtAGTNPT021GRoa/yxBpc+avz+OHc9dS7fbw5NWDmKp7fUVERETED4wxK6216Q295usRYBFp48qr3fx40QZeWLaLkYnRzJ4ygtS4SH+XJSIiIiLyHQrAInLSVuV4F7raWlTOz8/rw28vTiMkWPf6ioiIiEjLpAAsIifM47H89fMd/M97m4iLCOWDaWdwft/O/i5LRERERKRRCsAickJyS6u49fXVfLitiKsHJfD8dUOJjdBCVyIiIiLS8ikAi8hxW5iZzx1z11BZ6+HvE4Zwxw96aqErEREREWk1FIBF5HtV1Lj56Tsb+fvSbIZ3j2L2lBH0i+/g77JERERERE6IArCINGrNnlKmzF7FpoKD/PSc3vzukjRCg4P8XZaIiIiIyAlTABaRI3g8loLyaqrdHoorapixIJP9VbUsnnY6F6bG+bs8EREREZGTpgAsPlc/UIUGu4iPCMXl0n2jLZHHY8nML2P8zBVkl1SSFBPOK9cPZ2CXSDpFhPq7PBERERGRU6INO8WnDgWqM55cQsrvP+KMJ5eQmV+Gx2P9XZocpaLGzcaCA4fDL0B2SSU3v7aaGvWXiIiIiLQBCsDiM8UVNWxqIFCNn7mCgvJqP1cnAHUeywdbC7n1tdUk/OY/7CuvPdxXh2SXVFLt9vipQhERERGRpqMp0NJk6jyWjN37eX9LAYu3FLJ8Vwkf3TW6wUC1q6SShZn5XDO4K3GRmlrbnKy1rN5Tyqur9vDGmj3klVUTHRbMxKHd6dohlKSY8CP6LCkmnNBg/axMRERERFo/BWA5JfllVSzeUsjiLQX8Z2shxRW1GAOn9ejILy5IpXt0w4GquKKGu99az73z13Nen85cN7QbVw9KUBj2oax9FcxZncPsVXvYXHCQkCAXl/WP54YR3bmsfxfC2gXh8VgWTh11xD3AC6eOIl73/4qIiIhIG2CsDax7+9LT021GRoa/y2i1atwelmYX8/6WQhZvLmBNbhkAXTqEMi4tjovT4rkwNY7YiBCg4UWVFk4dxcAuHdiw9yBz1+by5tpcthWVE+QynNc71huGByfQWaHrlO0rr2Hu2lzmrMrhy50lAJzdqxNTRiQyYUhXYtqHfOcYLVomIiIiIq2ZMWaltTa9wdcUgOX77CyuYLEzrfmjbUUcqHYT7DKcmdyJi/vFMS4tniFdo44Zkr4vUFlrWZdX5oThPLY7Yfj8Pk4YHtT1cKCW71dZW8c7G/Yye1UO720uwO2xDOzSgSkju3P9sO4kdWrv7xJFRERERHxGAbgeBeDvV1lbx+c79vH+Zm/o3VxwEPBOXb44LZ5x/eI4v09nosLaNfnvba1lbW7Z4ZHhb/ZVEOQyjHWmSV81KEFhuAF1Hssn24uYs2oPb63P40C1m25RYVw/vDs3juzOkK5RGKNRXBERERFp+xSA61EA/i5rLVsLy72LV20u4NNv9lHljNae2zv2cOhNi4ts1hB1aLGmN9fl8ebaXHbsqyDYZRjb99sw3KmBKbyBwlrLmtwyXl2Zw+vOYlZRYcFcO6QrU4Ynck7vWII0dVlEREREAowCcD0KwF4Hqtx8vL3ocOjd6SxSlRYXwcX94hmXFs/ZvTrRPqRlrJNmrWXVnlLeXOsNw1nF3jB8Qb0w3ND9rG3RzuIK5qzew5xVOWzce5B2QYZL+8UzZUQilw3oQni7IH+XKCIiIiLiNwrA9QRqAD50n+37m70rNi/JKsbtsUSGBjG2T2cuTovn4rR4UmJb/v2h1lpW5pTypjNNemdJJe2CDBf0jeO6od0YP7BLmwvD+8preHNtLnNW72FJVjEAY1I6MWVEd64b2i2gR8JFREREROpTAK4nkAJwcUUNH2wtZPHmQt7fUkD+gWoAhnaL8k5rTotjdHInQlrxHq/WWjJ2l/LmOm8YznbC8IWHwvCgBDqGN/29ys2hsraOf238djGr2jpL//hIpoxM5Ibh3UnWYlYiIiIiIt+hAFxPWw7AdR5Lxu793mnNWwpZvqsEj4WY8HZc5GxRdFFqHN2iw/xdqk9Ya1mxez9z1+Yyb20eu/Z7w/BFqd4wfOXAlh+G6zyWT78pYvaqPcxfn0dZlZuuUaFcP7w7U0YkMqybFrMSEREREWmMAnA9bS0A55dVsXiLd1rzf7YWUlxRizFwWo+OXJwWzyX94knv0THgFkOy1rJ8lxOG1+Wye38VIUEubxge1pUrByQQ3ULC8KGVr2ev2sNrq/eQW1ZFh9Bgrh3clSkju3Nu784B138iIiIiIidLAbie1h6Aa+s8fLWzmPe3FLJ4cwFrcssASOgQyri0eC7uF8cFfeO0VVA9Ho9l+eGR4VxySr1h+OK0QyPDXXyypdP3yT68mNUeNuw9QLDLcGn/eG4YnsgVA7WYlYiIiIjIyVAArqclB2CPx1JQXk21swVRfEQoLpdhZ3EFi51pzR9tK+JAtZtgl2FMSqfDWxRpn9fj4/FYlu0qYe7aXN5al0dOaRWhwS7GpcUxYWg3rhjg2zBcXFHDvHV5zF6ZwxfOYlZnJscwZUQi1w3tph9ciIiIiIicIgXgelpqAPZ4LJn5ZYyfuYLskkqSYsKZPWUET36xg7lr8wBIiglnnLNF0Xl9Yv0yatmWeDyWr+uF4T1OGL6kXzwThnTligEJdAg79W2gqpzFrOas3sO7m/ZSW2fpFx/JlBHduWF4YqtYeVtEREREpLVQAK6npQbg/ANVnPHkErKd/XjBG3j/OWkY6/LKGJcWT2pchEZ5fcTjsSzNLjl8z3BeWTWhwS4u7RfPhKHduLx/lxMKwx6P5bMd+3h1VQ7z1+VRWuUmoUMok4d358YR3RnePVp9KSIiIiLiA40F4FMf3pImUe32HBF+AbJLKukV257z+nT2U1WBw+UynJnSiTNTOvHYlQP5KruYuWvzeGtdLgsy8wkLdnFp/3gmDOnG5QO6EBka3OCU9cz8A7y6KofXVu9hT2kVkaFBXDu4KzeMSOT8PlrMSkRERETEnxSAW4jQYBdJMeHfGQEObcV79LZWLpdhTEosY1JiefzKgXy5s/jwNOn5671h+P6zUrhmcFcmzlp5eMr6S5OH8eC7m1iZU8q4tHj+fMUArhjQhfYh+mcmIiIiItISaAp0C9HQPcALp45iUEIULo0atgh1HsuXWd4wPK5fHNMXZH7nBxYLbh1FYscwOkeE+rFSEREREZHA5bcp0MaYjsALwCDAArcBW4A3gGRgJzDRWlvivP8h4HagDphhrV3stI8EXgLCgX8D91trrTEmFHgFGAnsAyZZa3f68pp8xeUyDEqIYumMMd9ZBVpahiCX4ezesZzdO5bskooGp6zHtG+n8CsiIiIi0kL5en7tE8D71tp+wFBgE/Ag8JG1ti/wkfMcY8wAYDIwEBgHPGOMObQR6rPANKCv8zXOab8dKLHW9gEeAx718fX4lMtlSOgQRlJMexI6hCn8tmCHpqzXpynrIiIiIiItm88+rRtjooCzgRcBrLU11tr9wHjgZedtLwNXOY/HA69ba6uttVnAduA0Y0xXIMpau9R652u/ctQxh841DxhrtLSuNIP4iFAWTh11OAQfmrIer9FfEREREZEWy5dToHsBhcBMY8xQYCVwP9DFWpsHYK3NM8bEO+/vDnxd7/gcp63WeXx0+6FjdjvnchtjSoFYoKh+IcaYaXhHkOnZs2dTXZ8EME1ZFxERERFpfXw5XzMYGAE8a60dDpTjTHc+hoaSg22kvbFjjmyw9nlrbbq1Nj0uLq7xqkWOk6asi4iIiIi0Lr4MwDlAjrV2mfN8Ht5AvNeZ1ozza0G99/eod3wikOu0JzbQfsQxxphgIBoobvIrERERERERkVbPZwHYWpsP7DbGpDlNY4GNwCLgFqftFmCh83gRMNkYE2qMScG72NVyZ7r0AWPM6c79vTcfdcyhc00APraBtq+TiIiIiIiIHBefboMETAdmG2NCgB3AVLyhe64x5nZgF3AdgLV2gzFmLt6Q7AbutdbWOee5m2+3QXrP+QLvAluzjDHb8Y78Tvbx9YiIiIiIiEgrZQJtwDQ9Pd1mZGT4uwwRERERERHxAWPMSmttekOvadNSERERERERCQgBNwJsjCkEsv1dx/fozFFbOUmLpv5qXdRfrYv6q/VRn7Uu6q/WRf3Vuqi//CfJWtvg9j8BF4BbA2NMxrGG7KXlUX+1Luqv1kX91fqoz1oX9Vfrov5qXdRfLZOmQIuIiIiIiEhAUAAWERERERGRgKAA3DI97+8C5ISov1oX9Vfrov5qfdRnrYv6q3VRf7Uu6q8WSPcAi4iIiIiISEDQCLCIiIiIiIgEBAVgERERERERCQgKwM3AGPNPY0yBMSazXttQY8xSY8x6Y8w7xpioo47paYw5aIz5mfO8gzFmTb2vImPM4818KQGhKfrLaZtkjFlnjNlgjPljc15DIDmR/jLGJBtjKuv9O3qu3jG/N8bsNsYc9Md1BJIm7LP3jTFrnX9jzxljgvxxPW1dE/bXp8aYLfVei/fH9bR1TdFf+szRfJrw35c+czSTE/2caIwZ4ry2wXk9zGnX5w5/sdbqy8dfwNnACCCzXtsK4Bzn8W3A/x11zFvAm8DPjnHOlcDZ/r62tvjVFP0FxAK7gDjn+cvAWH9fW1v8OpH+ApLrv++o85wOdAUO+vua2vpXE/ZZlPOrcf4NTvb3tbXFrybsr0+BdH9fT1v/aqr+Ouqc+szRgvtLnzladJ8FA+uAofX6Ksh5rM8dfvrSCHAzsNZ+DhQf1ZwGfO48/gC49tALxpirgB3AhobOZ4zpC8QDXzR1rdJk/dUL2GqtLXSef1j/GGk6J9pfjZzna2ttXhOXJw1owj4rcx4GAyGAVnX0gabqL2keTd1f+szhW03UX/rM0YxOsM8uAtZZa9c6x+6z1tY5j/W5w08UgP0nE7jSeXwd0APAGBMBPAD8ppFjrwfesM6Pj6RZnGh/bQf6OdOVgoGrDh0jzaLB/nKkGGNWG2M+M8ac1fylyTGcVJ8ZYxYDBcABYF6zVCpw8v/GZjpTN39pjDHNUqnAqf2fqM8cze9E+0ufOfzvWH2WClhjzGJjzCpjzM/9Up0cQQHYf24D7jXGrAQ6ADVO+2+Ax6y1jd0PMBl4zcf1yZFOqL+stSXA3cAbeH9qvhNwN1u1cqz+ygN6WmuHAz8B5pij7ucWvzmpPrPWXox3ClkocH7zlhzQTqa/plhrBwNnOV83NXPNgexU/k/UZ47md0L9pc8cLcKx+iwYGANMcX692hgz1j8lyiHB/i4gUFlrN+OdFoExJhW4zHnpB8AEZwGDjoDHGFNlrX3aee9QINhau7L5qw5cJ9Nf1tp3gHecY6YBdc1eeIA6Vn9Za6uBaufxSmPMN3h/Opvhp1LFcSp9Zq2tMsYsAsbjnXomPnYy/WWt3eO0HzDGzAFOA17xQ/kB52T/fekzh3+c5L8vfebwo0Y+J+YAn1lri5zX/o33/uGP/FGneGkE2E+Ms/qlMcYF/AJ4DsBae5a1Ntlamww8DvzhUPh1XI9+EtvsTqa/6h0TA9wDvND8lQemY/WXMSbOOCsFG2N6AX3x3r8tfnaifWaMiTTGdHXag4FLgc3+qD0QnUR/BRtjOjvt7YDL8U4ZlGZwCv8n6jOHH5xMf+kzh38dq8+AxcAQY0x753vVOcBG/1QphygANwNjzGvAUiDNGJNjjLkduN4YsxXvB7ZcYOZxnm4i+mbkU03YX08YYzYCXwKPWGu3+qzoAHaC/XU2sM4Ysxbv/aJ3WWuLnfP80RiTA7R3zvPr5r6WQNFEfRYBLDLGrAPW4r0P+DmkyTVRf4UCi53+WgPsAf7RvFcSGJrq/0SHPnP4WBP2lz5zNJMT6TNnevpf8a4SvQZYZa191zmPPnf4idGaBiIiIiIiIhIINAIsIiIiIiIiAUEBWERERERERAKCArCIiIiIiIgEBAVgERERERERCQgKwCIiIiIiIhIQFIBFREREREQkICgAi4iIiIiISED4f0zA9eg/GvDGAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1152x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "y, X = load_longley()\n",
    "plot_series(y);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>GNPDEFL</th>\n",
       "      <th>GNP</th>\n",
       "      <th>UNEMP</th>\n",
       "      <th>ARMED</th>\n",
       "      <th>POP</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Period</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1947</th>\n",
       "      <td>83.0</td>\n",
       "      <td>234289.0</td>\n",
       "      <td>2356.0</td>\n",
       "      <td>1590.0</td>\n",
       "      <td>107608.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1948</th>\n",
       "      <td>88.5</td>\n",
       "      <td>259426.0</td>\n",
       "      <td>2325.0</td>\n",
       "      <td>1456.0</td>\n",
       "      <td>108632.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1949</th>\n",
       "      <td>88.2</td>\n",
       "      <td>258054.0</td>\n",
       "      <td>3682.0</td>\n",
       "      <td>1616.0</td>\n",
       "      <td>109773.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1950</th>\n",
       "      <td>89.5</td>\n",
       "      <td>284599.0</td>\n",
       "      <td>3351.0</td>\n",
       "      <td>1650.0</td>\n",
       "      <td>110929.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1951</th>\n",
       "      <td>96.2</td>\n",
       "      <td>328975.0</td>\n",
       "      <td>2099.0</td>\n",
       "      <td>3099.0</td>\n",
       "      <td>112075.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1952</th>\n",
       "      <td>98.1</td>\n",
       "      <td>346999.0</td>\n",
       "      <td>1932.0</td>\n",
       "      <td>3594.0</td>\n",
       "      <td>113270.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1953</th>\n",
       "      <td>99.0</td>\n",
       "      <td>365385.0</td>\n",
       "      <td>1870.0</td>\n",
       "      <td>3547.0</td>\n",
       "      <td>115094.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1954</th>\n",
       "      <td>100.0</td>\n",
       "      <td>363112.0</td>\n",
       "      <td>3578.0</td>\n",
       "      <td>3350.0</td>\n",
       "      <td>116219.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1955</th>\n",
       "      <td>101.2</td>\n",
       "      <td>397469.0</td>\n",
       "      <td>2904.0</td>\n",
       "      <td>3048.0</td>\n",
       "      <td>117388.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1956</th>\n",
       "      <td>104.6</td>\n",
       "      <td>419180.0</td>\n",
       "      <td>2822.0</td>\n",
       "      <td>2857.0</td>\n",
       "      <td>118734.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1957</th>\n",
       "      <td>108.4</td>\n",
       "      <td>442769.0</td>\n",
       "      <td>2936.0</td>\n",
       "      <td>2798.0</td>\n",
       "      <td>120445.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1958</th>\n",
       "      <td>110.8</td>\n",
       "      <td>444546.0</td>\n",
       "      <td>4681.0</td>\n",
       "      <td>2637.0</td>\n",
       "      <td>121950.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1959</th>\n",
       "      <td>112.6</td>\n",
       "      <td>482704.0</td>\n",
       "      <td>3813.0</td>\n",
       "      <td>2552.0</td>\n",
       "      <td>123366.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1960</th>\n",
       "      <td>114.2</td>\n",
       "      <td>502601.0</td>\n",
       "      <td>3931.0</td>\n",
       "      <td>2514.0</td>\n",
       "      <td>125368.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1961</th>\n",
       "      <td>115.7</td>\n",
       "      <td>518173.0</td>\n",
       "      <td>4806.0</td>\n",
       "      <td>2572.0</td>\n",
       "      <td>127852.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1962</th>\n",
       "      <td>116.9</td>\n",
       "      <td>554894.0</td>\n",
       "      <td>4007.0</td>\n",
       "      <td>2827.0</td>\n",
       "      <td>130081.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        GNPDEFL       GNP   UNEMP   ARMED       POP\n",
       "Period                                             \n",
       "1947       83.0  234289.0  2356.0  1590.0  107608.0\n",
       "1948       88.5  259426.0  2325.0  1456.0  108632.0\n",
       "1949       88.2  258054.0  3682.0  1616.0  109773.0\n",
       "1950       89.5  284599.0  3351.0  1650.0  110929.0\n",
       "1951       96.2  328975.0  2099.0  3099.0  112075.0\n",
       "1952       98.1  346999.0  1932.0  3594.0  113270.0\n",
       "1953       99.0  365385.0  1870.0  3547.0  115094.0\n",
       "1954      100.0  363112.0  3578.0  3350.0  116219.0\n",
       "1955      101.2  397469.0  2904.0  3048.0  117388.0\n",
       "1956      104.6  419180.0  2822.0  2857.0  118734.0\n",
       "1957      108.4  442769.0  2936.0  2798.0  120445.0\n",
       "1958      110.8  444546.0  4681.0  2637.0  121950.0\n",
       "1959      112.6  482704.0  3813.0  2552.0  123366.0\n",
       "1960      114.2  502601.0  3931.0  2514.0  125368.0\n",
       "1961      115.7  518173.0  4806.0  2572.0  127852.0\n",
       "1962      116.9  554894.0  4007.0  2827.0  130081.0"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### D.2––Learning process\n",
    "\n",
    "* Temporal dependency in data\n",
    "* Forecasting horizon"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 2, 3])"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sktime.forecasting.model_selection import temporal_train_test_split\n",
    "\n",
    "fh = np.arange(3) + 1\n",
    "fh"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(13,) (3,) (13, 5) (3, 5)\n"
     ]
    }
   ],
   "source": [
    "y_train, y_test, X_train, X_test = temporal_train_test_split(y, X, fh=fh)\n",
    "print(y_train.shape, y_test.shape, X_train.shape, X_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_series(y_train, y_test, labels=[\"y_train\", \"y_test\"]);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sktime.forecasting.naive import NaiveForecaster\n",
    "from sktime.forecasting.arima import AutoARIMA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "NaiveForecaster(sp=1, strategy='last', window_length=None)"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "forecaster = NaiveForecaster()\n",
    "forecaster.fit(y_train, X=X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = forecaster.predict(fh, X=X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1960    68655.0\n",
       "1961    68655.0\n",
       "1962    68655.0\n",
       "Freq: A-DEC, dtype: float64"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_series(y_train, y_test, y_pred, labels=[\"y_train\", \"y_test\", \"y_pred\"]);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Overview of forecasting algorithms:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['ARIMA',\n",
       " 'AutoARIMA',\n",
       " 'AutoETS',\n",
       " 'BATS',\n",
       " 'Croston',\n",
       " 'DirRecTabularRegressionForecaster',\n",
       " 'DirRecTimeSeriesRegressionForecaster',\n",
       " 'DirectTabularRegressionForecaster',\n",
       " 'DirectTimeSeriesRegressionForecaster',\n",
       " 'EnsembleForecaster',\n",
       " 'ExponentialSmoothing',\n",
       " 'ForecastingGridSearchCV',\n",
       " 'ForecastingPipeline',\n",
       " 'ForecastingRandomizedSearchCV',\n",
       " 'HCrystalBallForecaster',\n",
       " 'MultioutputTabularRegressionForecaster',\n",
       " 'MultioutputTimeSeriesRegressionForecaster',\n",
       " 'MultiplexForecaster',\n",
       " 'NaiveForecaster',\n",
       " 'OnlineEnsembleForecaster',\n",
       " 'PolynomialTrendForecaster',\n",
       " 'Prophet',\n",
       " 'RecursiveTabularRegressionForecaster',\n",
       " 'RecursiveTimeSeriesRegressionForecaster',\n",
       " 'StackingForecaster',\n",
       " 'TBATS',\n",
       " 'ThetaForecaster',\n",
       " 'TransformedTargetForecaster']"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "[name for name, klass in all_estimators(\"forecaster\")]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### D.3––Evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.01656386931275494"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import mean_absolute_percentage_error\n",
    "mean_absolute_percentage_error(y_test, y_pred)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### D.4––Composition\n",
    "* Ensembling\n",
    "* Pipelining & transformers\n",
    "* Tuning\n",
    "* Reduction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sktime.forecasting.compose import TransformedTargetForecaster\n",
    "from sktime.transformations.series.boxcox import BoxCoxTransformer\n",
    "from sktime.transformations.series.detrend import Detrender\n",
    "from sktime.forecasting.trend import PolynomialTrendForecaster"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "TransformedTargetForecaster(steps=[('boxcox',\n",
       "                                    BoxCoxTransformer(bounds=None, method='mle',\n",
       "                                                      sp=None)),\n",
       "                                   ('detrend',\n",
       "                                    Detrender(forecaster=PolynomialTrendForecaster(degree=1,\n",
       "                                                                                   regressor=None,\n",
       "                                                                                   with_intercept=True))),\n",
       "                                   ('forecaster',\n",
       "                                    NaiveForecaster(sp=12, strategy='last',\n",
       "                                                    window_length=None))])"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pipeline = TransformedTargetForecaster([\n",
    "    (\"boxcox\", BoxCoxTransformer()),\n",
    "    (\"detrend\", Detrender(PolynomialTrendForecaster())),\n",
    "    (\"forecaster\", NaiveForecaster(sp=12)),\n",
    "])\n",
    "pipeline.fit(y_train, X=X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "y_pred = pipeline.predict(fh, X=X_train)\n",
    "plot_series(y_train, y_test, y_pred, labels=[\"y_train\", \"y_test\", \"y_pred\"]);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "## E––Reduction\n",
    "\n",
    "![](https://i.imgur.com/u0YlHYU.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Example: Reduction from forecasting to cross-sectional regression \n",
    "Following arrows (b), (d) and (g)\n",
    "![](https://i.imgur.com/7OSd7K8.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sktime.forecasting.compose import make_reduction\n",
    "from sklearn.neighbors import KNeighborsRegressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RecursiveTabularRegressionForecaster(estimator=KNeighborsRegressor(algorithm='auto',\n",
       "                                                                   leaf_size=30,\n",
       "                                                                   metric='minkowski',\n",
       "                                                                   metric_params=None,\n",
       "                                                                   n_jobs=None,\n",
       "                                                                   n_neighbors=1,\n",
       "                                                                   p=2,\n",
       "                                                                   weights='uniform'),\n",
       "                                     window_length=12)"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reduction = make_reduction(KNeighborsRegressor(n_neighbors=1), strategy=\"recursive\", window_length=12)\n",
    "reduction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RecursiveTabularRegressionForecaster(estimator=KNeighborsRegressor(algorithm='auto',\n",
       "                                                                   leaf_size=30,\n",
       "                                                                   metric='minkowski',\n",
       "                                                                   metric_params=None,\n",
       "                                                                   n_jobs=None,\n",
       "                                                                   n_neighbors=1,\n",
       "                                                                   p=2,\n",
       "                                                                   weights='uniform'),\n",
       "                                     window_length=12)"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reduction.fit(y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "y_pred = reduction.predict(fh)\n",
    "plot_series(y_train, y_test, y_pred, labels=[\"y_train\", \"y_test\", \"y_pred\"]);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Getting involved\n",
    "\n",
    "* [all-contributors](https://github.com/alan-turing-institute/sktime/blob/master/CONTRIBUTORS.md)\n",
    "* [Governance](https://www.sktime.org/en/latest/governance.html)\n",
    "* Mentoring (Google summer of code, Outreachy, sktime's own mentoring scheme): https://github.com/sktime/mentoring\n",
    "* [Contributing guide](https://github.com/alan-turing-institute/sktime/blob/master/CONTRIBUTING.md) (git workflow, pre-commit, CI)\n",
    "* Summer workshops (onboarding, \"sktime doc sprints\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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