python-pour-finance/08-Analyse-Time-Series/4-ARIMA-et-ARIMA-Saisonnier...

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"#### Attention ! C'est un sujet compliqué ! Rappelez-vous que c'est un notebook facultatif à parcourir et que pour le comprendre pleinement, vous devez lire les liens supplémentaires et regarder la vidéo explicative entièrement. Ce notebook et les vidéos liées n'ont pas pour but de donner un aperçu complet d'ARIMA, mais plutôt de vous montrer à quoi il peut servir, afin que vous puissiez comprendre plus tard pourquoi il peut être ou non un bon choix pour les données financières sur les actions.\n",
"____\n",
"\n",
"\n",
"# ARIMA et ARIMA Saisonnier\n",
"\n",
"\n",
"## Moyennes Mobiles Intégrées Auto-Régressives\n",
"\n",
"Le processus général pour les modèles ARIMA est le suivant:\n",
"* Visualiser les données de la série temporelle\n",
"* Rendre les données de la série temporelle stationnaires\n",
"* Tracer les graphiques de corrélation et d'auto-corrélation\n",
"* Construire le modèle ARIMA\n",
"* Utiliser le modèle pour faire des prédictions\n",
"\n",
"Passons par ces étapes !"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Étape 1: Obtenir les données (et les formatter)\n",
"\n",
"Nous utiliserons quelques données sur la production laitière mensuelle.\n",
"\n",
"C'est déjà enregistré en csv pour vous, chargeons le:"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import numpy as np\n",
"import pandas as pd\n",
"import statsmodels.api as sm\n",
"\n",
"import matplotlib.pyplot as plt\n",
"%matplotlib inline"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"df = pd.read_csv('monthly-milk-production-pounds-p.csv')"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
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"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Month</th>\n",
" <th>Monthly milk production: pounds per cow. Jan 62 ? Dec 75</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>1962-01</td>\n",
" <td>589.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>1962-02</td>\n",
" <td>561.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>1962-03</td>\n",
" <td>640.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>1962-04</td>\n",
" <td>656.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>1962-05</td>\n",
" <td>727.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
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"text/plain": [
" Month Monthly milk production: pounds per cow. Jan 62 ? Dec 75\n",
"0 1962-01 589.0 \n",
"1 1962-02 561.0 \n",
"2 1962-03 640.0 \n",
"3 1962-04 656.0 \n",
"4 1962-05 727.0 "
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.head()"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"data": {
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"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Month</th>\n",
" <th>Monthly milk production: pounds per cow. Jan 62 ? Dec 75</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>164</th>\n",
" <td>1975-09</td>\n",
" <td>817.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>165</th>\n",
" <td>1975-10</td>\n",
" <td>827.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>166</th>\n",
" <td>1975-11</td>\n",
" <td>797.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>167</th>\n",
" <td>1975-12</td>\n",
" <td>843.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>168</th>\n",
" <td>Monthly milk production: pounds per cow. Jan 6...</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
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"text/plain": [
" Month \\\n",
"164 1975-09 \n",
"165 1975-10 \n",
"166 1975-11 \n",
"167 1975-12 \n",
"168 Monthly milk production: pounds per cow. Jan 6... \n",
"\n",
" Monthly milk production: pounds per cow. Jan 62 ? Dec 75 \n",
"164 817.0 \n",
"165 827.0 \n",
"166 797.0 \n",
"167 843.0 \n",
"168 NaN "
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.tail()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Nettoyage**\n",
"\n",
"Nettoyons un peu tout ça !"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"data": {
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"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Month</th>\n",
" <th>Milk in pounds per cow</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>1962-01</td>\n",
" <td>589.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>1962-02</td>\n",
" <td>561.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>1962-03</td>\n",
" <td>640.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>1962-04</td>\n",
" <td>656.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>1962-05</td>\n",
" <td>727.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
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"text/plain": [
" Month Milk in pounds per cow\n",
"0 1962-01 589.0\n",
"1 1962-02 561.0\n",
"2 1962-03 640.0\n",
"3 1962-04 656.0\n",
"4 1962-05 727.0"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.columns = ['Month','Milk in pounds per cow']\n",
"df.head()"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
"# Dernière valeur bizarre en bas causant des problèmes\n",
"df.drop(168,axis=0,inplace=True)"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [],
"source": [
"df['Month'] = pd.to_datetime(df['Month'])"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
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" <th></th>\n",
" <th>Month</th>\n",
" <th>Milk in pounds per cow</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>1962-01-01</td>\n",
" <td>589.0</td>\n",
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" <tr>\n",
" <th>1</th>\n",
" <td>1962-02-01</td>\n",
" <td>561.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>1962-03-01</td>\n",
" <td>640.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>1962-04-01</td>\n",
" <td>656.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>1962-05-01</td>\n",
" <td>727.0</td>\n",
" </tr>\n",
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"text/plain": [
" Month Milk in pounds per cow\n",
"0 1962-01-01 589.0\n",
"1 1962-02-01 561.0\n",
"2 1962-03-01 640.0\n",
"3 1962-04-01 656.0\n",
"4 1962-05-01 727.0"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.head()"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [],
"source": [
"df.set_index('Month',inplace=True)"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
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" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Milk in pounds per cow</th>\n",
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" <tr>\n",
" <th>Month</th>\n",
" <th></th>\n",
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" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>1962-01-01</th>\n",
" <td>589.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1962-02-01</th>\n",
" <td>561.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1962-03-01</th>\n",
" <td>640.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1962-04-01</th>\n",
" <td>656.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1962-05-01</th>\n",
" <td>727.0</td>\n",
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"text/plain": [
" Milk in pounds per cow\n",
"Month \n",
"1962-01-01 589.0\n",
"1962-02-01 561.0\n",
"1962-03-01 640.0\n",
"1962-04-01 656.0\n",
"1962-05-01 727.0"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.head()"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"data": {
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"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>count</th>\n",
" <th>mean</th>\n",
" <th>std</th>\n",
" <th>min</th>\n",
" <th>25%</th>\n",
" <th>50%</th>\n",
" <th>75%</th>\n",
" <th>max</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>Milk in pounds per cow</th>\n",
" <td>168.0</td>\n",
" <td>754.708333</td>\n",
" <td>102.204524</td>\n",
" <td>553.0</td>\n",
" <td>677.75</td>\n",
" <td>761.0</td>\n",
" <td>824.5</td>\n",
" <td>969.0</td>\n",
" </tr>\n",
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"text/plain": [
" count mean std min 25% 50% \\\n",
"Milk in pounds per cow 168.0 754.708333 102.204524 553.0 677.75 761.0 \n",
"\n",
" 75% max \n",
"Milk in pounds per cow 824.5 969.0 "
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.describe().transpose()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Étape 2: Visualiser les données\n",
"\n",
"Visualisons ces données avec quelques méthodes."
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x1c1ead22d0>"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"df.plot()"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [],
"source": [
"timeseries = df['Milk in pounds per cow']"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.legend.Legend at 0x1c1ef56550>"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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K0xZMcAdyo1V69lV1DAu8hZPNVqrbe3AND8yKUCuYnRhJgW42Ff3vs/HB+cyKi0WlkE9o1yO6756t5eMm+MnVCxCdGrfrxOGku89Oq2UAY3c/JfXd2FARmgY3/OlttGI++YmR5CdFkhKtITREjkapQBMixyWKWPsdWAeGsA042NtRCcDPNjbQM1CPbcB9rqGrD/uQO+icpNNw2cxYlmXl8P9OP8fq+SL/uWj2tD4fb8xANblPP0yl4IqMRfz6KHQ7z5IQOXfMmqmynyTRl/iHxVdh8YU6i7uT4ZBriN3G3dyZd2dQ7TdaGymKK6JQX0iMOobDrYeDJvqeu5S0SLfoe4aQlJnK/BZ9j+jEaCbPQ1+csJgtdVuotdSSqcuc0m6zuZ/3S42cajcyYJ3Bts8OApAQqSYvIYLV+fHkJUYyOzGC5CgNgiCw22jn37a/h0NuRKWYPN4SopBRlBrF7g4nimYF31yaN+nvTIt1EVd/uJ4riwbQDcZzotnC+j01OJyTj2rUxDeiiFRzqrkXrVpBxPAdxMqcWOalRjEvLYq4CLV3/Ya2bCq7K6f8fDx0DXShDdGilI9fuDaSZG0y0epoytvLuS3ntjHnXz/1+qSPl0Rf4h8S84CZr2z4Co8veZwr064Muv16az0zo2bSP9TP58bPgyr6dqedlt4WbtDegCAI3mZfwaLOWodckJMS7u7nnqJNIUYdw1HT0XFFYDp4dvpTFR95/Pr7W/aPK/pOl8iO0yb2V3dyzGimtKEbUXShnWVlfnI637l2IXkJEcSEqyZ8jryYPAAqOisoji+e1uvvGugiSh015SYhISKSGbpMBHUjT11XCMDgkBNzn4M+uyewOoRMEIjQKN0Cr1by+L5dVHTF8fG9K6b1emZGzWRL3ZZJNy6Ntkb6HH3kROdMq/DLg6fd9UQ7+qkqmqWUTYl/SCo6KzAPmnn+6PMBd0YcD89g7suSLqOkrSSgdrjnY+wxIiJ6q1rz9fnUWmqDVqhVb60nKTzJuysUBIEiQ1FAIwE9PuupdvrJ2mSSwpPGpG4OOJy8dqCeVf+7k2+/WsKbh+pxiSIPXp7NxofmgODiK7NncWl27KSCD6DX6IkLjfMpF72rf/LCrJHkRudytvus92eVQk5chJoMfRh5iRHMT4umKDWKtBg1MWFK1Eq5+6IyhetlJDOjZmK1W2nraxv3vCiKfO+z7/HDXT90v/4pCsvOZ65hLg22hjFjLC2DFow9xkkfK4m+xD8klWb3rXGVuYrPjZ8H1fagc5DmnmbSI9K5NPlSHC4H+1v2B82+J3MnVTvcmng4PzyQgpqR1FnqvPNTPRQZimjqafKmRvpKZ38nMkE2LWErjC3kRKc7D72zZ5DfbjvL0qd28NiGE0RqlDz39Xmc+K+rue3yRopzW1Go3Be7yapxzycvJo+Kzoppr+8anL5oZkVlYeozYRm0TLruBzt/wPd3ft9n++AWfWBCF8/B1oOc7T5LvbWePkef905lunhaVhxtP8qzZc/y3/v/G2Ban5kk+hL/kJztPku0OpqEsAReOvFSUG03WBsQEUmLSGOeYR4quSrgJlYj8fajHxb92Xp3MC8Y/ehNfSZqLDVkRo52rXjy2/3t8Ng50IlOpRtTlDUeBfoCWntb+dEHu1n61A5+u62Seak63rl/MRv+bRlrChOwOSw8fehpnil5BlOv+0IUFxo37dczO2Y2dda6Se+OttRtYV/zPsC905+uaGbrsoGJBRmgrbeNnY07KWktQRRFn+wD3tjKyDuKkbxW8Zr3+0pzpc87fc9QlacPPc2Lx1/kvbPv0dbbNq2NhST6Ev+QVHZXkhOVw92z76bMVBa0odBwTpTTI9NRypXkRuf6VaU5EQ22BrQhWiJVkYC7UVZaRFpQnuP5o88jIo7x3c+KmYVGoaGszT8XT2d/55SuHYCyhm7+VuJ2K318+hBr5yax7QeX8ae7F7AoM8brv95at5UhcYhaS613vu5U1bgj8VwoJ2qZUGep49HPH+XZsmcBd0XudEUzO2pY9M0Ti/7mus2IiNgcNpp6mjAPmn0S5YiQCBLCEjjZeRJRHB0krrHUsNu4mxtm3ADAma4zPttXyVXkxeTR0tvCJUmXALCzcScVnRWjZveOR0CiLwjC9wVBOCkIwglBEN4SBEEtCEKGIAgHBUGoFAThHUEQQobXqoZ/rho+nx7Ic0t8sYiiyObazUH1hXtwupzUWGrIjsrmhhk3ICCwvzl47hdv9stwymNhbCGnuk4FrV1Co61xwtbEgVDVXcWHVR9ye87tY/6wlTIlRYYiDrUe8st250DnhIFEl0tk68lWbv39Pm56fh/Ha8MRkHH35TKevqWQLIN2zGM+rvmYpPAk5IKcjVUbERCmdVHx4AnmHmo9xOmu02N2/M+UPMOQa4jK7kp6Hb30OnqnLZpxoXFoQ7ST7vQ/rvkYrdL9vg63HsYpOn0SZXC3TPi0/lOufv9qNlRt8B5/+/TbhMhC+N787xGmDONw62FcosunOwmAO3Pv5M68O/nd5b8jVZvKjsYdnOw4yeyYydNE/RZ9QRCSgIeAYlEU8wE5cDvwNPAbURSzgW7gm8MP+SbQLYpiFvCb4XUSFyknOk7w8O6HefLAk0G33WBrYNA5SHZUNuEh4aRGpHKm60zQ7NdZ6jBoDIQpwwD3OMBB5+CEt+K+0mBt8Lp2PBToCzD1m2jrHT+wNx3Wla0jTBHGvxT+y7jnF8YvpMpcRUd/h8+2x9vpO5wu3is1cuVvdnH/a6U0mwd44it5HHj0GmZF51BjHX+AS6OtkaPtR7ll5i0sTlzMgHOAGE3MqJmvUxGtjiYxLJHfl/+eW/96K4/vfdx7bk/THnYZd1GgL8Dusntdc9MVZUEQyNZNnFJZ1V3F6a7T3Fdwn3vDMRzv8VWUH1v8GD9f9nPCQ8J5puQZb0LCvuZ93hbQ2bpsDra401inm73j4ZqMa/jxgh8jl8m5PPVyDrUcorm3+cKJ/jAKQCMIggIIBVqAy4H3hs+/AniSk28Y/pnh86uEC5WELXHB8dwaf1zzMZtqNgXX9vAfo+c2PCcqx6/OiBNRZ63z5riDuzQf4FhHYBOJwJ2F0dLbMqYffaCTm5wuJ583fc6N2TeiU+vGXbMoYRHAtJui/WDnD3j3zLve1+0RzQGHk1f317HiVzv50V/KCVHIefZrRex6eAX3LssgTKWgQF/AyY6TuETXcGuEcxlWn9R8AsCajDWsTl8N+Oba8fCLS37BowsfZXHCYspMZV43yQtHXyBVm8oTS54AYG+T233ky048OyqbKnPVGNcLwKbaTcgFOWuz1pIWkeYVZV93+qHKUG7IuoE7c+/EMmihxlxDR38H9dZ65sW5aytmRs2cVrO4qViZspIhcQg4d5c0EX6LviiKTcAzQANusbcApYBZFIefHYxA0vD3SUDj8GOHhtf7dmmT+Ieh2lyNWq52j7878POgdHn0UGmuRCbImBHpbriVE52DscdIj70nKPbPr8BNCEtAr9EHPIYO4M1Tb+ISXVybee2o4znROShkCr9jE+ZBM07RSVJ40oRrZkXPQqvUcrD14JT2eh29fFr/KVvrttLn6KN/qJ9wRRS/31XNJU9/xuMbTxIfqebP9yzgk4cu4fo5iaPaB+Tr87E5bBxuPcwNG2/w+tbB7Q+fHzefhPAELk+9HKVM6VPmjofi+GLuyL2DK1KvoGugi+beZnrsPZzoPMHqjNVk6bLQKDTeYK4vO/GZUTPpcfTQ0tsy5twu4y6K44vRa/TkROdMq5nbZHg6Y5aZyig3uf//PYF3T5aPr6//fObEzvG+vtyY3EnXBuLeicK9e88AEoEwYPU4Sz2X0vF29WMus4Ig3C8IQokgCCXt7e3+vjyJC0y1uZqMyAyeWPIEPY4e9jTtCZrtyu5KUrWpqBXuCsdZ0bOAiTMhfKHB2oB50Dwq+yVYBVR9jj7eOv0WK1NWjsmuUclVFOgL/A60elw2402F8qCQKZgfP59DLVP79T2TnU51naK2y33BfmF7G0/97TS5CVrevn8x731nCStnGcYtLvLcuTyw/QFqLbVsrtvsfZ1V5iouS74MAG2IlieWPMHdef43gyuIPXeXVN5ejkt0MS9uHnKZnJlRM70xGl/cI95gbnclTpfTG8/pdfRS1V3lrXLOicrxPsZf0U/RpqDX6CltK+WI6QghshDvbtzzOgKxD+7W19dlXsec2DloQ8bGWEYSiHvnCqBWFMV2URQdwAfAUkA37O4BSAaah783AikAw+cjgTFz3kRR/KMoisWiKBbHxvq+O5D4+1BlrmKGbgYZkRmEyEJosDYEzXZldyVZunPtBDx/eMFw8bx5+k0UMgVXp1896nhhbCH11nrMA2a/bb9f+T5Wu5X7Cu4b93xxXDEnO0/6Ffz2FOFMJvoAi+IX0WBroKVn7A52JB4XmtVu5bZX3gEgz5DERw8s47VvLmLxiEyc8ciIzCBUEYpLdHFN+jU09TTR3NPsdS0tjF/oXXtD1g0sTFg4kakpyY7KJkQWwvH245S2lSIX5MyNdeep50af29VO1QFzJJ7fr49rPmb1B6v5wc4fAO4Li4jInNg5gPsOzcNEbrWpEASBeYZ5lJnKOGI6Qr4+39tt1CP6AgI6lX/2Pfyo+Ee8tvq1KdcFIvoNwGJBEEKHffOrgArgM+CW4TV3AxuHv/9o+GeGz+8Qx3OoSQQNfwJ606HH3kNbXxszdDOQy+SkRqR6d1uB0j/UT6OtcdQOyBBqQKfSBbzTt9qtfFj5IavTV4/pZOjx6/u72xdFkdcqXmN+3HyvYJzP/Lj5OEWnX+MAp1sx6xHXibJ4hpwuTrdaeaXkgPdYUoI7hfWJNYsoTJ6e8Mhlch5f8ji/v/L3fKvgW4B7sMfh1sOEKcO8d2fBQClTkhuTy4mOE5SZypgVPYtQZShwzn8dIgshVBE6bZvaEC0JYQlsrttMW18bnzd9jmXQ4nW/eO4uPO8jIiRiwoHu02Fe3Dxae1s50XnC69rxvI7EsMRp10hMhiAI0+pV5XfvHVEUDwqC8B5QBgwBR4A/ApuAtwVB+PnwsfXDD1kPvCYIQhXuHf7t/j63xNSc7jrNrX+9lT9c+QeWJi4Nqu1qi3syj8fnnhaR5vOko4nY0bADEdFbcQjuX+ac6MCDuR+c/YC+ob5x++zM1s9GQOB4x3EuTb7UZ9vmQTMtvS2T9vCZa5iLXJBT0lbC0iTf/k+m494Bd+FRlCqKw62HKdZfxdlWG2fabJxpdf+rau/BPuQiLK0SXXgaPaKRwZBTMOB79siaTPdIRZfoQqfScajlEOXt5cwzzPMpU2c6FOgLeO/se2NqFDyiHK2J9rk53y0zb6HB2sDV6Vfz3e3fZW/TXo51HCMzMpOIkAjAHYCOUkV5ay78xePXd4muUaIP7t+95p7m8R52QQjof0YUxSeAJ847XAOMuZcTRXEAuDWQ55OYPp6mSxuqNgRd9GvMNcC5W+TUiFR2GXfhdDkD3q28dfot0iLSvI29PORE5fD26bcZcg35JShOl5M3T79JcVzxuIGuMGUYWVFZfmfwtPe740+TBSzDlGHkxeT5Vf3b0d+BRqEZs5t1OF00dvVR19lLbUcfVSYb9t5kNpzaz+sf7/CuS4hUMzNOyyXZerJiw1hX1cmVaVdQ3q70+vd9cY+MRCbIKI4rZpdxF+ZBMzdn3+yXncnI1+d7u0fON8z3Hs/SZaGQKXzqi+Ph/sL7AffvRrQ6mp2NOznWfoyVKecG3giCQHF8MQ5XYDUc2bpswpXh9Dh6Rm1owJ3a6c+wdn+Rumx+QfQ6enm14lXuy78PlXzyBlT+4HG3fNbwGb2OXm9OejCoMlehkqtIDE8EID0inSHXEM29zVNWA07Gyc6TlLeX88iCR8YMA5kVPQu7y069td6nMXoeWnpbaOlt8f6hj0ehvpBP6z/1q6VzR597Jz5VamJxXDGvn3qdgaEBb6B6KkRRpMlmIkwRxRsHG6hp76W2o4eajl6M3f04Xee8pFGhSqKTMukNOcFj12cwJzGe7DgtkZpzron2vnaerLCQHZWNw+WgylxFpCoyIPdFcXwx2xq2AbAgfoHfdibCEzgGKIo7t9zQJl0AACAASURBVFNWypUU6Av8Sgn1IJfJuTTpUjbVbmLINTTGPffLS3/pt+2Rz7EoYRHNPc1j7hoCydrxB0n0vyA+a/yM548+T2Zk5pigYjCotdSiUWjoH+pne8N2rp9xfdBsV1uqyYzM9O7qPemPDdaGgET/7dNvo1FouD5r7Gv1BNROdZ3yW/TB3SVyIgr0Bbxf+b53/qwveHf6U4jP/Lj5/PnknznecXyMOLpcIs2WfipNPVSbeqga/ldp6sEeW4kgKPnpkROolTIy9OHkJ0Vy/ZxE0mPCSNeHkh4TRnRYCPtbwvmXTzeSl26hOHFszra3DkKXjUt08VH1Rz67ds7HE7gNV4YH1Z/vIUWbQqQqkhh1zJgsl2dXPhvwHeaKlBVsrHaHHwtjC0edC9am7MllTwZ8xxAMJNH/gvB0YtzfvP+Cif4lSZdQ0VnBpppNwRV9c7XXRwnnRL/OWseypGV+2bTZbXxS8wlrs9Z6/akjyYjMQCVXUdFZwXWZ1/ls3yP6CWEJE67x/LEf7zjut+hP5XMviitCQGBXwwHMXamcbbMNC7uNalPvqMHW0WEhZMWGs6Ywgb39dlK0aTz9tctJiJh8PF6BvgABgWPtx1iSuGTMeU9hXVZUlveOypcWCeMxQzeDaHU0hfrCgAV4PARB4DuF3xnXt+5vVs1IliQuQSlTopKr/NpUTIepUin/Xkii/wVRb3NnTOxr3hf0CVF2px1jj5HVGatJj0hn/Yn1tPe1+1Ugcz499h5ae1tHpVTGqGMIU4Z5G5n5Q4OtAbvLPmGAUylTMit61pQDIibCk8I4WafHzMhMQhWhlLeX+zwDtqO/gzBlmDerxIPTJVLX2cupFiunW2ycarEiOJJYX7KN/gZ3Ln9CpJosQzi3L4wmyxBOVmw4WYbwUX3nL33bxqzYJJJ0milfizZES0ZkxoSZSJXdld4dc0i0O3UwkBxxcPv1/3DlHwJOO5yMb+R944LZDlOGcWXale45vH7OGb5YkET/C8KT197S20KdtY6MyIyg2W60NeISXWREZpARmcGLx1+kpK2E1Rnj1c75hqdj4siiFUEQSItIC0j0p+MTz9fn80HlB34Fc1t6W4hWR0/qR5fL5NMq0rL0O2js6sPY3YcgCISFKDje0kgIOp7ZcoZ22yAm2wBt1kFqOnoYcLiG7QvMiA0jUZ9Hi2onL91fTH5iNFr1+L70OksdsaGxhMhCMA+afdqNF+gL2G3cPe6GotJc6U2JDQ8JZ03mGhbFL5q27Ym4EG6dvydPX/blaAcmif4XRIOtgWWJy9jbvJd9zfuCKvqe9Mn0yHR3Lr0g92ZoBIJLdPHHY38kPSJ9TEZQWkRaQG0MpuMTnx0zmzdOvUGNpWZU+fp0aO1tndS146EgtoCXT7xMr70fk9Xldb1UDfvZ67v6MPeN9ctq0hpADOH541Xow1XEalXERahYnBlDboKW3IQIsgzhqJVyttcP8b2dW1GHN6NVj3/n0WBt4KaPbuKO3Du4I/cOYGrX0UgKYwvZWL2RanM1Oxp3sDhhMYWxhdiddqq6q/h67te9a5+69Klp25W4+JFEfwIsgxY2VG3gjtw7gp5zbBm0YBm0sCRxCcYeI/ub93v/sIOBR/QzItx+8NSIVKq6Axf9nY07Odt9lv+55H/G+G3TI9LZXLuZQeegX4Gv6fjE8/X5gDsd1VfRb+ltGdMawT7koqvXTkfPILUdvVSZejjQFsaQOMS8p19msPdc07S4CBVZhnDWFCSQFhNKanQoyVFuV07v4BA/Kfk/8mJm85vvXIt8En874G22VdJaMiZn28OvSn6Fw+WgzFTGNenXAKBX+yb6AHdvvhur3Up5eznPrXqOk50nsbvsY9IGJb48SKI/AVvrt/JMyTMka5NZlboqqLY9rp0UbQqLExbzUfVHOJwO78zTQKm11BIXGuf1L2fpsgJuTSyKIn849gdStCnjuolSI1IRETHajH4Fwjr6OtCpdJN+BmkRaYQrwznZeZIbs2+cll2XS+RMmxWjrZkooYCn/naaZnO/t1BpZLqjIEBSTCzEwqJcG9elF5JtCGeGIZyICVww4P5srPu7SI6Im1LwwZ2il6XLorStlG/z7THn9zXtY2fjTvQaPac6T9Hc6y7c8WWnn6XLIlwZ7p2fW9JagsPl8E7W8rQxkPjyIYn+BDTa3Nk1m2o2BV30PUHctIg0RFHknTPvcLLzZNB2X3XW0TNUs3XZbKvfRp+jb0ygcbqc7T5LRWcFjy1+bNw7n/QI9/NVm6v9Ev32/vYpRU0myMiLyRs1jEQURVqtA1Sb3LnrXb0OrAMOrP0OunrtlDZ0Yx6woM0ZYO9pJ3utNcRHqsk2aLkiz0CiTkN0aAhpMWFkxoahVsq59oM/oNMZua144vRTh8vBj3b+iK/lfo38mHz6h/p9yhWfHzefv1b/dUx8QhRFflXyK1K1qXx37nd59PNHvV0kffHpK2QKXlv9Gjq1jrK2Mn6464dUdFZwpO0I6RHpAWfrSFy8XLSiP+gcpKq7yjtWLdgYbe6J8rsad2G1W8dNI/SXBmsDAgLJ2mRv46UaS01QRF8URWottaPSGrOjshFxH/f382rrcw//GNmAaiQzo2aikqs4YjrCVelX+Wy/o79jWqKZFp7DB9Vv8djGoxwz9lLVZqPX7hy1JixEToRGSaRGyRW5caQnqPhDNTx1w6XcOHP1pOmOAPMM89hl3IVLdE2YyfFZw2fsaNxBhCrCmxHky068OL6Yd868w6nOU94+L+D+nKvMVTy68FFvWuzOxp2A72mVnjmtnnqAA80HONp+lBUpK3yyI/HPxUUr+hurNvLkgSf5aO1HQQ2CejDajBhCDZj6TGyr38ZN2TcFzXaDrYH4sHh3VWtYIiq5ytvaIFA6+jvocfSM+kw86ZWV5kq/Rd/T5XGiIp4QeQhzY+dyuPWwX/bb+9vH/D86nC4qmq2U1ndT2tBNWX037S4XmuQh3jt+mDmGfG4tTmGGIZwZsWFk6sOJCQ9BKR8t1DsadkA1zNKnTSn44N6Fb6zeSK2ldsK7lr+c/QsAx9uPe/vinN/EbTKK44oBeHzf4yxJXMIduXeQFJ7kzRyaEzuH+LB4DBoDpn4T2hCt30VCUeoocqJy+KDyA8yDZm/bYIkvJxet6HuClTsadvDNgm9Osdo3RFGk0dbIdZnXsb9lP5tqNgVX9K0N3slKcpmc9Ih0aizBEf0tdVuA0X26U7QphMhCAgrmTqfL44L4BTx39DksgxafGlSJokhHfwdaRTTbKtoobeimtL6bY0azN90xSaehOD2a7ITLebH+DX56Uxh35I0tPBpJR38HMeoYb2FWfFj8tF6PJ9Ba2lY6rug3WBs40HKAaHU0NZYa7++iL+4dvUbPD+b/gM8aP+PNU2/SY+/hZ8t+xvGO4yhlSm+guiC2gO0N2326ixiPhQkLea3C3XZXCuJ+ubloqxCaepoAdzuDYGMZtNDj6CFFm8KajDUcbj0c1DbFDbYG0rTnJjdlRmYGRfQruyv5TelvuDTpUu9OEtwXlhm6GQGlbXb2dxKmDEOjmLg4aEH8AkTEaY/rK205xqbjRr7/3l6GXEOs39XJt14t4cXdNQwOufj6wjSe+/o89v/H5ex99HL+72tFPLh8AQaNgeOdk6eH1lnquOq9q3it4jVae1sJkYVMuwApVZuKXqOnzDT+wJP3zr6HXJDzvXnfQ0Rkl3EXAPpQ34T53vx7eXX1q6xKXcXepr2Iosjx9uPMip7ldft5es4EKvqePPwoVZQ3/iLx5eSi3ekbe9w+92Ptx+jo7wj4j2IkniBuijaFCFUEz5c/z6nOU3613D0fT7rmyBmqGboMNtdt9qkJ1/nYnXYe+fwRtCFanlz25JiCnCxd1rTG6E3EdD7jfH0+armaw22HWZU2fvC7zz7EhiPNfFixj9OyX9DffCsaVyokw/X5s/hq3hIKkyNRK8cv5RcEgTmGOVP2pH/x+Is4XA5erXiVfH0+CeEJ06569g69GGfKlcPlYEPVBlamrPR2YzzQfACVXIVW6V+Z/SVJl7C1fitnus9wsvMka7PWes95Ui99Sdccj/lx85ELcooMRUGt/pa4+Lgod/ruroNNLIpfhIjoDXQFC88FJVmb7O0ZHyz3i6dqNVV7TvQzIzMREQMaRFLaVkpldyWPLnp0XBdMVlQWpj4TlkGLX/Y7BzqnbMoVIg9hrmF8v77JNsC6bZUse2oH//nhcWoHdgJw9bwBnr/LHXO4ozifhRnREwq+h7mxc2nqaaK9b/xxmo22RjbVbCI3Ope2vjZ2Ne6aVmHWSObFzaOlt4WPqj/i37b/m/cCcKLjBN2D3azOWI1OrSNFm4LdZUev0fstpp5Ct9cqXqN/qH9UR8nZMbORC/KAW2iEh4TzxJInJu0yKvHl4KIU/e7BbvqG+liespyk8CR3oC6IeHb6ydpkdGodMeqYoFS0gru/vUJQjOrp7ikaCiSY6xlMnh+TP+55TzDX3/fR0d8xreyRBfELONt9lu6Bblwukc8r2/nX10tZ+ssd/GbbWealRvHW/fMI0bndM52OGroG3fGC6frEPT7po+3j7/bXH1+PXJDz7OXPuts+i0M+i74nc+Yne37CbuNurz98X/M+ZIKMRQlud4mnYCyQ1r5xYXFkR2WzqWYTMLqNcKgylOdXPT/pcJbpcmP2jRcs203i4uGiFP0mm9ufnxyezMqUlRxsOejX3NGJMNqM6DV6r/96hm5GULJrasw1fFD5Abfm3DoqqJgWkYZMkAV0N2HqMwETZ5B4euX4W6TV2d85LReap8XuL3Z8zIpndnLn+kMcqOnk3mXp7PjhctbfswCr7Bg2u428mDzOdJ85V3w0TZ94bnQuIbKQcV085gEzG6s3cmP2jcSHxXsrnX0V/WxdNrfMvIWHix/m5uyb2dO0hz5HH/ua95Gvz/cGqj1jFgPdiV+SdAlO0Yk2RDvK9QewNGnptIPQEhJTcVGK/kj3S3F8MXaXnWpzddDsN9oaR/WFn6GbQbWlmkBH+v627LeoFWq+M+c7o46HyENI0aYEJPptfW1EqaK8AcDzMYQaiFJF+TVy0O60Y7VbJ3XvDDldfF7Zzu+3DiK6lPz1zB4SItWsu30u+/9jFT9Zk0dmbDgAH1V/hCHUwD2z72HINcT+5v2EK8MnDRKPRClXkq/PH3enX2oqZcg1xLUZ1wJw/YzrWZ683OfxhHKZnCeWPMFds+9idcZqBpwDbK7bzImOE6P6Dnl2+oHGlC5JvMRtLyb/n77Lo8QXy0UZyPVk7iSFJ3mn/dRYakYVuQSCscfIgrhzAy6ydFn0Onpp62vze8dV0VnBZ42f8VDRQ+NmkWREZgQ0Z9bUZyIubOK2wYIgMCt6ll+i3zXQBYwVtp7BIXafbefTijZ2nDZh6XegC1USn5WLKrWVd24em1LZPdDN3qa93Jt/r9eNUd5ePirGMR3mGObwWsVrnOk6w5un3+Se2feQEZlBSWsJKrnKK8ahylB+t+p3Pr/nkcyPm49OpWNd2TpcomuU6M+KnkWUKmpUiqw/FBmK0Gv0LE5cPPViCYkAuChF32gzEq2OJlQZSrI2GaVM6R3WHSh2p5223rZRO32Pz73KXOW36J/sdPeBn2gASGZkJnua9vg9A7atr23K4qBZ0bN4/dTrOFwOn0bjedJVYzQxtFoG+PRUG9sq2thf3Ynd6UIXqmRVroErc+NYOcvA66cbWFe2jq6BrjEXuCOmIzhFJ8uT3fGYiJAIrHarz+6RubFz+bPrz9z28W24RBcCAv+19L8obStlTuycCe94/EEhU7AyZSUfVn1IuDLce0EBUCvUbL1la8DPp5Qr+eSmTy7I6EwJiZFcnKLfY/SOvVPIFKRFpAWtorWppwkRcdRYPU8QtNpczSVJl/hl12gzopQpJxTmGboZDLmGaLQ1+lVhbOozjQoAjses6Fk4XA5qzDUTtlPwMOQa4k/lr5ITdiXb69wXrJ9/1EhVYy8AaTGh3LUkjSvz4pifFoViRBWsp0agtK2UK9OuHGX3WPsxFIKCWdGzEASB2TGz2d+y32f3yDzDPHQqHQviF2B32vm0/lMemvcQp7tOj3GfBYMr0q7gw6oPWRi/cMwF09802/OZrntLQiIQLk7RtxlHzbGcoZvh90Sl8xmZo+9Bp9YRrY4OKG5gtBlJCk+acJScJ9B6ouOEz6Jvd9rpGuia1k4fYGftUdo7Y+joGaSjZ5DOXjudPYN09ToYcrkYdLg4azuMXf9HBlrcKabqBIhURvPw1TlclRdHliF8whTF2frZaBQaDrUcGiP6xzuOkxOd4xXK2Xq36Pua/aJT69j91d0IgsBu4252GXfx3JHnEBFHFaYFi8UJiynUF3JD1g1Bty0h8ffkohP9IdcQrb2t3kAdwIzIGWyt2xpQcZOHTTWbCFWEjhoHCO7dfkCi32MkSZs04fnsqGy0IVpK20p9HtXnydwZOQpwwOGkydxPZZuNM609nGmzcrrVghit5Ne7P2OwLdy7ViETiAkPISo0hBCFDIVMIDneRM0QrCzsJ0efxsun4K1vXjktN4ZSpmSeYR4lbSU4nA72NO1hWdIy5IKcEx0nRs3rnR3jTiH0J+XRc9FZkrCESFUkfzn7FxQyxZjB1sEgRB7CG2veCLpdCYm/Nxed6Lf2tuIUnaPcL5m6c8VNgYxsa+ppYkvdFu7IvYPwkPBR5zIjM/lrzV/9nmdrtBkndb/IBBlFhqIJS//PxzU8e7VncIgjJveg60+O9vHm9n1UmXroHjHdSRAgNTqUnLgIREU62hQbP127hJjwEPRhKiI0ijHv6f6tr1DTAm32M6SLOiJCInzyWxfHF7OubB23/vVWqi3VPFT0EMtTltM31DdKlOca5hKqCJ3S3TQZSrmSK1Kv4P3K9ynQFwTN3SIh8c/IRSf6nsyd5PBzou+pmq02Vwck+q+cfAVBEMYthPFk8LT2tpIQ7lvOt2XQgtVuHeUyGo95hnnsNu4eNwAK7glNe6o62HHKxPYzLZiFwwxZC1FoT6BJhgNnh5gVA9fkJ5CkUxMfqSHbEE52XDihIe7/6if3F/G32r+xID1qwouXS3RxvOM4CpmCGksNhlCDz219FycsZh3rsDlsZOmyeOfMO+dy20eIvl6jZ9/X9k3o9pou12Zcy/uV718Q146ExD8TF53on+o8BTBqp58WkYZckAfkfuka6OLDyg9Zk7Fm3AydWTHui0lFZ4XPoj/ehWo8PFWgR9qOcHnq5Zhsg1Q0Wyk3mtlf3cmRBjN2pwutSkF29kkGne9w7/IcbEM63quDPT9aO2V3y1kxs3j37LsYe4wTXoRqzDX0OHq4fsb1fFT9ESVtE4/1m4h8fT5/uupP5MXkcbj1MP/+2b/z4vEXiVRFjknPDFTwwX1n8e/z/n3C7CgJCQk3F5XoWwYtrD+xnvlx80dVWCrlSlK0KQHlue827mbAOTDhrNpZ0bNQyBSUd5RP2ExsIjwDWUZeqEbidInUdvRQbdQhR8kvdmzix0YXnb12wO2emZ0Ywb3L0lmRY6A4PYp7Nr8KHdAnP4MmJASNQjOtQS950XmAe87sRKJf3l4OwNdzv+6d7jRV353x8LQqWJ68nISwBFp6W7gk6ZIL0vBLJsj4VsG3gm5XQuKfjYtK9F8ofwGr3cp/LPyPMcLhqZr1F0/vmkxd5rjnVXIVudG5HG8/7rNtTwVxUrg7kNtqGeBATSeH6ro42WzlTKvV2zc+LC0Fi+oMq3LvJi8hgrzESGYlaEfNaK3sruRYhzv18WDLQWZGzSQuNG5aYjozeiYahYaSthKuybhm3DXl7eXoVDryovPIisqisrsyoIpTuUzOV3O+ym/LfuttWyAhIfHFcNGIfo25hrdPv80t2beMG/TLjMxkZ+NOvweMm/pMRKmiJi2OKdAX8GHVhz4XUDVajWiVkbzwmZHtp0ycbrUBoFUryE+M5I5FacMCH8GW5mpePvkS/3VD9oTzbD+o/ACFTMFdeXfx0omXcLgcU7qOPChlSooMRZS2lU645lj7MQpjC91tjGPnUNldGfBM1Vtm3sLh1sNjUjglJCT+vlw0TT52GnfiFJ3869x/Hff8zKiZOEUnZ81n/bI/nYrWgtgC+of6px076OwZ5Ndbz/D+8XLM1gh+v6uGSI2S/7x2Fh8/eAlHH7+Kt+5fzGPX5XHz/GRyEyJYmFCMU3ROmMVjd9r5uOZjVqas5Jp09069qafJ51F9VeYquge6x5yz2q1UW6qZEzsHwPvVH/fOSCJVkfz+yt9757ZKSEh8MVw0O/2Wnha0IdoJ3QyedrvlpnJv7rcvTNW7BmCO3i2AxzqOjbrbEEWRnsEhWi0D1HX2cbbNxsHaLg7WdDI45EKfayZfl8vzd12BLnTytMciQxFKmZKDLQfHrf492HIQ86CZtVlryYnOIVIViWXQ4pvox7szXMraysbEJzx94+fGuj/PxQmLSQxLlFrySkj8k3DRiH5rb+uk7XHjw+KJC43jqOkoX8/9us/2TX2mUT1VzsflEhEd0YQpInnv+D5KjuVQ29lLm3UAk3WQfodz1Pr0xE6icl7hR/P+g1+UdrE4ZeaUgg/uUvwiQxEHWg6Me77MVIZCULAgfgEyQcbC+IV8Wv/plBeskcyOmY1KrqKkrWSM6O9p2uN9DeD+XLfcsmXatiUkJP6xuWhEv7m3mcSwxEnXzDXMnXCwxmR42hjEqAzUdfTSYhmg1dpPU3c/VaYeKk091LT30u9woklO4HjIMara2siMDaMwWYdBq8KgVREXoSYtJpRMfTj/d+xp3jlj4n/KfsyQODRh5s54LE5YzLNHnp2wYVluTK63T4tH9H3Z6YfIQ5gTO4fStlKsdiuf1n3Kmsw1qOQq9jTtYVHCIr/iIhISEv/4XDSi39LbwjzDvEnXzI2dy5a6LbT2tk7YDbOxq48DNZ20WgZosQ7Qahmg0WaECPjtljaesewctT4xUk1WnJZFGTFkGcI5NbCUjXV/Zucji9GGjD8TVRTdIxznx82nrbfN3SBumoFWOCf6B1sOsjpjtfe4w+ngRMcJbsu5zXvsirQr2N+83+c8+uK4Yl4of4G1G9bS3t+Oqd/E6vTVNPU0ce/se32yJTE1DocDo9HIwMDAF/1SJP6JUKvVJCcno1ROf5MWkOgLgqAD/gTkAyJwH3AGeAdIB+qA20RR7Bbc+YTrgGuBPuAeURSn1XOgx96DzW6bsijK69dvL59Q9A/VdvHwe+5RfdFhIcRHqImK7KMVuLkwjwXxc0iIVBMfqSYhUu2tZPVwsGUpG+pe4ojpCJclXzbuc1R0VdDW18YDRQ9QHFfMm6ff9KkfTF5MHlqllgMtB7gi7QpOdpxkTuwcKroqGHQOjhJ4vUbPusvXTdu2h8WJi3m+/Hmi1dGkaFN4veJ1ZMNx/WVJy3y2JzE5RqMRrVZLenq6NJhcIiiIokhnZydGo5GMjOk3aQx0p78O2CyK4i2CIIQAocB/AttFUXxKEIRHgUeBR4DVQPbwv0XAC8Nfp8STQz/VyLuc6BzUcjVHTUe5Ov3qcddckRfHrodXEBeh9g7g/lvt3/jxbvj20nlkRU2+I59rmItarmZf874JRX9Hww5kgozlycuJUkfx4wU/nuotjkIuk7MwYSF7jHu4b/N9HG0/ys+W/sw71NzXXf14FBmKePe6d8mKyuJs11lu33Q7fzj2B9Ij0n1yRUlMj4GBAUnwJYKKIAjExMTQ3t7u0+P8TtkUBCECuAxYDyCKol0URTNwA/DK8LJXgLXD398AvCq6OQDoBEGYVj+Dlt4WYGrRV8qUzNbP9laUjkekRklaTJhX8GHEfNmwqf3iKrmK4vhi9jbtnXDNZ42fUWQoIkodNaW9iVicsBhTv4mz3WdJ1abyuyO/Y3/LflK0KQGP5vOQG5Pr/cyWJS3D4XL4PS9AYmokwZcINv78TgWSp58JtAN/FgThiCAIfxIEIQyIE0WxBWD4q0dJk4DGEY83Dh+bEo/oT2dq1dzYuZzqPEX/UP+k6/Y17eMrH34F84CZ1t5WNAoNWuX4PvrzWZq4lDprHc09zWPONdoaqeyuZGXKymnZmojVGav5Ru43ePu6t/nZsp9h6jexr3lfUHb54/HdOd9FKVNyRdoVF8S+hITEPwaBiL4CmAe8IIpiEdCL25UzEeNdksZMGhcE4X5BEEoEQSjx3La09raiEBTT6rm+IH4BQ+IQh1sPT7jG6XLyq5JfUWetY2/zXneO/jTbGADeGan7m/ePObfbuBsgYNGPVEXyyMJHyIjMYH7cfK+9CyX6hbGF7P/6fm/TN4l/Pu677z4MBgP5+aNTkx9++GFmzZpFYWEhN954I2azecxj6+rqEASBxx57zHuso6MDpVLJAw884Nfrqaur48033/T+/PLLL0/L1ooVK8jJyWHOnDksWLCAo0enzthLT0+no8M99jM83N02vbm5mVtuucWv134+L7/8MoIgsH37du+xDz/8EEEQeO+994LyHMEiENE3AkZRFA8O//we7otAm8dtM/zVNGL9yA5fycCYrbIoin8URbFYFMViu8aOS3TR0ttCXFjctLoxFscXo1Fo2NW4a8I1W+q2UGWuQkBgX/M+TH0mn1IeMyMzMYQa2Ne8D1EUMQ+c+yPZ27SXVG0qqRG+Dfqeih8W/5DFCYtZnrw8qHZHIs1n/efmnnvuYfPmzWOOX3nllZw4cYJjx44xc+ZMfvnLX477+MzMTD7++GPvz3/5y1+YPdv/or3zRd8X3njjDcrLy/nud7/Lww8/7JeNxMTEoApyQUEBb731lvfnt99+mzlz5gTNfrDwW/RFUWwFGgVB8JSmrgIqgI+Au4eP3Q1sHP7+I+Auwc1iwOJxA02EedBMeXs5Lb0t0x5IrpKrWJq4lF3GXYjimBsJhlxDQR6zOgAAIABJREFUPF/+PNlR2VyVfhX7m/fT2tc6aurUVAiCwNLEpext3stNH93EindXcKrzFIPOQQ63Hr4gfvG0iDRevOpFnweIS0h4uOyyy4iOHjun4aqrrkKhcOd0LF68GKPROO7jNRoNubm5lJSUAPDOO+9w223n0ofr6+tZtWoVhYWFrFq1ioaGBsB9sXnooYdYunQpmZmZXqF99NFH+fzzz5k7dy6/+c1vAPfu+5prriE7O5sf/3jqBIglS5bQ1NTk/fmtt96ioKCA/Px8HnnkkUkfW1dX573refnll7npppvGfe7169czc+ZMVqxYwbe//e0J70YuvfRSDh06hMPhoKenh6qqKubOnes9X1payvLly5k/fz5XX301LS1u+XvxxRdZsGABc+bM4eabb6avr2/Szy1QAs3eeRB4Yzhzpwa4F/eF5F1BEL4JNAC3Dq/9BHe6ZhXulM0pk8FlyNhat5WWnhbmxU2eoz+S5cnL2d6wnTPdZ8YMVfm0/lPqrfX8duVvsQ5a2VLnrjb1ZacPsCJlBRuqNqCQKQiRh/D6qddZk7mGAeeAlPIoMSn//deTVDRbg2ozLzGCJ74SeKuMl156ia9+9asTnr/99tt5++23iY+PRy6Xk5iYSHOz+4b9gQce4K677uLuu+/mpZde4qGHHmLDhg0AtLS0sGfPHk6fPs3111/PLbfcwlNPPcUzzzzjvXt4+eWXOXr0KEeOHEGlUpGTk8ODDz5ISsrEw4c2b97M2rXuXJHm5mYeeeQRSktLiYqK4qqrrmLDhg3e81Mx3nPL5XKefPJJysrK0Gq1XH755RPu3gVB4IorrmDLli1YLBauv/56amvd7d4dDgcPPvggGzduJDY2lnfeeYef/OQnvPTSS9x00018+9vfBuCnP/0p69ev58EHH5zwcwuUgERfFMWjwHijisY0nBfd2+5/88V+WEgYW+u30tnfOWXmzkguTb4UAYGdjTs50HyA9yrf4+VrXkav0bOlbgsGjYGVKSu9WTvgu+hfnnI5W2/eSnxYPL84+As+rPwQURQJkYVI05skLkp+8YtfoFAouOOO8WdKAFxzzTU89thjxMXFjbk47N+/nw8++ACAO++8c9Ruee3atchkMvLy8mhra5vQ/qpVq4iMdA8CysvLo76+flzRv+OOO+jt7cXpdFJW5i73OXz4MCtWrCA2Nta7Zvfu3dMW/fGeu6Ojg+XLl3vvkG699VbOnp24qePtt9/Os88+i+X/t3fn0VVVd//H3zsDJBDmMIUggkwyZGAIgzIqsBgEaUsFlUdFV20fXdVVsGpVRC0VFftU2qe1jqiLn2K1rViHVsUQQBAEJYZJAXkEQSABQhIIZNi/P/ZJuMDNPHs+r7XuuveeYZ/vPffs79nn3HP3yczkySef5He/+x0AO3fuJC0tjXHjXC+zBQUFdOzoclpaWhr3338/x48fJzs7mwkTzl5uXt71VhH1+h+5LRq1KE7M5T29A+4PS/2j+7N061Jy8nIAWLF7BTN7zWTtd2u5uvvVhJgQOjTtQLcW3diTuadCfdeA26sX/VlsZq+ZLN+5nLf3vM2wjsNK7BJZBKiWFnl1e+mll/jXv/7FRx99VOoFDY0aNWLgwIE8+eSTbN26lbfffrvEaQPLadz47O9FwU67BpsuNDSU/Pz8oNMtW7aM+Ph47rnnHm677Tb+/ve/l1pueQRbdkXLTEpKIi0tjcjISHr27Fk83FpL3759Wbfuwos/brzxRv75z38SHx/P0qVLSU5ODhpTVT9fkXrdtXKzRs0ID3F/L46JKr3fnfON7jyanLwcpl4ylYS2Cfzj63+w9sBacgtyz7kssehKnIqc0z9f91bdGdLB/c9Mp3akoXn//fd57LHHWLFiBU2alN1gmTt3Lo899hht2pzb3fbw4cN57bXXAJeUL7+89N+2mjVrRlZWVqXjDg8P57e//S3r169n+/btDBkyhFWrVpGenk5BQQGvvvoqo0ZV7cKHpKQkVq1axbFjx8jPz+fNN98sc55HH320uIVfpFevXhw5cqQ46efl5bF161YAsrKy6NixI3l5eSxbtqxK8ZZHvU76ISaEy2JcEq3I6R2A2X1m89SYp3jkskf4cc8fs/fEXpZsXkLLxi3PuSzx6u5Xk9QhiW4tgt8xq7xu7HcjEaERjO08tkrliNSUWbNmMWzYMHbu3ElsbCzPP/884M7FZ2VlMW7cOBISEvj5z39eajl9+/blhhtuuGD4kiVLePHFF4mLi+OVV17hqadK7x4kLi6OsLAw4uPji3/IrajIyEjmzp3L4sWL6dixI48++ihjxowhPj6eAQMGMG3atEqVW6RTp0785je/YciQIVx55ZX06dOn+BRQSSZOnMiYMedest2oUSPeeOMN7r77buLj40lISOCTTz4B4JFHHmHIkCGMGzeO3r17ByuyWpnqOmSoCYMGDbJL3lrCHzb/gVcmvVLpSwpP5p1k7N/GkpOXw/Tu03n4soerOVKnonfUEv/Yvn07l156aV2HIZWQnZ1NVFQU+fn5TJ8+nTlz5jB9+vS6DqtYsG3LGLPJWhv0x8V63dIHGN5pOK9f9XqVriFvEt6kuLfKmvzHqRK+yA/PggULSEhIoF+/fnTt2rXcPwzXV77JUrf0v4WmYU0Z1nFYXYciIg3I4sWL6zqEauWbpN8pqhPzBs+r6zBEROpUvT+9IyIi1UdJX0TER5T0RUR8RElfxCfUtXLNda186NAhpkyZQnx8PH369GHSpElA2T2Jjh49urgDu9qipC/iE+pa+VzV2bXy/PnzGTduHFu2bGHbtm0sWrQIqNpnrClK+iI+oa6Vz1WdXSsfPHiQ2Niz95aOi4sL+hlPnTrFzJkziYuL45prruHUqdLv8FcTfHPJpki98d498P2X1Vtmh/4wcVGVi1HXypXrWvm2227jmmuu4U9/+hNXXnklN910EzExMRd8xt///vc0adKE1NRUUlNTGTCg/F3GVxclfREB1LVyVbpWnjBhAnv27OH999/nvffeIzExkbS0tAumS0lJ4Ze//CXgjgaKjghqk5K+SG2rhhZ5dVPXylXvWrl169Zce+21XHvttUyZMoWUlJQLeiIFyn0v7pqic/oiPqeulUtW3q6VV65cWXybw6ysLHbv3s1FF110wWccOXJkcffJRT+e1za19EV8YtasWSQnJ5Oenk5sbCwPPfQQN998M7fffjunT58uvqvT0KFDefrpp0ssp2/fvkGv2lmyZAlz5szhiSeeoG3btrz44oulxhPYtfKNN95Iq1atKvyZArtWfv7554u7VrbWMmnSpGrtWjkmJqbErpU3bdrE7bffTlhYGIWFhdxyyy0MHjyYvLy8cz7jL37xC2666Sbi4uJISEggKSmpSvFVRr3vWrm2r2EVqQnqWrnhUtfKIiI+oq6VRUR85IfWtbJa+iIiPqKkLyLiI0r6IiI+oqQvIuIjSvoiPmGMYfbs2cXv8/Pzadu2LVOmTAFgxYoVxb1DLliwoPgHzPJ0/3vLLbewbdu2Goq8egR2r+xnunpHxCeaNm1KWloap06dIjIykg8++IBOnToVj586dSpTp06tVNnPPfdcdYXZ4OXn5xf3WlofqaUv4iMTJ07knXfeAVw3xLNmzSoeV9ZNTAoLC7nhhhu4//77LxgXeDQQFRXFfffdR3x8PEOHDg3awdqCBQuYPXs2Y8eOpUePHjz77LOA65fnrrvuol+/fvTv35/ly5cDkJycXHxEAq5Hz6VLlwKuBf/ggw8yYMAA+vfvz44dOwDIyMhg/PjxJCYmcuuttxb3o5OTk8PkyZOJj4+nX79+xcs4//PceeedDB8+nH79+rFhw4bieefMmcPgwYNJTEzkrbfeKl53M2bM4KqrrmL8+PEXlPfyyy8TFxdHfHx88dFWsK6oCwoK6NatG9Zajh8/TkhICCkpKQCMGDGCXbt2lfj9lFf93R2J/EA9tuExdhzdUa1l9m7dm7uTSu8/HlzXyA8//DBTpkwhNTWVOXPmsHr16jLny8/P57rrrqNfv37cd999pU6bk5PD0KFDWbhwIb/+9a959tlng+4oUlNTWb9+PTk5OSQmJjJ58mTWrVvHF198wZYtW0hPT2fw4MGMHDmyzPiio6PZvHkzf/7zn1m8eDHPPfccDz30EJdffjnz58/nnXfe4ZlnngFcX0MxMTHFO7/MzMwSP8cnn3xCSkoKc+bMIS0tjYULFzJ27FheeOEFjh8/TlJSEldeeSXgehlNTU294J4FW7duZeHChaxdu5bo6GiOHj0KlNwVdc+ePdm2bRvffPMNAwcOZPXq1QwZMoT9+/fTvXv3MtdFWdTSF/GRuLg49u7dy6uvvlp8S7/yuPXWW8uV8MH1xFnUKh84cCB79+4NOt20adOIjIwkOjqaMWPGsGHDBtasWcOsWbMIDQ2lffv2jBo1io0bN5a5zB/96EcXLC8lJYXrr78egMmTJxf37dO/f38+/PBD7r77blavXh20Lx2g+Cho5MiRnDhxguPHj/Of//yHRYsWkZCQwOjRo8nNzS2+Wcy4ceOC3qRm5cqV/OQnPyE6OhqgeJp169Zx7bXXAq4r6jVr1gCuRZ+SkkJKSgr33nsva9asYePGjQwePLjM9VAeaumL1LLytMhr0tSpU5k3bx7JyclkZGSUa57hw4fz8ccfM3fuXCIiIkqdNjw8vLj74NK6Rz6/i2FjTIldGRd1ZFYkNzf3nPFF3SKfv7xg3Rj37NmTTZs28e6773Lvvfcyfvx45s+fX+743nzzTXr16nXOuE8//ZSmTZsGjd1aW67ulIumGTFiBE8//TQHDhzg4Ycf5oknniA5OblcRzzloZa+iM/MmTOH+fPn079//3LPc/PNNzNp0iRmzJhRYhKvqLfeeovc3FwyMjJITk4uPpWzfPlyCgoKOHLkCCkpKSQlJdGlSxe2bdvG6dOnyczM5KOPPiqz/MBujN977z2OHTsGuDtsNWnShOuvv5558+YV34TlfEXn+tesWUOLFi1o0aIFEyZM4I9//GPxzunzzz8vM44rrriC119/vXgHW3R6p6SuqIcMGcInn3xCSEgIERERJCQk8Ne//pURI0aUuazyUEtfxGdiY2O54447Kjzfr371KzIzM5k9ezbLli0jJKRqbcakpCQmT57Mt99+ywMPPEBMTAzTp09n3bp1xMfHY4zh8ccfp0OHDgD89Kc/JS4ujh49epCYmFhm+Q8++CCzZs1iwIABjBo1iosuugiAL7/8krvuuouQkBDCw8P5y1/+EnT+Vq1aMXz4cE6cOMELL7wAwAMPPMCdd95JXFwc1louvvjic24WH0zfvn257777GDVqFKGhoSQmJrJ06dISu6Ju3LgxnTt3ZujQoYBr+Rfd+7c6qGtlkVqgrpXPtWDBAqKiopg3b15dhxLU6NGjWbx4MYMGBe2duF5R18oiIlKiKp/eMcaEAp8B31lrpxhjugKvAa2BzcBsa+0ZY0xj4GVgIJABXGOt3VvV5YtIw7NgwYK6DqFUycnJdR1CjamOlv4dwPaA948B/2Ot7QEcA272ht8MHLPWdgf+x5tOxDfq86lUaZgqs01VKekbY2KBycBz3nsDjAXe8CZ5CSi6zcw07z3e+CtMXd8WXqSWREREkJGRocQv1cZaS0ZGRpmX0J6vqqd3/gD8GmjmvW8DHLfWFl3TtR8o6tyjE7DPCzbfGJPpTX9OD0jGmJ8BPwOKf20XaehiY2PZv38/R44cqetQ5AckIiKC2NjYCs1T6aRvjJkCHLbWbjLGjC4aHGRSW45xZwdY+wzwDLirdyobn0h9Eh4eTteuXes6DJEqtfQvA6YaYyYBEUBzXMu/pTEmzGvtxwIHvOn3A52B/caYMKAFcLQKyxcRkQqq9Dl9a+291tpYa+3FwExgpbX2OuBj4CfeZDcAb3mvV3jv8cavtDrBKSJSq2riOv27gV8ZY3bhztk/7w1/HmjjDf8VcE8NLFtEREpRLd0wWGuTgWTv9R4gKcg0ucCM6lieiIhUjv6RKyLiI0r6IiI+oqQvIuIjSvoiIj6ipC8i4iNK+iIiPqKkLyLiI0r6IiI+oqQvIuIjSvoiIj6ipC8i4iNK+iIiPqKkLyLiI0r6IiI+oqQvIuIjSvoiIj6ipC8i4iNK+iIiPqKkLyLiI0r6IiI+oqQvIuIjSvoiIj6ipC8i4iNK+iIiPqKkLyLiI0r6IiI+oqQvIuIjSvoiIj6ipC8i4iNK+iIiPqKkLyLiI0r6IiI+oqQvIuIjSvoiIj5S6aRvjOlsjPnYGLPdGLPVGHOHN7y1MeYDY8zX3nMrb7gxxiwxxuwyxqQaYwZU14cQEZHyqUpLPx+Ya629FBgK3GaM6QPcA3xkre0BfOS9B5gI9PAePwP+UoVli4hIJVQ66VtrD1prN3uvs4DtQCdgGvCSN9lLwNXe62nAy9ZZD7Q0xnSsdOQiIlJh1XJO3xhzMZAIfAq0t9YeBLdjANp5k3UC9gXMtt8bdn5ZPzPGfGaM+ezIkSPVEZ6IiHiqnPSNMVHAm8Cd1toTpU0aZJi9YIC1z1hrB1lrB7Vt27aq4YmISIAqJX1jTDgu4S+z1v7dG3yo6LSN93zYG74f6BwweyxwoCrLFxGRiqnK1TsGeB7Ybq39fcCoFcAN3usbgLcChv+XdxXPUCCz6DSQiIjUjrAqzHsZMBv40hjzhTfsN8Ai4HVjzM3At8AMb9y7wCRgF3ASuKkKyxYRkUqodNK31q4h+Hl6gCuCTG+B2yq7PBERqTr9I1dExEeU9EVEfERJX0TER5T0RUR8RElfRMRHlPRFRHxESV9ExEeU9EVEfERJX0TER5T0RUR8RElfRMRHlPRFRHxESV9ExEeU9EVEfERJX0TER5T0RUR8RElfRMRHlPRFRHxESV9ExEeqcmN0EfGT01mQlwuNmkB4EzAl3SJb6jMlfamagnzY9SGk74QTB6Bxc2jWAbpfAa0uruvopKpOHoXVT8LXH0D6V4B1w9t0h7hroP8MaN21TkOUilHSl8opLIStf4ePfwdHd7thjZpBXg7YQvf+omEQ91PoczU0aV13sUrlbH4F/nO/a+F3vwL6/wQiW8HpE7D7Y/h4oXt0HgLDfwmXTqnriGvfsb3w1X9g/0Y4vB1yjgAWmrZ166XTQGjXG6J7QeOouo4WAGOtresYSjRo0CD72Wef1XUYcCbHfZm5J9xhbeNm0DQaQkIrX2ZhAexJhr2r4cDncOoYnDnpym7Sxm0oHeKhY5xrVVVlWQV5cDIDcjNdS7xJGwhrVLmy8s/AV+9D8qNweBu06wuj74GuIyGypftcx/8Ptv4Dtix3RwChjSDxehh5FzSPqUDc+XBwC3z7CWTshhPfubIiW0LrS6B9P2jfB5p3qtypBmtdQsv6HrK/h+zDbnhYBLTs7NZ7o6YVL/ecz5AHh9Jccsg6BLYAQsLd0VB0D2jdDcIaV20ZZ3Ig8zvIOggFZwDj1lGzDtCsY8W3HWsheRGsWgQXj4CJj7v1fL7j+yDtDfh8GWR8DfHXwsRFENGiYsvLOgRHtsPxbyHvlIu3aVuI7unib9wcQkr4+TEvF/ath5x0VzfDI913Fh7p3jeNLjme/NPuM2R/77aBrIOuDgYKDXd10oRAfi7knXTLPLYXvvvMPYPbBtv1geYdAQOZ+2DfRjiTdbaslhe577tJNEQ0d9tBqPcoeh0S5g1r5F43bubWQfMY99yoSblWqTFmk7V2UNBxSvpB5OXCt+tg90rXojn05YXTmFBXqZrHeF9ITMDrjm6jjWrnKl+gMydhxzuw6jFXUULCoEN/aNrObainsyDnMBzZ6VVgILwp9JroDqW7jig7EWUfcRXh/9a5hHkw1SWbQI2bu9Z3VHsXZ1R7F3NouPtsRRtf02iIaOkO7fdtgF0fwelMlxBH3wt9f1RyhbQWvk+Fz16Ez19xZfb9kdsBtO/rKmOwZH18H2x4Bja/DLnH3bDI1tAiFgrz3SmH7O/PTh/Rwu182l3qdmhFFb5RE+gQ59ZvSKhLKNmHXYXc+g/3OJlR+rps0dkln+ieLkm37QVterh1VfS5C/JcgyD3uGsFnzwKBzbD3rVuneXllFy+CXHJoE13V26094hq7z5X03bnrt+8Uy7RfLsO9qxyO8Vj35RcfliEK6/TILhkrEtMUW3d9x9s3Z88Cisfgc9egITrYeqSsncaBXmw6nFYvdgd7Q38L+gx3n1nbbpDeMS501sLB79w6//rD+Hw1tLLx7jk17i5ay2HR3pJ+IyrQ/m5pc8e2RpadHLJNryJOxLN3O92NIX5ZSw7WDghro53GuiOZntOgDaXXDhdYQEc3eOOAI7scM+Z+1wD8nSWa9QU5rn1V5hXvmWHRbj6aULcdlG0Y2vTHbqPg26joHknTEiIkn6x3R/Duv91CaFRlFtpoY1cIsvJcK3UjN1QcNrtfS8a6lo7zWNcJcw/7Sp31vfuHHbWAfd84gCcyT5vYQYuGQO9J7tWzP5NsH+DS+bt+riWb88JwZN4QZ5L/N+nwrfrYfsKdzQQEg4xCS4BNm3rKkBRy+LoN24jK0qUYRGusndOcht9REuXlHIyXLI7me6SYPZhyD4Ep46Wvu6axUD3sdBrsqvUoRU4O3hsL6x9ClL/drb1ExbpElGfqS6ZHd4O/7fWtYxNKFx6FfSZBhdf7nZMgU4dd9Mf3gqHtsKhba4S556g+LxzkcbNXaIJbHWFRUCvSRCT6LWIO7hEi3FJ+theSP/a7ezSv3Kv8wJagSbElVtw5tzhgdr1hS7Docswd3jfrIPbkRbkue8rfZdLWulfu+eM3ReW1SjK7bQK8tw2lHP47LjmnSB2sNuBtuoKzdq7dWoL3TZw4jtX5uHtsO/Tc7fP0MZunV4yFkbMdeVvehE2LXXTDbsdxj1S8g49mANfuO9421tnGxlNoiHpZ+73nUNp3neV5ra3kDDocpmLodMAt/MrWqdZB936yT7kttncE+5I9Uz22XUU2siV220MtOritqG8k97jlGtgZX/v6kTW9+5oIP80GFzd6ZjgdubNO0KUtw00bua2AQCsi+V0tvs84ZFu/YaGV/+P2Na6nUTBGW9H4O0Qck94Oeage87NdNPZQvc4k+0aeQe3nG0IRbTA3LtPSb/YzvdcK/vMSXdYnJfjNviilm/LLtCmG3Qd5SpsRQ7vc0+c3RHkZLi9+5bX4MR+l6zb93UJ7JKxbkOtSIXKP+NOBe1d7Q4bsw66pJ2X6zbIFrGu4rfu5h6xg93OoSKnDgryXcvHFrjngjzXKjmZ4VoSzTqUv6ySnMlxPwpm7nOJdcc77rOAq1Cdk9yporifuiRQUda6ip130lWQ/RvdTjM0/OzRV1R7tzOvyGmIwkKXRNO/goxdbr2cOu7Wb0SLs4/Gzb0jj0sr/jtGYaHbdtK/duv81DFvx/+lW06rLm6dtOwCMQNc67K8yacgD77b5NZ59mG388jc79Z/YYF32inM/f4ycp6Lv7JOHHQ7sezD8OXf3OlAcEm6bS93Wq7LcOg9Rb/1VJeio+p9G+DwNsxVf1DSrzOFBa4F3rJz1c/d/hAVFrik1qQ1NI+t2I5Qqi7zO9j4rDsKjJ/ljhaq29Fv3CmYNt3dzldqnM7pi4j4SGlJX80qEREfUdIXEfERJX0RER9R0hcR8RElfRERH1HSFxHxESV9EREfUdIXEfGRev3nLGNMJvB1DS4iGkivwfJbAJk1WH5Nxw8N/zMo/tIp/tI11Pi7WGvbBh1jra23D+CZGi7/M8X/w/4Mil/x+zn+YI/6fnrn7boOoIoaevzQ8D+D4q9bir+eqddJ31rboFd4Q48fGv5nUPx1S/HXP/U66deCZ+o6gCpq6PFDw/8Mir9uKf4Kqtc/5IqISPXye0tfRMRXfnBJ3xjzgjHmsDEmLWBYvDFmnTHmS2PM28aY5gHj4rxxW73xEd7w940xW7zhTxtjqnBn8jqJP9kYs9MY84X3aBdsefUxfmNMs4C4vzDGpBtj/tBQ4veGX2OMSfWGP14bsVc0fmPMdeet50JjTII3bqExZp8x5vx7gDaU+Ot9/S0j/pqrv7V9uVBNP4CRwAAgLWDYRmCU93oO8Ij3OgxIBeK9922AUO91c+/ZAG8CMxtY/MnAoIa6/s8rcxMwsqHE7z1/C7T1hr8EXFHf4j9vvv7AnoD3Q4GOQHZ93X7KiL/e198y4q+x+vuDa+lba1OA8+/w3QtI8V5/APzYez0eSLXWbvHmzbDW3dHZWnvCmyYMaMQFd9uuGdUVf12p7viNMT2AdsDqGgs6QDXF3w34ylp7xJvuw4B5alQF4w80C3g1oJz11tqDNRJkKaox/oZQfwOdE39N+sEl/RKkAVO91zOAzt7rnoA1xvzbGLPZGPPrwJmMMf8GDgNZwBu1FWwQlYofeNE7NHzAmPLeQbtGVDZ+cJVhufWaP3WkovHvAnobYy42xoQBVwfMUxdKij/QNdRS0qmESsXfAOpvoGDrv0bqr1+S/hzgNmPMJqAZcMYbHgZcDlznPU83xlxRNJO1dgLuELcxMLZWIz5XZeK/zlrbHxjhPWbXbsjnqNT698yk7pNRheK31h4DfgEsxx2h7AXyazvoACXFD4AxZghw0lqbFmzmeqBS8TeA+guUGH+N1V9fJH1r7Q5r7Xhr7UBcAtntjdoPrLLWpltrTwLv4s7HBc6bC6wAptVmzOfFUOH4rbXfec9ZwP8Dkmo/cqey698YEw+EWWs31XrQASq5/t+21g6x1g4DdlKzfUiVqpT4i9SHHWuJqhJ/Pa+/RS6Ivybrry+SftEv38aYEOB+4Glv1L+BOGNME+8wfBSwzRgTZYzp6M0TBkwCdtR+5E4l4g8zxkR784QDU3CHmHWiovEHzFpr5zlLU5n4A+ZpBfw38Fxtx12klPiLhs0AXqub6MpW0fgbUP2tAaiEAAACMUlEQVQtKf6arb81/Wt2bT9wSeIgkIdrid0M3AF85T0W4f0pzZv+emCrt1If94a1x/3inuqN+yOuxdlQ4m+Ku+KlKP6nCHJVTH2NP2DcHqB3Q9t+AsrZ5j1q5cqRSsY/GlgfpJzHvfkLvecFDSX+BlZ/g8Vfo/VX/8gVEfERX5zeERERR0lfRMRHlPRFRHxESV9ExEeU9EVEfERJX3zNGGONMa8EvA8zxhwxxvyrkuW1NMb8d8D70ZUtS6QmKOmL3+UA/Ywxkd77ccB3VSivJe7PWCL1kpK+CLwHTPZen/MvYGNMa2PMP43rG3+9MSbOG77A6zs92RizxxjzS2+WRcAlXkdZT3jDoowxbxhjdhhjltVx53fic0r6Iu4v8DONuwFKHPBpwLiHgM+ttXHAb4CXA8b1Bibg+kV50PvL/D3AbmttgrX2Lm+6ROBOoA+u2+XLavLDiJRGSV98z1qbClyMa+W/e97oy4FXvOlWAm2MMS28ce9Ya09ba9NxXfi2L2ERG6y1+621hcAX3rJE6kRYXQcgUk+sABbj+kJpEzA82KmYor5LTgcMK6Dk+lTe6URqnFr6Is4LwMPW2i/PG56C6y8fY8xoIN2evStTMFm4PtNF6iW1OEQAa+1+XG+G51uAu4NRKnASuKGMcjKMMWuNuzH2e8A71R2rSFWol00RER/R6R0RER9R0hcR8RElfRERH1HSFxHxESV9EREfUdIXEfERJX0RER9R0hcR8ZH/D2ADyeJQ/ndsAAAAAElFTkSuQmCC\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"timeseries.rolling(12).mean().plot(label='12 Month Rolling Mean')\n",
"timeseries.rolling(12).std().plot(label='12 Month Rolling Std')\n",
"timeseries.plot()\n",
"plt.legend()"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.legend.Legend at 0x1c1ef1c0d0>"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
},
{
"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": [
"timeseries.rolling(12).mean().plot(label='12 Month Rolling Mean')\n",
"timeseries.plot()\n",
"plt.legend()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Décomposition\n",
"\n",
"La décomposition ETS nous permet de voir les différentes parties !"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<Figure size 432x288 with 0 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 1080x576 with 4 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"from statsmodels.tsa.seasonal import seasonal_decompose\n",
"decomposition = seasonal_decompose(df['Milk in pounds per cow'], freq=12) \n",
"fig = plt.figure() \n",
"fig = decomposition.plot() \n",
"fig.set_size_inches(15, 8)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Test pour la Stationnarité\n",
"\n",
"Nous pouvons utiliser le test de [Dickey-Fuller](https://en.wikipedia.org/wiki/Augmented_Dickey%E2%80%93Fuller_test) augmenté ([unit root test](https://en.wikipedia.org/wiki/Unit_root_test)).\n",
"\n",
"En statistique et en économétrie, un test de Dickey-Fuller augmenté (ADF) permet de tester l'hypothèse nulle de la présence d'une racine unitaire dans un échantillon de série temporelle. L'hypothèse de rechange est différente selon la version du test utilisée, mais il s'agit habituellement de la stationnarité ou de la tendance-stationnarité.\n",
"\n",
"Fondamentalement, nous essayons d'accepter l'hypothèse nulle **H0** (que la série temporelle a une racine unitaire, indiquant qu'elle est non stationnaire) ou de rejeter **H0** et d'opter pour l'hypothèse alternative (que la série temporelle n'a pas de racine unitaire et est stationnaire).\n",
"\n",
"Nous finissons par décider cela en fonction du rendement de la valeur p.\n",
"\n",
"* Une petite valeur p (typiquement ≤ 0,05) indique une forte évidence contre l'hypothèse nulle, donc vous rejetez l'hypothèse nulle.\n",
"\n",
"* Une valeur p élevée (> 0,05) indique une faible preuve contre l'hypothèse nulle, donc vous ne pouvez pas rejeter l'hypothèse nulle.\n",
"\n",
"Faisons le test du Dickey-Fuller Augmenté sur nos données :"
]
},
{
"cell_type": "code",
"execution_count": 17,
"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>Milk in pounds per cow</th>\n",
" </tr>\n",
" <tr>\n",
" <th>Month</th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>1962-01-01</th>\n",
" <td>589.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1962-02-01</th>\n",
" <td>561.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1962-03-01</th>\n",
" <td>640.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1962-04-01</th>\n",
" <td>656.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1962-05-01</th>\n",
" <td>727.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Milk in pounds per cow\n",
"Month \n",
"1962-01-01 589.0\n",
"1962-02-01 561.0\n",
"1962-03-01 640.0\n",
"1962-04-01 656.0\n",
"1962-05-01 727.0"
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.head()"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [],
"source": [
"from statsmodels.tsa.stattools import adfuller"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [],
"source": [
"result = adfuller(df['Milk in pounds per cow'])"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Augmented Dickey-Fuller test (DAF):\n",
"ADF Test Statistic : -1.3038115874221294\n",
"p-value : 0.6274267086030316\n",
"#Lags Used : 13\n",
"Number of Observations Used : 154\n",
"Faible preuve contre l'hypothèse nulle, la série temporelle a une racine unitaire, ce qui indique qu'elle est non stationnaire \n"
]
}
],
"source": [
"print('Augmented Dickey-Fuller test (DAF):')\n",
"labels = ['ADF Test Statistic','p-value','#Lags Used','Number of Observations Used']\n",
"\n",
"for value,label in zip(result,labels):\n",
" print(label+' : '+str(value) )\n",
" \n",
"if result[1] <= 0.05:\n",
" print(\"Preuves solides contre l'hypothèse nulle, rejette l'hypothèse nulle. Les données n'ont pas de racine unitaire et sont stationnaires\")\n",
"else:\n",
" print(\"Faible preuve contre l'hypothèse nulle, la série temporelle a une racine unitaire, ce qui indique qu'elle est non stationnaire \")"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [],
"source": [
"# Mémoriser dans une fonction pour une utilisation ultérieure !\n",
"def adf_check(time_series):\n",
" \"\"\"\n",
" Passe une série temporelle, retourne le rapport ADF\n",
" \"\"\"\n",
" result = adfuller(time_series)\n",
" print('Augmented Dickey-Fuller test (DAF):')\n",
" labels = ['ADF Test Statistic','p-value','#Lags Used','Number of Observations Used']\n",
"\n",
" for value,label in zip(result,labels):\n",
" print(label+' : '+str(value) )\n",
" \n",
" if result[1] <= 0.05:\n",
" print(\"Preuves solides contre l'hypothèse nulle, rejeter l'hypothèse nulle. Les données n'ont pas de racine unitaire et sont stationnaires\")\n",
" else:\n",
" print(\"Faible preuve contre l'hypothèse nulle, la série temporelle a une racine unitaire, ce qui indique qu'elle est non stationnaire \")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"___________\n",
"\n",
"## Remarque Importante!\n",
"\n",
"**Nous avons maintenant réalisé que nos données sont saisonnières (c'est aussi assez évident d'après le tracé lui-même). Cela signifie que nous devons utiliser ARIMA saisonnier sur notre modèle. Si nos données n'étaient pas saisonnières, cela signifie que nous pourrions utiliser seulement ARIMA sur ce modèle. Nous en tiendrons compte lorsque nous différencierons nos données ! En général, les données financières sur les actions ne sont pas saisonnières, mais c'est en quelque sorte le but de cette section, de vous montrer des méthodes courantes, qui ne fonctionneront pas bien sur les données financières sur les actions !**\n",
"\n",
"_____"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Différenciation\n",
"\n",
"La première différence d'une série temporelle est la série de changements d'une période à l'autre. On peut le faire facilement avec pandas. Vous pouvez continuer à prendre la deuxième différence, la troisième différence, et ainsi de suite jusqu'à ce que vos données soient stationnaires."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Première différence**"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [],
"source": [
"df['Milk First Difference'] = df['Milk in pounds per cow'] - df['Milk in pounds per cow'].shift(1)"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Augmented Dickey-Fuller test (DAF):\n",
"ADF Test Statistic : -3.0549955586530704\n",
"p-value : 0.030068004001785647\n",
"#Lags Used : 14\n",
"Number of Observations Used : 152\n",
"Preuves solides contre l'hypothèse nulle, rejeter l'hypothèse nulle. Les données n'ont pas de racine unitaire et sont stationnaires\n"
]
}
],
"source": [
"adf_check(df['Milk First Difference'].dropna())"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x1c1ef40850>"
]
},
"execution_count": 24,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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DjTliQWQWJ4M9b3T9Q1/o+b/hfffjX/3BnYOMz+3oOvkc0tZ0zcBOcpHn/Afj5O1xZwPP/1D7mTuuiHSp014kKdJUceY/+DsfwZ2PXB9k/EonP7ATAMya/Ld/fBf+8pOXBxmfgkjgGSczjZLBny/ArQR35LtB8jqIBB7L76SDfRe6/iPf02N+5KFruOPBYdQ73I6uk8+pI+YDO+G8BNEUWw3khHPSTL2dH2j+rhusJalE4KmDHlzQBc7bJrAxF0mKq3sLvO3jF/H3nx3mIco3EJvqxP3wTpLW5Js+9Cj+6tNXBhpf/Ut0R5pKzKJ08OcLMPf39z/0MN7zyScGGZ/W/yjw3LQCZkieN0sc7PkluontRGZx6gTwHFknX+Tkh0XC+X7mpMIYasFEObrA5BQGcgK5fu9RkuL/+esHBnP6iZQYB2p5uO6d4oJS4U5mHhvOfCj1VD6nYDj54cePkhRxkmKRDOeEafxJ6CNKUn0PhqMTWU5H0xEP4Pc+NIz6iJ7fceC52SmwIBKzXflQ1yflOwU9fuIkYB1ZJ19dDDUs3ZGna4ZClQt24wCO5Aeef5JCSomPPHQNP/W2ewdDqmkqMQn97LNcF+O4dvIJ9qMYwHBtY/N0zdA5nSRVR+Z5Qo05Gxok5JzkdGC6ic8zYuKG4egO9e849HTg/vTlHdw/kHot1TsdH3HWKXUeJ4MHcb4TmcfDBXFuR9bJ51vpDl3xmm+bMPRDWtT5D0vX5Cvm9rMgQonqg1rMnPzCAVJKpASd7ey6t8k8SvX1cUOnpKzOYrjxPSEQZpyzq8T9KPAQpczJu0gcM8Az9PXnnPaPv+Uu/NSffWKY8TmSTxWCT+WQ91f9O/JNkJpHyTOLrsl3ERyaTqHFnGRJuaGdcJRD8sT5Dz1/+iztZAbcTo5DtTxcHfQ8CVQQcdK7ho3J6ZohE6N0OHWUSFZnMdz4vkdOfvj1KbMgS3TE0BXfZV00Z1E6OBIeB4bT3p7GOpgfeHy+00klo4uH5eRHjG5SO51nFJK3i6FmA3OGebpgaAlcfgcyXTjkPGPphC5w6YQVHaSWn5OdQp6uGZguS6S5PovY0DWD3V8p4QuB0Bdu6KBs/MCzdwpDJxYBdX2klJjFw6t3xqHhtKdRMrjEmugseo5TafuOvpaWqHdmUepE5HBknXz+IG+D5IfP/s/jdPi2CTl1jUHyDh6iJB08p5CkBslHicSTu3O86j++G/c8vtXwzvbjazrIQeKVO9tFnGJ/EWevD4QkUwnfZ054YCeZphKeJxD4ii4wdM1Q9xfwPJq/HHwnyJ8jojukHC7xnXAJom47MCBnzumg1IBAYJjvUJbYHZLz53ZknbxR12TFUAMnXvNIwyzyYekazck74lTps6YDzz+VNpJ/+Oo+Lm3PcP/mMP3xUymxErrbKcQVdM2QdJnPOXMXSNsTGPkeFlyiOeD99QULIgPTQXkJ6NB1IppOydRBQMb5O0jsciQPDPMdDF3jW0h+qJ0CtyPr5E0rYMXj0YUYkjP0WOLPXathG8kPpl5wTDdxJB8nEnvzLLE7lESTIXnXxVbzmCVeB8xZeCWc+ZBI2/cEAl/knPBw99dnSH7otgw0z9AXTkAUT1xSz53pgBJEK7GbSqsx2RDXKF8MRXQWMHxXzaPr5Bknz8uiB8v+pxKro0B/BnH+w+nk8+qaYXci+S6OLiouNeecpNgbmO6IU4lR4EEINaaUEr/63vtxZXc+0PgMyUfJ4BJBKhYLfc/SyQ95qA3fKcwGoGv+6COP4u7HtvQ8PYHCTmTInQKgdPhxmmonOZiTZ3RHlEjdCXToOggloUy1AwaGadOgi9FCFaSIzgKGrxAezMkLIR7MDu7+mBDijuy1M0KIdwohPpP9e7rteLwYamZF0eGcDCHJvUU8eC+YvLrGlU4esItxhkpiJtJG8sRpD4XkU2nUI4tE4qEn9/Gzf/FJvOsTw5TtJwUkr+Y/ZIMpTwiMfIEoThkKG2583xMIvAxpH5AOSlOJf/NHd+G3P6iKkWgnQl0cZ9HAdFZinHwUy8F3mlJSkBJWzsJJYjeP5Ifg5Jl6J0rtIHJknXxmr5ZSvlxKeXv2/z8K4N1SyhcAeHf2/62MFyvxgzeG3K6ujpST357GZnxXOvmBJZRJKjHyTWJ0aDooTYHA8+B7KrFIdM2QiV1fEOecYmc27E6hmHgdev6pClIBqVOGlVAqJ1yCtHuunyd25qrYJjE7VuLkIytxP9xORAjmxAYWNhDdFHhuirnyid35wE6Yq2ukNCAQOH50zbcA+M3s598E8K1t38id5NB8GKAWCSX++Gk+Q13gfMXrENttbjGTIFpOZlDOViGlKE2xN1dOeKgWBGmGVEmdspPdg8F2IhkdBLhJvCYS5Tr2IZ1YJqGMLc683/V/6Mk9PS5gkDw5saETr3Zi2jjJIekaj65PevAgmDeiTqgi1VLXDEHXsCACALtzAzSHbm0wpJOXAN4hhPiwEOJ12Ws3SikvAkD27/m2g9Fi4wsQGFaCuJIheUKRwHBl7wV1zcCcP287ELlIbBGd4nmIYom9oZEwqUcyJLwzH5YOipIU62OVc+E6+SHrLHzOyQ98slgijYTS7nLZb/4PZ6eSkRM0SD4nAR0YaasgNbzEl66/7w0TBPPGi5UA6J0sMAyQSgvjGx80tNosGHCsL5VSPi6EOA/gnUKIT7Z5UxYQXgcAt956q349SVVFnpS2Ex4qUsdpqpH81jRirw+vk5dSDl6WHrMgtUiGd/JpmiGlrKx7f2AnHKdGnbJIhqdriI67umera4aUCPrEySdmtzmkRJDorP1FfOAgQk6eQJLh/D2Lkx9ypxl4LHE8+E5H5UQCvROJBx4/54QXwzph0ztIPcO7s+EpY7LBkLyU8vHs3ycAvAXAKwFcFkJcAIDs30KfUSnlG6WUt0spbz937px+PU4lVkNC2sYJD4GEpVR9KAwnz+iagROvqXTDmXO6aYjEXN5izXlmnPzASF45MeoHLvU9HpKTp/urKlKHdQI8SEVMfTGkMMBIKCXj/PvN/6EnMySfcLoGxYragSWaQZZYH1oemFLiNdNBbxNIGHB8IQydsjOwE6bLTEGE0zVDNykbxMkLIdaEEBv0M4B/BOBuAG8F8N3Zn303gD9pM16aKjnRarbdtjjzAaPoZFTk5Ic+GQpQQUrLowbVsZtiosF716R275Sh1TW8N8siTjSSGeoUoSRNMQ58BJ7I0TVDqoOyxGjMdmoD0nHm+qQHTiwaJG/TNSEhYQd0TeB7GOXomsETr8Rpz4ZH8qrtgwoi+4thnTCveAXyO4VhkfxQdM2NAN4iVFvBAMDvSCn/QgjxIQBvEkJ8L4CHAbymzWAUjVfLOPMBHiJytGanEBd+d+DPYDfqukUHDYf0VnjideBDVfK9U4gzHCoxqiWUgZIIEicfxcMh7cAXGAee6kI5d4FUPYSBh+k0Gr5OQTJOmyX++gYRzcmTukZz/koC6iTx6pneOFxCqZqjiQONT+uHnDA9w8ONr+gg3y9y8kM4Yd7qGQB2Oeffcfzf+sCDtb8fxMlLKR8A8AUlrz8J4Ku7jkcXQKtfLDpluAu8UkLXDNmFchQoFHZ9f1i6SY1jcgpuKgoNknRV8Upl+5EDTj5OFCc8CjzFyUfD6uRVWwBoTt5Fg7iyVsN9gsjOLMLVvYUeFzCcf+jbEsdUmr45B5t/VswVeJhOE0uVFSUSo+DgTt7LEsdAbjeemg6hBxrfM3SQRacM4YNySH7Xyjt2u8cffbj+SMsjWfFKD+JaRtfYSH44+VI+8SrEsHTNRjb/relCvz5k75ryxOtwTszTnOrwDb64E1M6+UxCOVhiN0XgeRgHPuZxMrj6Ik44nXWwk6c+/uh1XN9fWK8ZpJrXgXe/v8TH8/cbnbldDNX3O+SNkHyY5XT4+EMAqTxdM3TejtcRADn1y6DFUH5h/K73uIniPJJOnmSMOjGaOXl+QMAQ46/kxl8fBYMiyfWJcvJukLwtoXRRts/VI5R47eOEH35yH1La3zsvoSSkNKi6wxcYh55VDDVkAzGP6cAPIpH9p7/2Qfz6+z9rj0/XPxCWRLNPzuKRjKo5szbSDjZl81eHzgycWMyuP+0E59HQdIeiUzTSng2vfvEYHTQ0kq9NvHacf9PfH0knn+fkaSu2OvYH2W7HOTqIUMD6JBiwd43RaXMnP6QT4+qaIU4mIgdO6iPd6jaRjJPvNv8ntmf4qp9/D96Ra1fAE39cQjlU4pXomnHgYRYNr2PnTmwWJVjEadaHRxYCWp1JqfIR+Z49dkXnwRrQPZQ5+efesGYdlkOcPzC8ekQj+YDUR8O2JiE6xfcOPv8klYU56Z1Udn32DuCEqz4TKC+G6voMNwXNI+nk6QJQAzG6gWujYLCtnhrf5uTXxsEg6ggpJeJUGic/HR7Jp5Ifz8eKTXrSHQ9e2cPn//u34+OPXtdzDDInsGCJ165B6tp+hFQCn71ityhOmZOJ4uEllHGm7hgHPram0eDNn+KMbhoFQq/P9Wy9drnH/FQjboaOsIt9qONiF3voyX2cXg1xenVk6BoWxIF8Lcowz1iQ0TWLAl0zHJ0SZvM/SNX6v/6DO/HDv/8x6zXN+Xvk5IdtUEYSzXCAINL0zBxJJx/n6BRyAGtjf/CkhxDQyo71cTBIlKbIujEJAQBb+8Nz8rytwRDqoL//7FUskhSPXZvq68M5Z6I7urY1oN3BE9s5pKrpGtUPfFcnXodSN6U68eoqyNL1IRS2kdFzXe4BPaDcSQHGCY9yFbXqPd2+w8NX93Dr2TUEnsglXuGMjohJfeR7Bc5/iLwLVwcBhnIFus//kWv7uOcx+zAcXWzlGaRNSpih+sn7QuidiK2T78jJN1zPI+nkaSGuMaQd+gLjwB8mqZKQE/Mw8lWDoMATmITDcP704NJDT06GTpnpYps7c7z21z9obeepjiD0PQSeGKSO4O7sxKcolfqkeuJs9+fmWLWu49PB4k/szKzXifOknYKmawZU7ygk7+mkJjVbG2p8jiQBE9S7fAZJRrnCC2BOOCehBLonLh++uo/nnFlVckld8WpyIkDOiQ2WGIVum2AdujHQbtlnTvggdM0ikbi4NbN2SCmbP6B07CaId7s+v/v3D+MX3/lp6zUKUmFJYrfr/I8nktcSR0PXjANfPaSDcPLqogRskU9CP0tCDaAsyG5SnpPvw/l/4uI2/vozV/Dhh67p1zTSzpyMJQHtGaSoz3icpAzJq2vE2z50dcL0cD+xU8I5Z2X78zjF7mJYTj5KU83JX8uc/IlJ4ODQDfMIUaK9yz1YaCRfTteE2aEYe/OEHXLT7TtsT2OcWRtZSD7RdEQxSA2N5Avqmo73+A8//Cj+4u5L1mvFnEJ/uiaKVRC6xgUSuSCyN4+12q/r9X/XJy7jzR951HpNStQg+a50zTHk5OlLrrHE6zjwskU6HCfvZ04AACahGn8IJ0AP7noOya+Pu6t3SJVwacsgYT3/7IzRnQMqC5JU4hMXt/X7aXzqXXPdkoD2o2s2y5x85sS29g1n3mf+//UDDxYTlzrx6ut8xYmVcPA6ghHTYxPS6+JkaD5beSTP1C+AOqSaQEMfzjb0BXzP7FQpJxKw+Z/oiVTLLCVOXp8ha+vku9hvvP+zhYIfolOGSLzS83pxa2rNn9NBUSJ7X/9FkmJzZ27tFPJB6iDFVscTyecklLMoVU4+WzAHNUKqAUNik9CH73mDjE8XnRbFNnPyXZE8IeGLzMlTIKKdiJ106j7/+zd3rX7iPAiGntC/CzzRGcnT31/etumaVBrOn6P3rk54c2eOf/cn9+CPP/qY9XqUqV9opwYAJybhsBJKr5yu6fIZmpOfRgUnEPjGCVjjd1ZfpAh9T7flpflzJ6PG74dUP/7odS3TJIup334Zku+MVFNdzEXGcyJATp3SM3F58bpZo/nEK2Dqdvpc/3lsOq0CpgEj5/zpXvQJInV2JJ18kqNrAOWEA88bpq1Btoi9Al0jBkm80viak8/ogvVJ0PkBIid/iaEMC2n7nkYxqo6g+/zvZkmnmCH5IOfETq2OOs+fFuD+Iin0zOacMFnXtgbkPMp2CqoYijn5leEksrpiNOBOvjtdQ9ez2F8YZycAACAASURBVFK7jE4hJ9z+HkupjpYLfU+35aX5exU5ha6B8F/8/sfw02+713qNH49IOQWh6aaOOvBUWjJkGp/r2Pl97eyECUgxIELnBQScjutJ19D15GuUghSna/T4PYJInR1JJ68rXjMkDyglTDCQE+ZObOQzusYfJoiQY6OHcmsaQQj1fbojefXgP15C19BDRFv9jUnQC6ne/di2XmxRInXzJC+3yE+thr3VNYDSzPPvQMfnkZ1cCTtz8vT3eScfJQpJ0hGGgELyQ/Z7zwfBg9A1gC2j1EjbClJ9dgrqb0PfOFw+fmkQ6XiN9hcJPn15x3pN6+T9EifWFSjEKa7uL+zEaNaFkq9P8hfdkbAa91KermHqIwAHossAe43mE/dJKs1Ooev4DcDoaDr57EuuMCc/Dv3BOHMuEdRIPvBVCfYAToBu6iRLFqdS/UwHQHQx6lN+yaJr1Gu+7ym6ZmoSu30457sf38KLbtpQc08NkvdzTvj0andO23LyfJGzLo5kZ9ZGvemgzRwnHydSK7LITkxCSDlQ2XtCJysxumPcB8kzJ58rzfdZRSfQjzOn8Q2SJ3UN9MlKZOs95k+f8dDVfeve5RPTu/O41/UB1PflVct6/BydYtRN/ZDwxRyQyucs1sZZ8WFXiWM2H0shl0lk/dIg8gxA8roVcOjrLZ5KvA7U1kAjYU8vwpWRb21nD2KxRk8eJgdM7Bq6xki86BknJEB/szEJOgepNJX4xOPbePktpwAoVKDpoAKS7+GEkwonX0J3nFkbDZzYLdI1QDe6IEkl/rtfeC/+9M7H7ddlUUKpH9LeSN44eUr8lUs0uweR0Pe0HJPGVxLBEnVQxzW0iBUwePBJU/BGFce8FbBWp/RUj1xj9Samt1Ixp9A1iNMa4px8mfqIWlf3bTtg0TWpOYg8P/+u1+dYcvK0EEPf0CnjwIPPEkcHGl9z8qZ3xFgj7W4L5P7NXXzbr/yNlXiiix4Gnu75Pgn9XnQQ0TULlnyyJKAWkuyeWHzo6j525zFe9uyT8IQaO81JNMlOr/agU0roGmrORHUKZvweTj77+7y6JkqVoiSfeAW6cbb7ixgPbO7h/Z+5Yr2epBgk8bpgW20LyZOEr8QJ9JFohoENMsoTr/04eXpm7ntiV79GnDOtz71F0ktiqsZX3+HaHg+CxfXZ10nS+JcYnWhyFub6qGe4O1ArpWukXQwFsMTuMwnJ+55nJ0aHomsYkucSytDvLtG89+I2PvrwdfyHP/uEfk2jJ09oJL+Szb8zXcOcJG0nLfVLbpF3DVJX99TCu+nkig5ysTW+WYSnVkdIUtkpUNH8PWEWOdfhcyR/dq1HYjcb/8m9hUZMVCzme4auEcI8RF3O8aXx79vctV5PiZMvS7z2RPJbVmVuWRBR43cJtHQ9R77QwgUpZWnita+Ekr4Dd/JxlvguS1x2R8JFJG9yCgcLUmlq1vvFranZLecSo4ACmqHXnXKle1BA8rn7uzbqy/kfQ06eLiIVswAZkh+KruGcPJNQ9qGDiDN/xycu468+vQmA0TUMyY9DRQd1RvJRFyffnTOn8SeBp4NQWjH+yZXuFZ3kJC+cXNF0DaeDCOkJoRK7fXcKUkLvdAjJhb4J4quZeor/vtX4zIHxxF+cJXb5/PskFisTr1kxl+2EyYl1cPIxo2syhxWnssJJdp8/d5LcyZcVi9H8u95jjeS5k9fqmrLEdIfrk62FG9bHmEXm7AdqK8HnPw493Uuoi2lxwG5RXcOv/zgkmWv78duAriPp5DXStuga39L5Hmx8E0Q0J59txbpu9ejsyrNrI/zEn96DKElLg9RKpt7pLqE0lY6U/becfA5JdkUBNP9x6KtDu62KV0MXrI58/V26PKSLrBDn/Imxbm3AE7t0/dfHAcbZIStdGnDxnQ49RFaxW0g5l0Cjym50ihp/axrhyi7nhGEhYUqsdx2fr4ftXI+dfOKvj07e5uSNkqOuLUMfOgjII/m0sBPsk1ikZn8ALK08HdpSRmd1SYzS9b/1zAoAA6SorQRH8nSPu/ogeiY5pZhIWBW1AAOaPe5vnR1JJ58v9gFUlOuDhBdxio89Yp+cQovMUtdQYrQnkv/BVz8fD2zu4VOXdgwP6uc4+R4Vu/M4xfmNCQJP6AVoXZ9skYe+wCT0O2f+qdBJfX8PEUMGnjBBdm0c6GvVJfkaxSlGvofzG2PdpCzfAA1QKI9+7rLIuZOh7TA9uFwiu5ol1tXvu+9EgDKkatRBKyPfVEf2TbwyTp6CyKiErunUG4eJADiSL6MLDpI4DjyBB67s6l1gktS0feg0vlkL13LnMniVOYvu9/fWM6sAgEvbBkipYkAbyYeesPIoXb5Dnq4RuSBlCj77rZ8qO5JOnnPyxKlOAl8f0NDF3n7PJXzrL/8NHmKZf470OOcfZDriPkjythvUItmbxzpQjAKurvH7bfXiFCsjHzeemBToGv6QToJ+xVyU2J0EvjogJE6NeodtJ9dGvnY4nZxkoo5BPL8x0VWvacn1Xx8HelfS1wlv5uigwBM6yK6ODF3TJYjwnQLn5RXd4Vk7QXIIvSWU03yxGErplD7jjwLjEGm3VpXY7UY3qb99/vl1zKIUj11XTjJOi3UEfegsHhCu7RXpGu6ETxyg4picvH7GsnNi+fWZBNlut6f65cruwgTBtMj56/5ZPXeCVXYknfyQSJ76xnzykinW4G0N8oldoJsEi5zkqdURAFUYYtE1mZNZ6anzn8cJxoGHCycnurdGWUXqZKS2eqnsNn9C8uOMTiK+FrATo6ujQH9WFyS/iMnJj7E9izGLEjvIZmNuTMz4XapeFyV0DQU66kIJZEjbI7qi/fy5k7+/BMnTWaWTjK/ln9/GeAuMvLrGK9BxRNd0H7/AyVNit6QYqsv1ofFffOEEALPbyR+6wcfvllMwa+Hqfp6uqQhSXXIu2f191ukV+J7QMkpZon4Z99ztx0mKjaw5IeUV+MliZFRX0zdxX2UHdvJCiFuEEO8RQtwrhLhHCPHD2es/IYR4TAjxsey/b2w7pnlImZPPJGB9G3zlt9pAMfHq90R6o8DTmXHu5LlO3jjR7nTNOPBw4dSKLoiy1S92TgHohoSpLYDiG0XWoIwUMcYJrI19fS+6Iu1R4OHGExMACm1TEOGLfH3C6KBO45sCmSs7JDFlSF4HKZZ47cHJAyV0DefkrcRuF7pJ/e3Z9ZGlrtFtE0rbGvSQUDJOPs6qmnkrXQBYH3fX4dP1+byckyckPzogkucO+/q+nROpqtjtg+QnoY/zG2Nrt5ynm8aaTWi/PpNUnbJ280nF+VNeR2ZBkMUQpfDzuvXnagO4hkDyMYB/KaX8PACvAvCDQogXZ7/7RSnly7P//rz1gCVOeJw5ob4NvjgKq+Lk6YHq6iTHgaebqe0vYiNbY+oaI6HsSAdFKcaBnyF5VRBl000GSdK16hKkDCev6Bjl5NXvAs9DGFDi1SDtLq0N5oni5M+dGANQfeVtdZAaf2MS6vxC18QuAJzbGDMkT4l7Q/ethIF2CH0Si7edXTUotYQuWwl9Nn539cvZtZGF5DXdwe6vyVn04+R9jeTTQpAaB0au3Gcncv7EGGfXRrg/o7QUJ+9ZdNP6Ads+XN0rFotZSH7co5+/lph6uOnkxHDy0lSkUkEm7db6JEZvPmVADmCCiGBafMXJd+s/dShIXkp5UUr5keznHQD3AnjWQcbkOnaO5H2vO2dOSDXPpwI5JB8YJNxVBz4OfO3kp5FN13BO3s+cQJc4NY8TjEMPN52YYB4riVfZToQj+S6LhOgmSvrECa94NV3y1sZGXdMVaYwChZIAdUKUpa7hnLyma7pvt28+tYLNHdrpmOs/Ykje75EYpfFfcvNJXNqeYWcWlXYx5YnXPkjyhvWx5uR5EKHrT3Sfek+H8bP5j7IulDQ/qhil16z591CnhL5n7UZiSkwHRSTf5frogO0Jm5MndU1ZzqLLTopJTNfHgW75S+oa+myAKl67IflFYtYnAGzuEucPiCx60D2eaIXfEebkhRC3AfgHAD6YvfRDQoiPCyF+QwhxuuI9rxNC3CGEuGNzM9OZcwncgTlzg+QpOJS1NaDEK9DtIZpnbZDpPNq9OaNrAs9I+HrSKfNMnXLhpEICj29NS+c/ZvPvgoRnkRqfHMoiKa94XTsgJ09JsZ15bCV2KUid4Jx8j8TrzScnGiVpusbP0TV9kHw2/otvVnTE/Zt7VuLbWp89dlL0Xc+ujzWS54fC8CCux+/T1iAQGmRodY0wbQdU76bu659Tk+PA188btZXgdNPaOIAQ/XYKN6yPcY01KaPEq2D9a0gn38U/8Ipg2sma+dtOeBxkOvYeQUo7+WyNlgWRSZa8P3JInkwIsQ7gzQD+VynlNoBfBfA5AF4O4CKAXyh7n5TyjVLK26WUt587dw6AWQRhjpPvw5kTkt9bJAUJIm9rsDLiialuSHgcepiE6rzYKadrfA8TUgeFZvzOO4XQw5k1ldi9vh+ZBmUMKa2wINhlEc6iRAeiUZbZL6NT+kooF3GKEbuPUU6HT6/zxGuXIEV00PmNsXHyDP1NtE6eI9XuOx2eWCzT+XOk3YdOObMW6p7ydu+gzAH0nL/FyXO6JleMY+9E+oyvrjU9b7pLZ5BTp2Qy3bZG1+f8iTHmsTnrloIUYLTs/SSmZv6cb08zdQ1gO2GlY+8+/smVEJPQK9A1gMmL8N101/HrbBAnL4QIoRz8b0sp/wgApJSXpZSJlDIF8GsAXtl2PIuTZ4nL0OuOlPhhBcSpJppOYW0TAvaQdkHycYpJ4EMIgdXQL1HXFHcK3cZPMA58ze0vYoa08xK+XuOnmrcOsmIznRhldISduOwuoeRUTJkElIqh1Phd6SAP5zL1zjxOGF1jOHlLQtkDyT/v3BoA4OL1/EHnTF3TEwmPfA8nV0KkUoGR1Bq/KNHsI3EccSefqHN8vVziWNNBXZwwo4PUKVyJDlT5xCVx2l3oOM35Z3QfFUSRBBSAtdv0RE+Jqe8hDIxEO2Xj551wn8QorVFKvNKhJAD0Dot8xJGja4QKd78O4F4p5X9ir19gf/ZtAO5uO2ZZbxk64xXo1ntkHqc6wmsnn73dDiL9ttscCa+MAuwtEsRJCpFVyxkk3w/pER1E2/Z5nFpIdcSdTA/OeR4lGu2qU5qkOeicSdQ4ku+SeF1kdBNXznAkfGZthLWRjxfcuNGLDooS4+QBpV7QIMGia1jitQcSXh0pqmGRb/vAdlJ9JZSBLzSdtT2NcjsFceDxAdOFEuBtDaAVHiuhB5FJBrtIKDndMQlVR1RL4luo6OyXuDy3oehK3XYgk4ACxgmr3YjXK7E7yqiYhUU30fg2JdcHyY98D+fWzW6TO3mdeM3UNX0krHUWNP5Fs30pgNcCuEsI8bHstR8D8F1CiJcDkAAeBPD9bQfUdIqAdahHn94jsyjBzSdXcNmb6eQrb2tQ5iS7JS5Ti/edLmIsElW9KYSN5Gnr31WiOWbc/jxO9DWxkN7IBKlO6qA4wSTbJVAxVVlierVvMVSc6gdIvZfTQWobe9dPfB08T+CDDzzZb3zfww3ryslv7sxN7yDPw/okwCRLXPODUbqMD6gHcOSrnEWZ+mtygMRl6HuaT96aRlqOy08msipqe9IRvE6A1DWAcmJ0dkNXHXiek59FiRVkOV0zDjyLEmljNFYeycssSKk5K6p0nPVf6rNTC32bk08lCyIeQ/Idx+f5obPrY92tNkklxoFNN421jLl7TqHODuzkpZTvByBKftVaMpk3as4kBOfkjTqlK6c9CT08/9y6peEF1MUlBHVyJeylXpjHKU5lD+jqyNA1xI+TA10JfY0Auzn5BOPQONhFnOpFwZ08aXiBjnRNZIIUNWjjSJWcTG9OPlHqGkJ0eboJMA9TyNB+6/FjG8lv7sz14Q6+J7A6CvDef/Vq3LA+wv2bquq5K50FQO9GFrFB8kR3jAMPp1ZGvSS4qrePp5u/bU8j7dA4HdSXrqF7FTC6hsry9XX3BFZ0oO9WVU5jjXwFRDiSz/dmod1ypyCSzZ/qLHgxkc8485XQzypUuyUuqU5BnYGbS7xSYpQj7Y5BigeRcWDOM05YEDGUmRp/jx2T2WRtqK9B1TVDWZxIxoNlXRwDrxeSUTp2H88/v844eePEvubFN+JN3/8lePbp1V7b+XmmkweMk4+TVDss3spY65Rbzl9KWYLkUzsxGhwM6VlIPvAKiVGicjaYk++DtEmuZ9E1udWndwqdg4jt5M2hLep63HRyopxcn8SlxTkrJ8+vj+8J/OEP/EP8j6+6FV5GfXR1YiNO18xiOyfimftLuu0+id0RK4aiHaXmtFk9B+Vl2o9PdIfIkHz5TgdgnHyX8XNInmSUpK6hOa+w3WjfnAIPQJxOMeqa7v3kOV0zCthOgQURnwHC7nTWIUsohzLqRQ3ASrweRJ3y/PPruLq3wNW9hSVRC30Pr3zuGQDotd1WOwVK7gXYX8RYJGb+HMkHfredgqoJUIFi7JvEaxknb/VO6VgMpTl5T1jH/3lC4IU3buD13/4yvPpF5/upXzKkDWQokTlJeojIDN3UMfHqe7rkf3du1xFY4/eQUM5j1UWTksSLXJAFgJc++6T+/D6ccBh4+tSq7WlknfxFjp2cWH86xZToU+DiSNKM3w3JW9XdoYd5nOidTsDUQUIYR9qpd42WmI4gBHA14+TT1E68Ttj8e+UsAkUtLZgT5nQNXb/A8zqBEE7XjLL1A5i2DzQ+YIqh+raqrrIhOPnBjSc98ic3Ad2cwCxKcXbN10jv2v4i62BnFjlZP7rDIPmVkY8ru/NMMWFuHEBn1Hajm4gqUOoahuRL2gJYidcOi3AWJZoqUMoHaak7hBD4jttvAWCcezcJpbk+tNWtcsJ9dgoURMhJ5dtKcOuL5AmNEhLjdETeunLCUZpx8hrJR1bvIICkoP3olIiJAGhnQ+uKrv+PfN0L8fzz69n43ZrcFXTyHMn7rA4lU6B1PT6Ptx04uRIaJC9tJ+yPDEXXV11j6eSlnbMgAUXfnUKYC3BJaiSaeYVTHwlrnR1JJx9nnDxgJ177IXmlHiFHQ4mhvIMB0JlOUeOn2gGvldA1X/icM/iml13Ai27a0P2k2zox6rtDST8AFpLkvX24hLLLIqScBQCtHNBtH3JOTM+hh4QSUE4yr67hRk6oT7EVUUvTRWLRBdz65FwWSWLm7+fmX7KGOpelx4qTJwXY9jS2OH8A+J4vfS6+4gU3ZON3o1OI8yflDGCuL13/12RB3Izf5foYTnsSquvDJcRcYqrG7xqkDPV2enWkOXmSgAK2Sq6rE9bzz5LCqaR++3bidcyfkV51BJ7eyQK2RNOia/zh+8kfTSfPOPmX33oKr3zuGZxZG/XqBz6PFJ1CnOMsSrNDjEtQWI8GU1xnvjIKMiQptUM5tzHGL/0PrwCAzkHKIHnDaSsdeFFHPQ65DrwbkjcoRT2AGsn7eSdMnHl3OgXInGRsJ3a59Q0iVC6/Ogqwt4itM4K5mTqFbkGE7q9OvDLOPG+dkV626wsyJ1lGN/3oN7zIfIeOdEqcSH1d83Rb2fw7H1qR08kDqn8TzZ/Qu76GPTl/tdsJsD1TYydMXWPRNV2dcI6Tp89MuXrHN/PvXqxkciKcDuJsBe9d0/WI0Da79qPp5Bkn/4pbT+NN3/8lAMzF6Irkx4HZbpGT5Fl/sj4NpminAHAJZVqgCgDmZFouck7XANCcXsqQsNW7pof6YhalJunmURdK6PG5UaJxkST5YUotTlKkEhaSzyd2ufVtazBaNdeAdlJq/Cq6pmOQYnSTSnxn41U4yS51HFEi9boYB77KudTsFPrQKfTc0Hi0Q/SLw/emU8LAJOmp/4vNmRsk36t3jVY3ZRWvDAl/7o0bWB+znEUPJKyQdrbTyZy81rF7noXku+RceEfdMaODJFPX0HNFtQxD95M/kk4+qaRTuqtfZhmSp0U2j9LK8bs6gSSViBKpnfDayMd+1qCMHIM1vqaD2iJ50zwMUGh9zpwA712zEvarSJ1zzjzI2hpopFr8e548ajJCLXwrzemmQk7kAK2MAQqyCbs+FYnXjju1fJCi9ZdPHAPorB5ZMCc8DlQQSSuCII3f1YnRGtEyVh0Ey+mmg/SuAYC9DMnT8xT4giVGReedmhpfUTLThaqo5U7yF77jC/Tfd6VToiSFJ2zKh6qyOR2kkXxXHX6OrtF0kJS6zTBvSd61HXmba3mE1TXlSS2gT1sAswAVJ5+WL/CO6ou8E14ZBZAS2J3FFSivWxCZswM9AONgqZjL9wVOraqE3em1EavY7U5nAUZdw3cKeQs7FGvwrbB6b33i0hRbdXuItJMfBxmSNzkLbr16szC6iSSUvMFa3rqf7GOc8DjMqXdKE7tdddpSj0/0G62rKrqpj8SU9wnSSJ7l1cYscdzl+us+Vp5XzIlUBNmux0fS+uE7YU7pnlwJcWbNqKe6dMK16Ro7r8Y5eesZfCaoa+Kk3An7HZ0kIW0LyWfb7Xok3zYxajhzALrd8NY00g3F7PG7IVVTiJPVCmQSNU6nfP6zTuJPf+jL8PnPOqGPXmu7SJJUYpHYiVcpWWKu5BqNWKfBJuN9O+jfusRlXv3R9jN0VW7oY38Rs4rmHF3TK/GaWonXaxbdVPz7Pif78JzFvGanA/ThhBndRIlXQvJlTrIzUlWcvxCiwMnT9Q58wZBqPx04iQx4nUJpkOoYBCMWBPlOOGESx5/81pfoexIyH5TP+ZQZp2t4TsSWaBphSOB7nejoKElLASW3o4vkaznt7kh7Ehokn6TlF6ZrRaHmzEkLnzn569Oolq5pn3g16hqAOwHbCb/02Sezwwe67UQWOc6fri81dSt7iEZ++8TQPOfkSWdeRUfQAQoHoWso8a0+rzh+VyfM21YUdfLl97hbv3fjZFSr3sRq9Vwcv3vvlAInH1cj+cDv6CRZECGwsDunxKu5br0loKmhO0ZZzoK+fhVd1i1ImTYhvL8SV++c35jgQnayU9cmg5zOGuWDiCCZuMBkZBK7XQ89Kcv/cTuSSD6poGsMp93uIvBTj9pIKM2hIW2dWHZ0npZQmoKW0vl3LIbK7xRoO1/FORuk2vb62PPPI+my70Boqo0RYhwzJKyQtvp9Fd3R9dAQTtdMF0nlTgHogVRZg7tR1qWwlk7pygmnrDo63xagSr3Tkw6inU1eQpkfn56bKnvD++7H825Ywz96yU1WENFIPqNruAyar7FudI2RaGq6smYnFfge9hbthAFAHsnb6poygMz7Z63Abxx/wekaa3wTZP/nL38ensy6U4Yt1E3vuOcSHriyhx/4ys/BIk4bdxRHFsn3TYxu7szxy++5L2sJUILk45rEa8ftvD4Em7WzBahKslq906cYiv6d894pB0xcznSQMihLfW6G5CucWNvx85y8pmsqKl67jg9k/eTJyYc+9haxhf7Kxu+qrhlzusmiU4p/3y8xapxhk5PvirQXDOkFOojbnDk3v4WT+a2/fRBvvfNxNv9yJE/r83u+9DZ8+xcqLX5niWYuMcrpvtL100MdRE3UuESY0zXcuoonuPrIJHbp0Bb1N6+49TS+9sU3qvF9dcRpHef/1jsfx2/+7YN6/DLWwJpzq5keslXRKW2c5Ds+cQk/9/ZP4ZtedkEfs8eRfL26ppuEsph4NZE9LKNrunL+ufEJyVQnpjvSWbmdAs2Pglc5J98eyfM2rkCGQmNZoJsK47d8gKSUFie/ktE1vDdR3oKOSHIeJxbdZKlfBkiMRrHU920c+tiaVrdlAHq0NWDXpw1d08ZJzuJU7wJ5YncSlnPyr/2S28z8WTvfVvO3JKaeBXKq72+3nEuek18kiVLvlNJB3XyE5uSZEm6RJJVBhLf2GAXF3wPq+dzPdis8yFbZkUTyESuG4tamARdtFXeyAyQA0xMi8ARmcVJNB3WUUOaRNtE1gEnQWOO3QAFSSnzowavagQGGkzeJ14adTl8k79lIvsyJEZpqY/nEq1HXoHr8DkiVKxcAI6GMKugs9Vq3QxkWia2u4eqgqjXaWV0TGCRvJRYrrn+TRPMzl3d0+X8ZUp3X0DVt5j+PEg0EOJIksEB0SakT65FTKKib6iSmXvP1efTaPh69tl8YX5/ZUANyuhZMLlh+yJyXIC2Jpj3/ZiA4jxMdSKt8Jbcj6eSTymKl5sQlRbjdeWxx8vSv4eRLtvJ9kTA7Yk6P1bMY6kMPXsNr3vAB3PnoVmkx1LwF3dSEhC9lxyCa65M54cBG8uUSu2Ykf3l7ZgUpTtdYSKxkcYYtkN4T2zOkmTKIxgVUO+Q4lZhFia62zFvn3iA5nbxF11Qgva5BhEsoVYMv9bu+SP61v/73+JX33gfARno0nFFPFd/bpsEaP4aP000ayc9tJM8tDLqekZpqJ9aG7msTpH78LXfjx96izjCyit0ob1dDZ2mZZYfdbOhT23QDUquq7tuceTCPUkSJRJSklYWX3I6kk6/m5Jsz2/uRWmB789j0fmHZf3IyVfI3NX7HxGWOk+dz5cblV1VGhyJc2pqVFkMRXVN2fUidUjf/BzZ38ar/49348ENX9fxNoYdJTpeNT3OpQ9qXtmb4hz/zl3jfpzcxzzlhQulJHd3RgOS3phG+/PXvwdvuuljYKVCTsqrEN9BjO8/bGuR12j3pDm55pDqPmhOLTTuda/sLXN1T3Ro5J0/rQ9M1PRqsxUmqAynNP2TzB4Ddeb2T7JpT0B1pW0h823ShvD6NcHWP+kgVE68E3kouT2fKNbboIDvxWkfX1H0HCkL7i8Si46rsSDr5ak6+2UkSXbM7jzHLSRz5yTWlSL6r+iVHp3AnPypBqSaIVI8/zYLU9f1FgTPnSL7SiTUkti5vq8X98NV9PX+uk6fvVeXkQ7+ertncmSNJJR65NrUOTACKXRzLEpeNTn4/wjxOdUNT+QAAIABJREFU8fj1acHJ0/XfnlU7+abEa5Sk+OL/+C6dWMy3NVC1F9VOkhJnbY07GTpUIt+gzJ5/fWKXziDgTpg7Ad8TujVAnwZrtGYIyXORwbjAyZc/Y10Sr3FiOrrSfdAS3wr1S9P48yjRfoJXHNO/sxq6sms7bN7HivdmSqRsCCLV49P338/6NB1PJJ80dImsiaJE1+zMYoZU2XY4qnaSbXTUn7m8g6//z3+lnHAOaa9yTr6GrqnNKWTzv5Y5M346E0nsqpC8+ox6nS1dk+v7UQHJ60UeJaULHGhua0AP//Y0Yjp84ySbKjqbEq80/u48LtBBq1mjsp1ZXLqTAqi3SfX8d2cxLm/P9QEz+bYGgHEC5Zx8e6SapoqbtXTykZGAVvVXauOEDWebWvMMPXP/+jRYo/HnnJPPIXni5MvVR911+IFvX//poj7INjn5RZLq1gt8J0Lfo0540LkWJSnSQdR/qipxT/OqMpqfPoXuOHLyVdGpzaEPhIR35zFDqhknnxWbVLU1AJobHN39+BY+eWkH92/uFjnzwLRDLi3mapFTmGYPyPXpwuorA5CDTSr5PKAZCU9LnDxvHgU0IPmgvpiFnMv2LKpIvMpaumPUoJOn+e/M4gInv9qKrqmfvw4iM9XyN06llVMAjJOp7CffEqlqqWfG1RKSj2t2Ok1ngNI91Zx5Ts7rM7qmbP6+V78TKYyfSGv+APTxdaVIPisWa90WgFWW6usfHYwOmkepRvJRYhe78e9YFUTUvFpy8nExiERJjURTiyda3INFkunkn2IkL4T4eiHEp4QQ9wkhfrTNeyoblLXgwwgJ782LSH4Selmr4fIFDjQntqgvx9Y00pw/OUnAJF/L6Jo2WzHt5Pciq9oSsJF82QMEULFJc2J6axoVghQvhqqqlG5E8gtC8kUn3NTWAIDVjrXM6J7uMSSfbyuxPYsrFQdNidcpH79k/vxvqipG22uobXWQTkzXqEea1CmE8ug+cE4eUE6widOuc5KarmESPpq/am3g6cRrHRJuS2nFJUi7zsk3rX/AIHkSB2jOXNNBNUi+Y38rVb1vq5sWcdos0ay9x4yTb6GTd+rkhRA+gF8G8A0AXgzgu4QQL256XzwAJ68klDaSN5x8Wu0EGjhJQqrX9yPD+QeGiycZZV0xVH3imOiaRXbIthl7rBN/aSnKo89og1QV519RDFWTeB01OGEKIhaS1w8pBZG6h7TdTmS3xAkTXaOQfAVd07CdJ+e1u4hZELTnP6V+6RXqndYS0Fxl8ThQicU6CWtT4jiPtOM0tQBH4DUkXhvGp7nN4qTgJAG1lnZr1DVtWpP87f1XcNejWwDsnIXZSdUh7WaJ5jxKkEoVsOzEq6Er1fjF93aVKZfp8GuLDVv4OPI7e4u4VVsD10j+lQDuk1I+IKVcAPg9AN/S9KakgpNvozPf53QNO1kJyJB8jc4caOYkbSRvOxnAoMkyukbJ+uqjtEby+5Gia9guYRz6kFIhjVokX4cCNB0UmYpdoms8xslXOfkenPyosB2uczL148+5k881cLMSrxVBPPTqkTana/I7hVZ0QYfEoqmGNJw8YAJllYS1lq9lygugSNfwYqTKxGuLnYLUTjK1Cv/Ggac/u1ZnXvMdfurP7sX/+e5P67+je8lbk1SO34IOInCwn6M7tE6+IWcBtNfJc/XLqLBTKP59k4KQS5OnR4STfxaAR9j/P5q9pk0I8TohxB1CiDs2NzcBqAtY5SSBBiTP6BqN5AOjk6czKKvpmvpDHwjJK7ojAT8gGTB0TdWFb+L8pwuG5HPyKPp5v8YJNyW29nNBBIB1MhTQrK6pRdoL5uQb6I6qitfWSL5CQhlVgASAnHDz/EvpGt+cLlY1/y4SQd5rnH8OzaGyS2QtZ55m/3LOnNM1gu2kiu9vcpIUZNXPCgmPckieH15dNn+aV5XtL2INpqIk1RRJgS6roTuq6CBSHwHqHiu6w6ZTdBCpaBAHdKh4ZfnFUW78qi6jQHUQ5B1a9xfJkdDJlz1p1tWXUr5RSnm7lPL2c+fOAahuUNbmIGBLQhklEMI43HHQjOR9r/7Qhz3LSdp0CmDomiqerEkdYeiayDo/FjCIe7qIK4NUU5c/ekC2MiTvsevDF3mluqahrYGha0yQzR8/N615iEZN88+SnruzWJ9QlZdQAoY7zVvrxCvbCXYJUq044UxdkefkCanWIeEmzp/ztVLKghMI2E6pj5OcsXs/jYpIkueQyummZic5jRJDN7HELgVZQ9cU39uU91K94NXPmtPO6+TjNk64/W4tz8nXrf+mgswZC7L7i7ggkS0z107+UQC3sP9/NoDHm95U1U8eaNYhE9ImTp5OiQeo4jWTUFZt55sSlxnfuJ0heZ50BQySr+OE650Y08nHiRVENJJfVCP5pmIoI6FcYBap8en6mAZWaekCB5Sji1NzTmveiC4juoZ6jfP5U/Ct2g7XBZFZKV2jxl0bGwlr5fVpkFDqxO6CIXnf3p0dhBOWUuKrfu49+K9/95DVhhYwQbyJ7ojStBJpc07eBJFyTr7PmQ0cyRsnbyP5/FjctMKkYTfLdf5BBZKvbzLYjIT3FrGdeM33bqrRybcthipL7NZd/yYkzzuEqmKop56T/xCAFwghniuEGAH4TgBvbXpTU7FPGyRG6hruhBVdQ0i+Gum1Ue8QEs4j+dUWdE2btgxxKnF1Lyqoa+hvatUjLeigrWmEae76jHyDIuroGqD62LFZLvHKdzT08zxKG3Yi7eiafL/6ceDpApOq6+83qKd0W4xZkQ7K0ylVp5fV0R2LJMXjWzN89soec/J5pFqtTgk8lZytRNqMM6cEaJ6T1xLNisQrUOPkmZOcRUUJ39iihkrEBy0SlzOrbUIx8VpLdzSIG3iQ2p8nFp1FTEEd598mL/if3vEp/Mgf3pnNn+8UhDWHsrYb+vpXjG8j+cTqTVRlTp28lDIG8EMA3g7gXgBvklLe0/S+KJWlfU0AQvLlC2SRZcsB2m7n1CmBp1oNS1l6iDHQLKHcZ4nLvMQRYBLKKrqmZRABVA8YWydvKgrLHlAav5ZOyRZJKoEru3MLefHAUeWEaT5Vjth0x5PYyh2ewrerVTuFporaeYmTH7OHlLTydUGqTRDcmycFJ68Tf7Xb+Xq6g+jE7Vmk12qYG7+ec25ywmb9bE9VawNLJ88cb3mDrHo6ZZZD8rzYB8gh+YpiMaCa7khSlVicarBjt2IG6nMWvN97mfG1RQotfn2o0yhQ3taAtyaoso8+ch13PHgtm7/R+RMVYzj/4nubzrGexTxIFedfZs5bDUsp/xzAn3d5T5LKak7Vq1a/0M0XQiGxWY5Ood4vUZzWIPl6JLzHEq9n10YFZ06cfCVd0xBEZlGi0fjVvUUhSAEZkq9Rv0zZg5i3KQsil7bnVhDhi6XOCQOoDCT77LOv7M7txDFDwnWcfxskL6VxYpa6aRxgb5FUV7w20Fk0/iJJNRI2EkqbE2468yC3yQNg1s/uLDatmDvQNSPmZLhD1fNn93eLnHwu8UpWr35ph+QV0i7n5OvOaa6mU2wJaKmEslbd1ITkzeduz7L1wyuCfZ6Y7sfJzyNTUbuITcWu56neQfWJ+/rxLbomSo4EJ9/ZpJS1idE69QvxwWfXxthdxJpzJiOHvx9VO8kmdQQhMZ14DTvSNQ1BZH+R4KaTE/3/5YnXOnVNO04eAC5vzSxHwQNrZRCh0uyKz+BO5sruPIfkM84zrpdotqHjAODJrJkbX+RawtqT7uPXh5rFmbJ0Yc2hT09/3nYjr5PPqy+qdPJAu+18mROznXzx/U3FOBanPU+stgyAjeTr+7HXAzWLky9IKGsSxw1V5Xzdbu0XdzqjwKvl5IMGpA2o9c3VQaPcTqGumK6pbYK1k1okOAo6+c5m+nbUcKoND9CNJ8aQEri2F9lInh1PVkUHNSVeKUJvT1VbgEkFXVN14ZsaiO0vEtycnSep5tyNM2+TsyCp4ebu3ApSnK6pooP46Tml49c4eb7drqNTklQ2cs4AdM90/hn03aqQfFMdAafLruaCSJ4uqKVTKoIg7Q525kZiao7/y+vki+/XdEfldp45sRK6pukeN3HO3MnszIo7qUYk30CncAQfJakloWylk2+gUziSvz4trp/Q92o5/zZNDKeLRFfU5nc6ig5qEcRbBNmdWVwrIiE7ck6eHGAdJ191AQhln98YAwCu7M1LkfwiqU78NfXuoAdwkaTYmkYFJF9X8Qq0KKtfxLhwiiF5Ttc0KBcAVZHapJOn8ZNUVtI11ePTNSynhPajBCeyM1Gf3F2U0jX1vXHqt/NlSD7sgOSbEq98/Gv7avx8MVRtv/EGzllLfGdxpYRyTyd2i2so7OCEt6cliVev/h43Osmck+HzB8warVZPNSFVmw6yJJSFnEXx/U05C75ur5cgee7k+0pAVTWwmmfMGqyZ8WuKAb369UNzGwVeac6lzI6sk6/j5KuRvFp05zIn/+TuwqI7JkGzkwz9+kMT9uaxPtg5nxgFWhRD1ST+pJSYRok+GR4oR/JAPR3R1Kb0AqODLLqGo7xKOkW9vqhE8rGef5zK8sTrIqncKfB2rKXzL0HafN4ko+zbarhs/KK6hh7SkvG9eiS2xyS+eQmlDiItkHwbJ1mK5Nmky+5Bo4SyIbE7yVVPF+avg1T7xG5BQllTEawP9WiF5InOsilFU/FaM/8WBWl786TQO2jki9qdAgWpKnEJvffs2kjf32PHydedzwnUFxNR0u/GE8qJbU2jHBJu4ySrkV6Sqmo5olOu7UcFJ1/X1sCMX42SUglsTAJsZM6qjJMHaq5PQ6vh6SLR1weARTdRq2WgPPMPNHPy+4sEN7IgUqWuqVqXhg6qVhcQJXNtb4FR4FlSNEPX1N3fjnSNTrwauqDq5KmmMwN0HcecO3mbc6bcUh/1SCknzyR2loKqho6oS1zS23ZKJJr0vNVVZAM1OR02/9kiRZzIwvWvLSZqmn8ZJ8+uTyPSbqGu4U30FCdvxhllCj+gKifSkPjO5nZ6dcSC+LGja7JkVK2Esp4PJroGsDtEWki+BmlXywPVor75VDkSBoCTKyEAu/oyP/8qFEDzXx35OLWmxikrhgLqin3qKyL3FzFOTEKs6yBiz5MWTJNOvrpYI8GN7PpbOxHGqdapa9T41dfoho0RAEXXjP2KIFtX8VonoYwSPQY5ed5Kmv6mTuev5l+B5OmwijjVP/N+8vQdgfrj5yqdGEPa5Ui+fg01JRbncaLX+HaJkzFIvjonUjd/SwcexVkxlHpP4HvwRD0n35g4LuHk84nXec34vifgiXatgPcWsdVFkz5rVpPTCZskrNn9PbM2MvN/KrtQ9rG6NrRAJqGsuMDUx/qc5WQ4J8+cfE2r4UqNc3ZzLpwqp1MA4Cs+9xx+5Z++Ai+6aaNy/EqUFxknf3p1VBjf3olUPEQNnPwsSrEy8vWDmk8c0yJrpFOqJJQL5QToYS/rvTOP05rEd1MQSXHDurq/eR0+YDpRVtM1zRJKGv/JCiSfpLK6C2gDJ0xrFIA+gq6s2EqI8mKZNhWRNE4ZndKYeG1AkrMoxfokgO8JtlMoIvmKy99YMcoT9/uLpHC2xCjwausIjESzXl0T+kJz8nn1S93JUAABhfL5J6xdxV5WbJXn5Ou6UDatHwogp9dGesdx7Dj5qEFdE2TqizKjm39ugyPtIl/YNH7TA3rzyfLEKKAu+De+9ELpA6o+t7oYihb4JDRO2HqAfCZP6yEBjRN18O9K6OPUaqg/y5p/9nlVO6k6uoZyCqsjHycmxfnzbXE1Elavz6vomijBuXUTxAtOvoGu8T2BVKKyLcMsSjRIuJZT1xCSrJt/Eye8Z9FBUfaePF1Tt1NodgJnMoCwPYut9wA2eOpzxug8TjAJfKyEvk68lnLyNeomoCaIsPu+WzL/EU+M1rVlaKh4PbU6MonXnMyX5lZZK1ID1PhOhJrcWTp8LtGsTXzX51zOZM8vcIw5+Sqk6nvVSHW/hK6xi4l44rViEbYY30qMht0uYTu6JmBIvltOoe6MS3qAuJPP70Ro3CYJZRmSn0XqMISVUYATJUGKL8bqxG69k5xGaqeQPy2IjKiWyvvbIEHcXyS4YV1d++sldAS9v653EFDDyZcg+XzFq5T1OSk1fnUQpHtbhuS5oKGqyyVQ17tGNc2bhOXqjkZOvqmic1HMKQQWkvcZnVUyfsP9JXByZnWk5ax5JE9WV1XeJnFM8y8kXuuQfENiep7VmNDzlR+/zI6ck2/i5MMaTn6/hK6pRPJ1Es0GOuj8ibFGdHkn2WS1dE3G+Su6puiE23DybXIKKyMfp1aUIysg+QYnRvMpQ9q0k1oJPb0TsRxMQ4dC/vd1nP/KyNcqmjyKIbqmrncQUF0sM10kWB8ruklKFBK7FFSaEovV6hqG5HN0gRBC/1yXWAfqkd7GJEDgifK2BpaCqnr+ldc/a5o3Cf3SnUKjuqZh/jzxWrZTGDMk3Kf3C3Hyp1bLnaT9jJUOgbozJ/j8aadgB6l2O5Gq8WdRikngaRUfzafOjpyTb+Lkfa86cba/UMqLSegz7XE5J18Vpf0aCSIh+fVxYDjtktLyOqujg4iTXxn5OEVIngUmKosG6p1kVYOs2cIg+ZOarslx8g3j0/fliEXPXwepQGvlq5B8kw6/DsmvhL5OHFcj+XonXKdDXhl5JjGdG3/c4OSbdOx0jQCG5HNODDhAEIwTTEIfKyO/FEmGbN59GnDNoxST0FNOvkTC14Tk2wRxsrLELh0hCfTr966R/NqIzam4UwNqkHyNws8q1tsvSny5eqcsiDS1U1dNF31NSwLHMPEaabqm+iGq0pDuRwnWxurL0792xWtbuqNe47zGnHw/JF+/VbXpFDuIaKRXtdOpQapTHkRWysfnfTbKjILOvMTJ0zZ6ZeQbuqZiK9zY5bJEhy+lVJ0/a5w8SSirtrAG6bULIvn7S9+nbisP1CVeEz32tb0InrCvBV3f6pwL0SnVTmacceZlOurGBmUtulDS+GWJ1yYk31QM1YTkm4BCYxdNjeSNk7fzRm12y+04eZ3YrSg4rMzb1VGukepZtMraah8/Tl4nXqs5+bq+F7SNWZ8UH9I2FaNBjQSRqhVXmTol7ySbrBbJMwllmboGMN+hLnEMlC9yQ6fwxGs5J9+M5ItOhs+fEq/j3EOjdfg1FbtAeWJ3znIK61V0TVZxXHcoDFB+fShxvDIKKukgnZhupCOqJJQxzp9QdOLVvUUhGDXRNU2JuXnWPnp15LMulxxJtky81ujwJ6GHlZCNX4LkK4NUh37pmpNnY3GHWdcFtE4CGnhC7zSBYjGUHr9ut9yiWKxMwmoFqarxa8QTs+xIUOuAnOPm5Onm1Ens6tQvq6G6eevjIp3ShpMPa4qJaKu9NgpwssIJN5mq2K0v5loZ+Tp5vDGxG4U2IUlapGVO0uLkNR2U2ykQXVBxfVZq6BoeRE6slCPt5vlnTqyM89fqI08H8aKEMkPyNXUEQLmTmcdZ4jhknH/V/BvGr6NrbsqK0aZRUggidD+ark8dkpxklGX+PUBOXdOjdw0heU4jdql4bXPyEQVw3TahpNaiaf6VEsrsjAMCA/n5W+KAGpl1ZbEe1+FndA2/Fk1BFqjfKcwjpW5afTpw8vW9R6q32vSQr2f/8sWoTilSP1dz8tWJXUqarY4Zku+qrqndiRhO+1XPO4v/93u+CC+/5ZT1N/lDt/NW5wRmHMlX5BSakHzoe/A9YfW1NvNndM2kSNeo99cj+TrOlj6zjk4x/eTL74spGy/Z6SxM4nijysk3Jl7r6ZS9hVK/0LzzfKpB8qVvbxx/FqcayZPlj/8jq+stU8eZjwNPB3vArqg1nHz99a98hheKch35nm6Axnf1tkKr+P6mIEhnQBCdC+QllG3ommYZNKAq4oFquqZWvVND1ygkXx6kyuzIOfm4IfFa37vGVCuSE+BVrkII/XA16eTLEpf7ixiBpxQQ2kl2pmvqJJSGjvA8gVe/8HyBt2vi5OseIj0+R/L5YqgGugBQBVT1dE1QqvPn/99U8Vq2E+FBpDrxmhVD1VQ0A+VIj++kKpF8Y2K0Hknuz2OsjQK9Q8uvQxPEK3IKLRpYTQLfUl+U9TxqDlLVTrJup9C2d82iUidP4gmPNVgrp2v6dHEkJF+lTrHbPpQOYWnpy+YPqGKwUrqmBecfNiVec0i+6oAisqPn5Bt08nVbGaWuyeiaSTnSnmik16COKFnke3PF+QshDoDk63rvxBgFXq2Dpc+r4/OAcgmWpmtCHy979kl875c9F6963ln7/Q10CkBn5dapa/xSnTzQzDmPapww0UHjoIaTJ7qmITFd5gRMEDGcfFmxW938m3Tge4sEa+MAG5OixFR9HtFBpW+vDeIqMZ2pa0KO9IoUQVMxV51OO4/ku+jkhRC1VcfTRb06yOLke3RxpHOT10blnHxbuqYu8Q1kbQf2i20TbCRfOkQtkp837NTK7Og5+QadfL3EMa5F8kCzBK6uy9/+ItaLo2/ita4f/pTtRKqMPq8x8VdarGSQ6iT08e++6cX6e+TfX7du6ED0qvEnYXnFK2C2xpU6fH0wSlz4HX3mysiv5uQbiqHq7i+nswhpFzjzhvlrnXPJ+FJKlTeq2Yk07RT0/S1ZQ1EikUqFpgmpipx6hxx+ZRBpmL9S7+SRcHskr35XQ0dkOwVeURtUIPm6Lo51EkrFyZcj4bZ0TVMr4LNrY43ku8yf/r6OLlNB0ASpumsNHEEn38TJq2Ko6mrFtQInbzvNJiRfJ1HbWxjO/2RFxWiT1R1Ksr9ILP1rmRkkXLGdr1EX8MRo9fya6Zpx6JVy8pa6ZqVCndKQuKTgQIU23HQQCbxKJ3l2bYx/cOspvOTmE6XjGydWf30omPdNHJfd40WSIk5lhuTLi7aa1Cl1DazonnAddejbxVy0bpqCSFnOgii0cegXcl1t50+fUenEWK1LGZLPq7Wq5l9bsRt4eqeWH78NZz7ymytez66PQFOoUu/U7cYrE+tZHcRaB7rG+RmvXa2Jk2+UUObUNXknPNFIuCkxVILk5wbJv/DGDayEvnXARxur7b3DJKBVpumaGr4QqKA7Mk6+roCrKTEKqGtYppPfZzr/Z59exdrIx203rFl/o4NUxfDjwEPoC43iuPGdiKFriuqgt/zzL62ce50T0/MfmcTckIlXLsE1Tr5ip9DghOt2IuPQcPJVie+mM3zL2gLwg9MtuoYnXlsg+dCvdmKqQdwIKyNftx2oOkO2rhiqKoiUIXlL/cK+S9U1moQeruyWH5pjnLypuq8KIrXqmho6iO/U8mOWjlf72wYTQvwcgH8MYAHgfgDfI6W8LoS4DcC9AD6V/enfSSl/oM2YTZx8lYRSSok9TtdkD1HeoU3Cpoe0epHvMTrlC245hXv/w9c3fp/C+J6oPfqsyck3Ifm6VrRtOP+gAekB6hqW0TXTTEPteQJn1ka45yeL16eJrhFCYGMSamVFfnwgU9dU0DVNFtRwtkZdE1TuFBp719SMz4vpCIQUdPJNFbV1Tji7J7zsPb9TaKpTqJNQcjqOP4OlFbu1a6yejlgZ+VrOmh+/SV2j+vzXV+xyJD/K7XTa6NjPbUzw0YevV8xf9ds/xWjQKrqm6hFrooPGgVIfkRLQtYTynQA+X0r5MgCfBvBv2O/ul1K+PPuvlYMH2nDy5VGOFgXRKTesjyAErKIHoEXZdc0in2ZJs4NY4KmeKOVI0uj8q6y5GIrUCyXb+RacfxOdAlQnXqeLpJYKAqA78tU5gY1JUIrkeZdOg+TrF3jewhqkbe0UqJiuoxOuO9mHdgpcXdOV82/rhDldwy1sCOKmi2N1EOFIXgh7LZKCrQnJ1/WuoS6XZJaEsk3/I6+6FfA8STFi6pS8gwwbggigzpB+cm9R0aQvo1MqKlLbnQlRjeTnWcWrEIJ9B4dIXkr5Dva/fwfg2w8yHtCCk69IXGo+OFsc3/jSC7jlzCrOn7DplKbtJF34Mie8t4jxnNFqm69RaXw773u2Q5wuEqvcusyainH0Yd8VnHOTE27qXQPA4ku5KQlr/ZJqcpIAOfni+NRF03LyXZF8DWducfIlJ3NZ829KjJaMT/TD6pjRNYE9ThMIqXPCtLuaMLom7wDo/VXrh9QvZYldTdeEvoWy8zLfSehX7jTVe+roiASTkW+d4MR1+G0kiIEvdDfbwneIEow3xpU5l7AFkqditid2Znj2adsfkAR0rYJOaTO+6rRbnH+SSiySVAOB1ZFKTh+muuZ/AvDf2P8/VwjxUSHE+4QQX171JiHE64QQdwgh7tjc3GzByZcjYd4cC1AX8xW3ni68v1FCmV2w0orReWJJr/pYHRLbb6OuaezyV0NHRGmjkw/acPKVdE3cSDe1kWhujMNyTn5RRNrd6ZrqxCvPKVRJNE2QLR+/rqKWOHlbJ18eRKquDznhMp25SbwauiZ/fYIWQbxK5ssT37QOy/qmNCH5uk6ps2yNViJ53+wgqs9sqFawkZM0UtvynAVQfQ/oeMvL27PC76aLtIDkKxug1figsuu/YCAHUL6OtwqpskaPJYR4F4CbSn7141LKP8n+5scBxAB+O/vdRQC3SimfFEJ8IYA/FkK8REq5nR9ESvlGAG8EgNtvv1220ckDRSSskfy4wUk2FEPRw8Er18j2Fs1OrMnqeqe0QdpNOnMKEvslEsTpIm7smtmKrgmqdPIHnz+gkPxDT+4XXi9V1zSgmLzVJV5LE7sVideq9el5AuvjQDen4rbH6giadPJNeZNaJ8ycZJ6O0DmXRs68HsmTlfHBCslXj396NcSTu4vS31FeZxEzJ1/CadeBhLAmiMyzk7NGvgpEVTkRoPoZuHGDnPy88DvqLWNV1HZU11QVTJr7q8ZbCf1GPh5o4eSllF9T93shxHcD+CYAXy2zMlGy09cEAAAgAElEQVQp5RzAPPv5w0KI+wF8LoA7mj6PuMw2nCSnx7l8r87IyVXdQEJYeTpCSmlJNPta3ck7XdQ1VUFKn79ZxmlH7Tn5OiQ2rtDJt5l/2EB3AKhNvIa+QOB7uHByBd/5Rbfgy15wrvbz8qYTrxVtDXxPIeVKJ98iCJ7fGGNzp+gAKPCuj3lit4Kuqbk+p1dDff4sN03XMM45H4zIYdawKZXnKJOiahx4oNmVUQWvfdVzcMuZlcLrZM86tYIPP3yt8HqUpEhSaTU/A3Kcdov1U1cwqZC84bRr6ZqKe3xThuQvbRWRPPWWqZJoNhVzARTE63ZqptNuE1UDHFxd8/UA/jcAXyml3GevnwNwVUqZCCGeB+AFAB5oM+Y48HHD+rgSoVV1WTTVnPVfqalYg3TaebpgHqsF2MQ5N1mThK+RrmmrM5+WOMkWiWMtsWtQ15S2Go4Sq093mbVF8qWJ1+wBovf/zD95We1nlVldq2HaiQghmPqiooFbDYA6f2KMJ3aKDoAO7rY4+QokWeeEbzmzioev1ux0sn7vQLE3TlNvIqC6N8uc0QV0/8qczPd9xfOqJw/g5lMreNtdF5Gk0loHU7YT4XRU/tANoCFI1SVes947gFI51SVeq67R6dUQI9/D5ZJ7TBLHtVE5XdOkDqK/LxcG0PXPkPwoaLWTPSgn/0sANgC8UwjxMSHEG7LXvwLAx4UQdwL4QwA/IKW82mbA7/iiW3DHv/0aXWyUt6rjsbgGuc6aElv08OWdjFFGHAzJa0445+TT1LS5rbMmdc16xU4EUN+hia5ps52fhH5lMVTT9W/j5E+shNiZx4VAOItSTA56/RsSr7QTOTEJ8Lxza3jBjevW37QpFju/McETJUieThbjnHwfuuY5Z1fxUK2T9zUYyauPms4LABQNsDsv3l9qo8vVNV1zIoBy8lEicWXXvkazRZFuAnI6eV1nUUfX1HRxjE3icqUUyTNOvuKrCSFw/sQYl0uQPK0hDqbyB3mTVVOu5SDH0JUZJx8eApKXUj6/4vU3A3jzQcausqrEKDWXaqJTDJIvvzgbGsnbTnJPKyMOLqEEikGK90qvsyYn6XsCG+NAl1Rzm7Wia1okXgO1nS4gsRZBhNQkdU6GZK+789hquzBrkbNoMhNkyzltGj/wPfzlv/yqwt+0UQed3xjjie05pJRWcnCPJXYrOXldx1H98D7n7Bo2dx7N2niY9TjTnLnHOPnuSP7CyQkubU0Lr3P1DtGqbTjhvD3rlKJyHrs+xY1M/abbVoS+9XyU0R31FbXlOxEppS6GAlSwzUu12+jkAeDGE5NyTj5KcGoltNsmcCffQgJ629lVvPkjswL9yYM4ANxyZgWXSpK/eRtSXXMoRuXy+UhH9EQTHTHRZdflv69C8rSVPLC6pqLsmjf3qrNxQzEXoJAwdfDj1k5C2ZzYokCZT7524fzrOXm6B3agUkHkYEu27ozXVjr/Nk7+xBjTKNGSSbL9rG8NJWeBItJuqggGFF0DoEDZzHnitUJC2XRQO6Cc8OPXS/hmhuQnFUGkjd2cOfnHr9uBZFoy//xntLn+VYljdSym2S2d2xhbRUuA7YTrrtFNJyal6hrSya9XqmvMz1XqIKoSf+jqXm5sE8QB4F9/3YvwO9/3xZVzJDt+Tj5DQHmket8Tu1gd+TrzXWWNrVx9dRJ9NZI/KJIspwv2mTywzsgJ1CVGT6yEpXRNG6Qd6J1C9d9UnfOaR5Zl1k4nX54XIQ3yQYwe4nlZq+RMo11n4xZB8PwG6ahtpLfHciKVdE2DxBcAnkNOPqdA4tv5Siff0NYAUE740vascrfJ6Zp+Tl5dn7yTN+omE0TyDdZGLUBCUNHlknb/tAZ/5p+8FD+by+u0kTgCCslf2p4VWpJTv3e6z1XzB6rv8XMzJ//ZzZyT10HWUGVtcoTHzsnT9n0rJ1H71KUdfO6NG7U3BmjWyQOk7qji5A+G5I2E0l6EhGLaI/nqW3diEpQnXlu0TTB0TfX4Gsmzir80lVrjXGdtE69AyW5qkRQaznW1jXGAjXFQnrhs0SCO6KYmugYAnsht51UX00wZMQrgiSKnrVsN1zix55wtR/JUUh/6Qn+PvHrH5Fwqh8ezTq8gSSUu54IUpwsmB+DkNyYhNiYBHrtWg+QpiFTUEVShYKC6otaog9TY5zcmhWLJNnQloKpe9xfF3RpRfmtjM3+rbUILuoac/ANXbCc/zyVe29qxdfIcqUop8anLO3jRTRuN7580JF4B5STzDkYj+QMm/oy6w16EU8bX1hmpPeoeUoXk7flHSYookc1OrAVdUIbk+SHhrcZvheTtQDWLm4NIkwkh8Dnn13HfE7uF37XrHdS8fugM17zCRrUZVgHM8wR+/jVfgNfcfos9foudzsmVsLSWgKgCIURjxWsdEq6iU3hbA+3keyB5QFFCj+UooTK6Js/5m+tTPfaFkxM8cq0YxPNIvsza7BQAI6PMUzamn78PIfq1TVgbBzi/McaDeSefk1C2tWPr5Dlds7k7x9W9BV7Ywsl/+efegH/2Jc/BLaerdbwbkyLdQQ/UhZPduk7mjRbv3qJ8p9Cokw/aIPmwgORnLZ1w08lBag7VTr5RXdMCqVYh+VkLzryNPf/8Ou7brHDybTn5mvmfI7omh+T35nadxX//imdr1EY2bpFYFELgOWeLMkpqQ0vjKCfTna55Vo5OedOHHsEHH3gS8ziF76k6Bd9TJ6T1SbwCKpAUg4gBOlrnX9U7qOb6v/DGE3j46r4GZmZ8E6SqzKiP6ud/vqIgio5fFEJgbRQUJKxt1DWAQvOfvZLn5J8hTp4cAHfyn7y4AwCtnPyFkyv4yW/5/MLiyX9G3sHc/fgWbj45sVqI9rHTWW+afEXk++/bhBBKOVFnbVq5nlgJCkFq2nKBtFEvUKDgBVFtdyJhC86/MvEaHTzxCignv7kzL+R12uQs2iDtE5MA48ArIPk2OYumQ2HInnNmTTv5d9xzCZ+9sqckpozOWCmR2LXpMnrhpFG/JKnEv3/rPfjl996fHT1nxpuEXi9OHlC8/ONb5XTNCqODCnUELSSgL7qwASmBT1/esV5vg+Tb1IkA5QVRVMxFbEFZsVKbk6cA4Hnnypy86TLaxY6dkw98VdLOH9BPXVI380U3lR8U0dVOlFRc3v3YFl7yrJMHHpuc/LV9U7E4XST47Q8+jK/9vBu1vKzKnndu/f9v78yD46jOBP775tJ9WpYty5ZvW9jybYxNCBCcQAIshKQoyBIqCWw2F5XN1tayCZvUkmXZpNiQoza1SdiEbG6SJckmhLBATpIsTsCY+MLGBzZIPnRYknVrJL39o/vNtEaj6Z5RjzQjv1/VlGa6p998PZr++nvf9VhUXRTzyyaj3I4pODNIvCphT62GY8FLa0ylFJ97+mVbvtQ3qYjHmQhMrNod9OBO8cKKuVbue6LLxlt2kLslrPOodeB198lzdPYN0zc8Oi7rIhlu/eQ1DXOKae7s50R7H+//9m4efOpwzFWguWRp9YTFU2LZNSnkLykIUVkc5lTXAMfbehmIjnKgpdsu2Y+PXxQJTrBUvVJfWUxXf3Scte1c72Cytgxe6gi021brBU3ckp/8f+zVXTPPdsk5C6ISZ8slkVCsq61mXL/6FJ+xZE4JHX3D4/RcppZ8zi0a4oWKhBTBQ2d6mFtW4Fpt6ZVES75vaITj7X3csKF+ymNX2kVeTkv+x3ta6OqPcudlS12Pr68s4nd3X5XyPXp91d7BkVhRmVd3ipfpfMwnb/sIH3zqZX60p4W/e9MqtiyuTjl+rNVwih/4ZAuHDNi9tKfKilpLyR9r7WXL4ngTO09tJVx6H2lqywppPT/E+cEot3xlFytqS+nsG/ZQrOduqQI0VBcTHVV86omXGFOw6/g5Ni6qGKeEv/6ebROO89KgDGBBhZVGuf9UNwAdfcOc7OgfZ0Uuri5hYQq3Z8rxbZfQ6e4BVtRaStm5/KLCMlAmFou5t31YVFVMcSTIoTOJlrw1fmpL3tv3X2wXtDkLomJrEMfaDoQYTci+8ZJdA/Hg64n2PhrryhgYHo2nUKbblC+td+cI5UXh8Zb82fOegq5eKSsc7+44ePo8SsG6hVOfKWgrpdPuPaKU4uE/vEJTfTnblqZWkF4pd1S9aiWvFaZbMZeXFE2t5AeGx3jtXD9f/PVRbt6ykLuuSlobNw4v7prJFg4Zio75Yskvqi4mEgqM88vrimNXd40deHWbzteWFfDy2R6ee+UcI2MqpnDc6ji8KDGIp1E+eeAsFUVh2nuHOHDqfMyNMBleKprB8pk3d/azrzneU3DPq13MLYu7K7/z3ktcv4fJiBdEDdJQXUIoIA4lGUChffLJA6+plHAgIKycV5bCkk+h5D3MFDTz7TTKxPH1jbA4EoydU+L4MPlC3uDMsOnl008c4lhbL1evnUfIjomkQ965a2B8iuDomOLI2V6flXyYwehYrJPd/hbLmmlaMHV3DVi9L7ps+V94tZOjrb2859KlKdPC0iFZcFrn3C5J4eYB762GwbK89I/8ho0LPMnv5SKFibOpkdExhkennl0D1rktqynhiMNn295nuVZKXeogvPjkwa567Rni2WMdREIB7r+pCYjPstzGd7XkHf9HPfbp7sEJC9cn4mWmBlbwtaVrgP2nummcX0ZArApkp4IMB1OvMpYKncGzv6WbN3/hGe7+4V6GoqOIJBRbTdqKOfX4jfPKOHTm/Lg89iEfffIAdZVF7G3upst2vSa6U6pLIhNu6vE1glOngTbMKUYEvvLb4zx7vIPWniF+8Hxz2q4ayFMlX+Gw5E909DE0MsZqn/zxMDG7Y3/LeeaWFUzIqc2UiuJI7Iehg2ebGip9GRviisQ5GznS2kMkFJiwyEEi2tJLXfEad9dot1NlkTdXmZeKV5i4cEh8wRB/frKJGTaP/Ok1AK5qnJfyOK951LXlhfQMjvDrw61sbqjktksW8733bufdly5JeVzc55xa/rqKIiLBABcvqeK6dXUssC14t+8n3tYg9fgLKovoGRzhz691sW1pNcvtOMZU6xQ0tWUFBAPCg08d5nhbH7861Eqfo0FcLDsoNIkl7/L7WT2/jM7+6LhuoJ4s+YC3mwjAXW9YQUfvMO/95vMMRuPuFH193HPtRTx4c/JiKzf5C0JBFlYVcehMD0315dyydRHDduZOuuStktcKLB509deSh3h2x4FT3TQt8O8mUlUcptNWjvpH6JwGT5V4J8q4JXy0tZdlNSWuyiniodinMJZCORYLIFdO0lBuwvge8uRh4sIh/XaALhNLJhkraktp7hxgMDrK0Mgo33z2JFeunhvz10+GV0te/z+PtfWxfdkcAHYsn+MaN3KryNYEA8Lnb93Ip962HhGJfYb3imZ3dw1YVa5NCypospMO0vUHp5JjfnkhCrhuXR3n+obZ19ztqHS1soMmLKriUf7GOksfOP3yOrsm1TkEAkIo4L4QB8C2pdV89pYNPHeik08+dmBcdhBYbkEdb9AEA0JA3GdSYAVfAT55QxN3v3m1nbWV/u8/L33yTkteW8JLalJndaSD05IfjI5ypLWXN61JbeGlQ1VxhJfOWL7Otp4hCsMB16yLdND9fZyW/NG2XjYsdJ8teLHkCxzumjE7g2eyrqGJeFWSicU+j+87DcB6D+fghRW1pSgFx9p6eel0D+29Q9zxOvfAt5c8bYhXvQLssBWwFyIeLT2wlrjUbF8+hx/tafHQZdSbO6LeEVBdW1/O+cEoP97T4puSB7jrqhWUFITYtKiSx/edZvernbGl9cBSlonFVl5nUjrT7vCZHi5fZa05kFjxOhnhYMBzrOH69Qt4+uBZfvlSK29psv4fbtZ2JBRAcB//r16/jJ2NtbHkgM/fupHTSTpfupGXSr68KEz/8CjR0THOdA9SVhjyVUk6Fw556fR5RscUa33yx4Nl9XY5LPma0gLf/PHgcNfYN8LB6CjNnQO8ffNC12NX1JZyzdp5bFw0uTLVU+mh6Cj9wyOxzpde8FLxCuMXDhkdU/zX/51gc0NlSrnSQVvsn3v6CC+f7WHVvFJev7LG9bgCHXj1kF0D1ne1MQ1XnC40Sjf9fEfMkvfornH1yVtKPhIMsLK2LDar8msmBfCObQ2x5wurimjuHBgnf2E4OCHwKiJEQu5KuLokwtyyAp47cY47L1tKICCx3jturRjCQW+WvOaiunJ+8uKp2Kzcy+prKnkn5HFcsWouV6yKL4rj5kqcjLx114ClxE51DUy5CjUR58IhuqDiojr/3EFVtk9+bEzR1jvkq6sGoDQSQiSu5I+19aIUrq4IsLI/vnL71pRZGtpnOjgyRld/lIqisOeblNfAljPw+suXznKyo587L1vm6TO8sKymlNXzyth1vIOu/mE+vHOlp3OI9a5xs+TtPOoti6vSnmK/YfVcNqR5M1tUXczOxtqk6xo7cVvIWzO3tIBwUFg9v4xIKMAa213ppyXvJJm7qSiSvF96gceA79Vr5vHUwbP8xRd/z/6W7tgaqW7nEAmlF1DWdRcHTp23z8Fl/KBlJE0XeWnJO7NHzpwfZH5FZrm6k+F017x6rp9QQFyLlNKhsjjMmIKeoRHae4ZTFjZlQsC2rHUxkS768aLkvVIYttZ57RqITmjXmgqv7prywhC9wyOMjSm+9vtXqK8s4pq1/rnMIqEAT/7t5WkfVxgKUl+ZuhgNoLo4Qn1lEW9uSrY8cmq++q6L0z4G4Gvvdj9OxPY5u2iZQEBYv7CS7custN7ywjBbFlexzEe3qJMdy+bw6O7mcdlTK2tLx/Wb10RCAVf5Af7lrU1sW1rNfT87yCd+sp+r18yPHZ+KcJpKWF9XOgvPiyWfmD+fTfJeyZ/uHmRNnX9BURgfeD3Z0U99VVHauampqIy1NhimrXeIrUtSW1+ZYPWUty351l4CwoQ+KVNBL+bd3R/17I+HNAKvhWGUgj2vdfHHV85xz7WNvv4PMiUQEP7w0dTFaPp9v/+HN0yDROkT9BhYfPT9Oya89tOt6GT78omW/JfeuSXpeyMhb0pYRLhxYz37W7r5xrMnuWyF5Y5za6oWDnq7iWh03cWBUx6VfEiIjk6fKT/zV00G6MBie+8w7b1DrgUg6ZJoyTdU+2tpV9lKsa3Haqzmt7sGxmcgHW3rpaG62JdqUU1hOMBgdIyugeG0LHl9A3WLoej/wbd3nSQgcNMm93hCriEiWVOKU8FrYDFR/myeS31lEctqSjwZDOm6U5rqKxgeGePgqfOeionS9cnruou+YW9tB8LBgKfsGr/Ia0v+SGsPSk29M2Qi4aC1KIK25K9fX+d+UBpoS167UbKh5K1OlHF3jZ+uGnC4a/qjrKz1Hq+YX1HIjz94aSwlbzL0zeDxfad53YqarHxHFyqZBHang6+/52JPhkgkzSIsnTTxwqudnvrfp5Ndo1leWxpL13RrIBZJc6YwVab0rxaRe0WkxV7E+0URudax72MiclREDovINVMXNY7OHtE58n775MGyJFu6BugeiPruM9eW/BFbyddMsbNlMsqLrCZuI6NjvNLex3KflXxBOMjgyBjd/VHPOfKaTQ1Vrt0L9WxteGSMGzdOvWeQIY5VqZp7Wn7xnBJPs/KCcHpKfmlNCcWRIJ39UU+B43RnChAPvnqZKVjZQWkNPyX8sOQ/p5T6jHODiKwBbgXWAguAX4jIKqXUxCXgM0Bnv2glv8BnSx4sJa+j5Q3V/gabdCfKI9m25AejnDzXT3RUxX6EflEYCtA3NELP0Ijnatd00JZ8QSjga8DVAPdc25jW7CvX+MjOVWkt3hMMCGsXlPPcCe+WfLLlA1OhZ8pe2m5Mt7smW7fzG4FHlFJDSqlXgKPAxJZ4GVIYDlIQCnDMLkv32ycPlpLRxTh+++TLi8KIEOudMjcrlrwVeP31oVYANi/2N7hbGA7GVsVJ15L3gvbJv/GieTGFb/CHt21eyLqF/tV9TDdvXDOPS1e41zQ40S4bL+6gcFDSVsJayXtp+5Cuz3+q+KHk7xKRvSLysIhoTVIPvOZ4T7O9bQIi8tci8ryIPN/W1ub5QyuKwkRHFaUFoawoAa1kYHwzKD8IBoTywnCsei1blnzf8Cg/eqGFpvryWO8RvygMB2IrH2VDyddXFnHxkiruuGyJ72MbLjx0DMiLJX/FqtpxRUheWFpTQkC89VZKN3tnqri6a0TkF0CyZN9/BL4E3Aco+++DwB2QtGY3aWKoUuoh4CGArVu3ek4erSgK09rjf2aNRruEakojvlbTaqqKrdYMZYUhX6sINdqnffD0eT5+3UW+j18YDsZ6gVSkkV2Tzvj//f5LfR/XcGHSVO+9mOsDVy5Pe/zCcJBF1cWeVsoqCOVYdo1S6o1eBhKR/wR+Zr9sBpwrFC8ETqUtXQq0YvE7s0ajLXm/XTWayuIIdPRnxVUD8ZuUiNVfw2+cLW11tpDBkKusmFtKQSjgyZLPlC0NVXT0Dbu+r66iaEKf+WwyJRNVROqUUqftlzcB++3nPwW+KyKfxQq8rgT+NJXPSqQ8z5W8zrCpyVJqoP5+ti+dk5XZjnNamk6evMEwE4SCATYsrKS0MHtZ4//6tnWe3veJ69cwlkcVrw+IyEYsV8wJ4H0ASqkDIvID4CAwAnzIr8wajbbks5E+CfHsjgaXhbUzRWfYZCv/W7e0vXGj/1Y8QGHEackbJW/Ifb74l5uSO5J9wqvbNZuziWRMSckrpW5Pse9+4P6pjJ8KreSzkT4JcUt+cTbdNWQnswZg06JK/uO2zVztY4tkJ9pdI4LJfjHkBX4t+pNv5GXFK8TdEdkKvMYt+WwpeWv8bFnygYCM6zfuN9pqKS8MT2s6mMFgSI/cK3vziF6sui5L7prLV9Zw+/bFrM9SPrH2yWfLks822idvXDUGQ26Tt5b8juVz2NlYy5Ka7FjateWF3PfWpqyMDQ53TZ72ZNGWvAm6Ggy5Td4q+bULKjz1z85VmuoraKgu5iKf2yRPF3FL3qRPGgy5TN4q+XxnaU0Jz9ydm/3GvaADr8ZdYzDkNnnrkzfMLMZdYzDkB0bJGzKiwHbXVBh3jcGQ0xglb8gIY8kbDPmBUfKGjNA++Ww0JzMYDP5hlLwhI1bNK+V9VyzjqsbamRbFYDCkwGTXGDIiFAzwsbf438LYYDD4i7HkDQaDYRZjlLzBYDDMYoySNxgMhlmMUfIGg8EwizFK3mAwGGYxRskbDAbDLMYoeYPBYJjFGCVvMBgMsxhR07hquBsi0g0cyeJH1ADtWRy/AujO4vjZlh/y/xyM/Kkx8qcmX+VfrJSam3SPUipnHsBDWR7/eSP/7D4HI7+R/0KWP9kj19w1j820AFMk3+WH/D8HI//MYuTPMXJKySul8voLznf5If/Pwcg/sxj5c4+cUvLTwEMzLcAUyXf5If/Pwcg/sxj50ySnAq8Gg8Fg8JcLzZI3GAyGCwqj5A0Gg2EWk/dKXkQeFpFWEdnv2LZBRJ4VkX0i8piIlDv2rbf3HbD3F9rb/1dE/mxv/7KIBPNM/t+IyGERedF+TMuSTX7ILyJlDrlfFJF2Efl8vshvb79FRPba2x+YDtnTlV9Ebkv4nsdEZKO9734ReU1EeqdLdp/lz/nr10X+7F2/052zmYW808uBzcB+x7bngCvs53cA99nPQ8BeYIP9eg4QtJ+X238F+CFwa57J/xtga75+/wlj7gYuzxf57b+vAnPt7d8Aduaa/AnHrQOOO15vB+qA3lz9/bjIn/PXr4v8Wbt+896SV0o9A5xL2LwaeMZ+/jTwdvv51cBepdSf7WM7lFKj9vPz9ntCQASYloi0X/LPFH7LLyIrgVrgd1kT2oFP8i8DXlZKtdnv+4XjmKySpvxO3gF8zzHOLqXU6awImQIf5c+H69fJOPmzSd4r+UnYD9xgP78ZWGQ/XwUoEXlSRF4QkbudB4nIk0Ar0AM8Ol3CJiEj+YGv21O9T4iITJewSchUfrB+/N9XtnkzQ6Qr/1GgUUSWiEgIeKvjmJlgMvmd3MI0KZkMyEj+PLh+nST7/rNy/c5WJX8H8CER2Q2UAcP29hBwGXCb/fcmEdmpD1JKXYM1ZS0ArppWiceTify3KaXWAa+3H7dPr8jjyOj7t7mVmVc+acmvlOoEPgB8H2sGcgIYmW6hHUwmPwAicgnQr5Tan+zgHCAj+fPg+gUmlT9r1++sVPJKqUNKqauVUluwFMYxe1cz8FulVLtSqh/4OZY/zXnsIPBT4MbplDlBhrTlV0q12H97gO8C26ZfcotMv38R2QCElFK7p11oBxl+/48ppS5RSu0ADpPdRnspSSG/JhdupJMyFflz/PrVTJA/m9fvrFTyOjItIgHg48CX7V1PAutFpNieVl8BHBSRUhGps48JAdcCh6ZfcosM5A+JSI19TBi4HmvKOCOkK7/j0GnzU6YiE/kdx1QBHwS+Ot1ya1LIr7fdDDwyM9K5k678eXT9TiZ/dq/fbEefs/3AUgqngSiWpXUn8DfAy/bj09iVvfb73wkcsL/EB+xt87Ai4nvtff+OZVHmi/wlWBkpWv4vkCRrJVfld+w7DjTm2+/HMc5B+zEtmR0Zyn8lsCvJOA/Yx4/Zf+/NF/nz7PpNJn9Wr1/T1sBgMBhmMbPSXWMwGAwGC6PkDQaDYRZjlLzBYDDMYoySNxgMhlmMUfIGg8EwizFK3nBBISJKRL7leB0SkTYR+VmG41WKyAcdr6/MdCyDIRsYJW+40OgDmkSkyH79JqBlCuNVYhU/GQw5iVHyhguRJ4Dr7OfjqmxFpFpE/kes3vC7RGS9vf1eu3f4b0TkuIh82D7k08Byu7HUv9nbSkXkURE5JCLfmeFmcYYLHKPkDRcijwC3irXgx3rgj459nwT2KKXWA/cA33TsawSuweor8k92CfpHgWNKqY1Kqb+337cJ+AiwBqsN8euyeTIGQ1Cntu0AAADvSURBVCqMkjdccCil9gJLsKz4nyfsvgz4lv2+XwFzRKTC3ve4UmpIKdWO1dJ23iQf8SelVLNSagx40f4sg2FGCM20AAbDDPFT4DNYvUTmOLYnc63o3h9Djm2jTH79eH2fwZB1jCVvuFB5GPhnpdS+hO3PYPWLR0SuBNpVfNWhZPRg9Qw3GHISY2EYLkiUUs1Y3f4SuRdrhZ69QD/wLpdxOkTkD2It5PwE8LjfshoMU8F0oTQYDIZZjHHXGAwGwyzGKHmDwWCYxRglbzAYDLMYo+QNBoNhFmOUvMFgMMxijJI3GAyGWYxR8gaDwTCL+X9a+HDjcspErAAAAABJRU5ErkJggg==\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"df['Milk First Difference'].plot()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Seconde différence**"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {},
"outputs": [],
"source": [
"# Il sera parfois nécessaire de faire une deuxième différence \n",
"# C'est juste pour faire le show, on n'avait pas besoin de faire une seconde différence dans notre cas\n",
"df['Milk Second Difference'] = df['Milk First Difference'] - df['Milk First Difference'].shift(1)"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Augmented Dickey-Fuller test (DAF):\n",
"ADF Test Statistic : -14.327873645603301\n",
"p-value : 1.1126989332084581e-26\n",
"#Lags Used : 11\n",
"Number of Observations Used : 154\n",
"Preuves solides contre l'hypothèse nulle, rejeter l'hypothèse nulle. Les données n'ont pas de racine unitaire et sont stationnaires\n"
]
}
],
"source": [
"adf_check(df['Milk Second Difference'].dropna())"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x1c1f3b1390>"
]
},
"execution_count": 27,
"metadata": {},
"output_type": "execute_result"
},
{
"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": [
"df['Milk Second Difference'].plot()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Différence Saisonnière**"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x1c215f8450>"
]
},
"execution_count": 28,
"metadata": {},
"output_type": "execute_result"
},
{
"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": [
"df['Seasonal Difference'] = df['Milk in pounds per cow'] - df['Milk in pounds per cow'].shift(12)\n",
"df['Seasonal Difference'].plot()"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Augmented Dickey-Fuller test (DAF):\n",
"ADF Test Statistic : -2.335419314359398\n",
"p-value : 0.1607988052771135\n",
"#Lags Used : 12\n",
"Number of Observations Used : 143\n",
"Faible preuve contre l'hypothèse nulle, la série temporelle a une racine unitaire, ce qui indique qu'elle est non stationnaire \n"
]
}
],
"source": [
"# Différence Saisonnière non suffisante !\n",
"adf_check(df['Seasonal Difference'].dropna())"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Première différence saisoniière**"
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x1c21713ad0>"
]
},
"execution_count": 30,
"metadata": {},
"output_type": "execute_result"
},
{
"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": [
"# You can also do seasonal first difference\n",
"df['Seasonal First Difference'] = df['Milk First Difference'] - df['Milk First Difference'].shift(12)\n",
"df['Seasonal First Difference'].plot()"
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Augmented Dickey-Fuller test (DAF):\n",
"ADF Test Statistic : -5.038002274921984\n",
"p-value : 1.8654234318788342e-05\n",
"#Lags Used : 11\n",
"Number of Observations Used : 143\n",
"Preuves solides contre l'hypothèse nulle, rejeter l'hypothèse nulle. Les données n'ont pas de racine unitaire et sont stationnaires\n"
]
}
],
"source": [
"adf_check(df['Seasonal First Difference'].dropna())"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Graphiques d'Autocorrélation et d'Autocorrélation Partielle\n",
"\n",
"Un tracé d'autocorrélation (aussi connu sous le nom de [Corrélogramme](https://en.wikipedia.org/wiki/Correlogram) ) montre la corrélation de la série avec elle-même, décalée de x unités de temps. L'axe des y est donc la corrélation et l'axe des x est le nombre d'unités de temps de retard.\n",
"\n",
"Imaginez donc de prendre votre série temporelle de longueur T, de la copier, et d'effacer la première observation de la copie #1 et la dernière observation de la copie #2. Vous avez maintenant deux séries de longueur T-1 pour lesquelles vous calculez un coefficient de corrélation. C'est la valeur de l'axe vertical à x=1 dans vos tracés. Il représente la corrélation de la série décalée d'une unité de temps. Vous continuez et faites cela pour tous les décalages possibles x et cela définit le tracé.\n",
"\n",
"Vous exécuterez ces tracés sur vos données différentielles/stationnaires. Il y a beaucoup d'informations intéressantes pour identifier et interpréter ACF (Auto-Correlation Function) et PACF (Partial Auto-Correlation Function) [ici](http://people.duke.edu/~rnau/arimrule.htm) and [ici](https://people.duke.edu/~rnau/411arim3.htm).\n",
"\n",
"### Interprétation de l'autocorrélation\n",
"\n",
"L'interprétation réelle et son rapport avec les modèles ARIMA peuvent devenir un peu compliqués, mais il existe quelques méthodes de base communes que nous pouvons utiliser pour le modèle ARIMA. Notre principale priorité ici est d'essayer de déterminer si nous utiliserons les composantes AR ou MA pour le modèle ARIMA (ou les deux !) ainsi que le nombre de décalages que nous devrions utiliser. En général, vous utiliserez soit AR (Auto-Regressive) soit MA (Moving average), l'utilisation des deux est moins courante.\n",
"\n",
"* Si le graphique d'autocorrélation montre une autocorrélation positive au premier retard (retard-1), alors il suggère d'utiliser les termes AR en relation avec le retard\n",
"\n",
"* Si le graphique d'autocorrélation montre une autocorrélation négative au premier retard, il suggère alors d'utiliser les termes MA."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"_____\n",
"### <font color='red'> Remarque Importante! </font> \n",
"\n",
"Ici, nous montrerons le fonctionnement de l'ACF et du PACF sur de multiples ensembles de données différentielles qui ont été rendus stationnaires de différentes façons. En général, il suffit de choisir un seul ensemble de données stationnaires et de continuer jusqu'au bout.\n",
"\n",
"La raison pour laquelle nous en utilisons deux ici est de vous montrer les deux types de comportement typiques que vous observeriez en utilisant l'ACF.\n",
"_____"
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {},
"outputs": [],
"source": [
"from statsmodels.graphics.tsaplots import plot_acf,plot_pacf"
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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Ucosl8M3sSuC/AWngy+5+Z8n0DwM3ATngIPBv3f3ZONqulXTKmD+0h/lDe1S3nwVB6Cf7WhnLBWRzp3ZZW66r2sUL2pjbmmIk+3JIzmlNMb8tzVPPHT7ZU2JcV6mWfrnEbc2SBbS1pBgrCv22lhRrliyY1XZlahqt5FZx4JtZGrgLuAIYAB43s63u/kzRbE8B3e4+bGZ/APwZ8DuVti2Ny90Zy4WMZUNGcwFjUbCfDPgZ9pJ43pIFnL+0nR3PvQjpFua0tnDB0nZeeXYHo9nGu+ipq3MRFy4b/3ouXNZOV+eiWq+aML7kBuNLbvX4H1gce/iXAH3uvgfAzB4ArgFOBr67/6ho/keB34uhXamRMHSOjmZ56USGY6O5CXtDLFXYCw89XwqZjQuRUinjtqvW854P3kLQvpyb37ulrv/FPp1mez3NptFKbnEE/iqgv2h4ANg4yfybge/F0K5UUSYXMjSc4fBwliMj2bruOTGVMtoO9cGhPi4+9yO1Xp2KNdvraSaNVnKLI/DL7WqUTQMz+z2gG3jDBNO3AFsAVq9eHcOqSSVOjOU4PJxhaDjLsVFdQCRSqtFKbnEE/gDQWTR8DrCvdCYzuxz4GPAGdx8r90TufjdwN0B3d3f97kI2qUKp5vBwlsPDGcYasOYtUk2NVnKLI/AfB9aa2XnA88B1wDuKZzCzVwNfAq50d/U/ELMwdIazAcOZHCOZgOFM/vZwL9fIX/7unOymzOpjXWT6GqnkVnHgu3vOzG4GHiZ/WuY97r7DzG4Hetx9K/BfgHbgr8wM4Dl3v7rStpOmcGbLibHcyVAfju7/qZ4YReR0YjkP3923AdtKxn2i6PHlcbTTqLJBmP/J5W8X554/o9w9f1u6/OOXL/gpXPxTGB+6M5LJ3+S5ng+Wikh9S9yVtjM9HdDLHIcuBPPhExmyQUgmCMnk8jd3Lgxnc6HKJCJSFxIX+E8/fyS2C3COR2eu/NMLx2J5PhGR2aTO00REEkKBLyKSEAp8EZGEUOCLiCSEAl9EJCEU+CIiCaHAFxFJCAW+iEhCKPBFRBJCgS8ikhAKfBGRhFDgi4gkROI6TxMpJwyd3v4h9h46wZolC2btrkXVakfqW622AwU++hAmXRg6d3xvJ32Dx8nkQtpaUly4rJ3brlof63ZQrXakvtVyO0h84OtDKL39Q/QNHmcsl+82eywX0jd4nN7+IS4+98yGa0fqWy23g8TX8IvffGf8my/JsPfQCTK58fdIyORC9h460ZDtSH2r5XYQS+Cb2ZVmtsvM+szs1jLT55jZg9H0x8xsTRztxkEfQlmzZAFtLeM/Cm0tKdYsWdCQ7Uh9q+V2UHHgm1kauAu4CtgAXG9mG0pm2wwcdvcLgc8C/7nSduOiD6F0dS7iwmXtkMuAh8yJynpdnYsash2pb7XcDsxncoPX4icwuxT4lLu/KRr+KIC7/2nRPA9H8/zUzFqAF4ClPknji89d71fcds+M1qn3Z70AdL2q65Rpx8dyhEXNujvPvTTC8FgWMCxlzGtNs3rxPMwmr+HvfuZpANZu+LUZrafMnun+bdydX/btgXQbK1euoH1O+rR//3puR6or7u2gY27rjNfloff+iyfcvbvctDgO2q4C+ouGB4CNE83j7jkzOwIsAV4snsnMtgBbANpXXDDjFSoX9BMxM1YvnsfxsTbGsgFzWtNT/hDO5MM33Q1jJh90LTP9v42ZsW7t9Le5em0H6vvv02zLVGs7qFQce/hvB97k7jdFw+8ELnH3DxTNsyOaZyAa/lU0z6GJnre7u9t7enoqWrdynnrucGw3MZ+J97/jagDu+sbWWZm/WsuEofOeD95C0L6cm9+7Zcqnslbr9Uj9bjszWSZp29vG8xbP+CxBM5vVPfwBoLNo+Bxg3wTzDEQlnTOAl2JoW2qgcCrr8Q1vgXQLn//hbp3KKrNG21t84jhL53FgrZmdZ2ZtwHVA6VfjVuCG6PHbgB9OVr+X+lY4lZWWNrCUTmWVWaXtLT4VB76754CbgYeBncBD7r7DzG43s6uj2b4CLDGzPuDDwCmnbkrj0KmsUhCGTmbJhYyc+1qefPYwYRj/fpy2t/jEcqWtu28DtpWM+0TR41Hg7XG0JZMrfACD9uU8+ezhWekmonAq61jRh1CnsiZPtUot2t7ik/grbZtJ8Qdw5LzX8fkf7uaO7+2Mfa+rcB7xnJYUBjqfPKGqVWrR9hafxPel00zGfQCZvT46UinjtqvWq8O5hJus1NKo21s1/kOuJQV+E6nWBxDyH8KLzz1z1jt7avYPYCOrZqmlGttbEs4GUkmniTRbNxHVKlHJzDRbqSUJZwNpD7+JFD6ApV09N8UHEHUnPB3V+M+o2Up71fwPuVYU+E1EH0CB6pYmqlXaq4YknA2kkk4VVeOc5cIH8K0Xn8PF557ZsGEPzVeiqpYklCZmQ7OVqMrRHn6VJOGAUNyarURVLfrPaGaa7T/kchT4VaJ69PQl4QM4G5JQmpgtzVSiKkclnSrR5eEz00wlqmpJQmlCZkZ7+FWivS6pFv1nJBNR4FeJ6tFSTc1empCZUeBXifa6RKTWEhf4c1undvvCqXB3cqGTC6Z2eqX2ukSklhIX+OtXdMT+nGHoZIKQTBCSzYVkAyeTi4ajn0w0XkSkVhIX+LMhlTLmptLMbU1POp97/ovBnfwPHv2G0POPKRofujOvLf+cqxbN40Qmx3AmOOVsHxGRqVDgV5GZMadl8i+FUq3p/Jmzq5fMPzkuF4QMZwOGxwKGoy+BkWww5dKSiCRTRYFvZouBB4E1wF7gWnc/XDJPF/BFoAMIgE+7+4OVtJt0LekUHekUHXNbx40fzQaMZAKGswGj2YDS2wYXD5Z+NbSkUzhOykCdUYo0p0r38G8FHnH3O83s1mj4IyXzDAPvcvfdZrYSeMLMHnZ3dewRs7mt+bLSTA4Jz49KR91rFjM0nOHwcJah4YyOO4g0kUoD/xpgU/T4PmA7JYHv7r8serzPzAaBpYACvw6lU8aS9jksaZ+Du3N8LMfhE1kOD2cYzgS1Xj0RqUClgb/c3fcDuPt+M1s22cxmdgnQBvxqgulbgC0Aq1evrnDVpFJmxsK5rSyc28rqJfMZzQYMDWd56USGY6NZlX5EGsxpA9/MfgCcXWbSx6bTkJmtAO4HbnD3sqeZuPvdwN0A3d3dipM6M7c1zdlnpDn7jLnkgpAjI1mOjebGHQ8oPW5Qqq0lhQMd81oYzeZPWT3NIiISk9MGvrtfPtE0MztgZiuivfsVwOAE83UAfwd83N0fnfHaSt1oSadOln6mo3Dq6kUrzwBevoZhLBsymgsYy4aM5QJGo986hiASn0pLOluBG4A7o9/fKZ3BzNqAbwFfdfe/qrA9aTLF1zCcQesp04PQGc0GZIP8P4WF/wYKXwPF/1G8PO7laYHnr4QOo6uig5KfXBhN0xeLJEClgX8n8JCZbQaeA94OYGbdwHvd/SbgWuD1wBIzuzFa7kZ3762wbUmAdMpYMKc6l4vkgnBKxyW85KTWoeEse188oWMaUvcq+iS5+yHgsjLje4CbosdfA75WSTsi1dCSntntIZZ3pFk4t4XdB47rTCapa7oBikgM5re18OurzmB5x/SOaYhUkwJfJCaplHH+0nZesbydlrS6vZb6o8AXidmS9jn8+qozWDhXXVVJfVHgi8yCua1pLlrZwTlnziOm2y+IVEyBLzJLzIzOxfNZv6KDthZ91KT2tBWKzLIz5rXyG+ecwZkLTr3OQKSaFPgiVdCaTvHKsztYc9Z8dBtjqRUdVRKpohVnzKNjbisHj41Ne9nQPfrh5JXCxcOh58epb6K8MHQySy4kaF/Ok88epqtzEamEf9sq8EWqbMGcllm9ejgshL97UTcT0e/oKuFyXVTkQid19joyC5ax78gIl6xZjDtkw5Bc4Cfvz5wL678rijB07vjeTo5veAukW/j8D3dz4bJ2brtqfaJDX4Ev0mRSKSOFTevDHYTOO7/yGIde8dt4qoU//vbTdHUu4v7NG0mXCcgw9JNfBIW+iopN9HVQ6PtoXlsad1jeMYeR6A5tmVx8XyK9/UP0DR6HljYAxnIhfYPH6e0f4uJzZ3KLoOagwBcRtu8apLd/CE/nA3I4E9DbP8T2XYNctn75KfOnUsacVJqZ/qNSuFfz+UvbT47LBSEj2fz9mUczRY+zwbTLVHsPnSCTG98LeyYXsvfQCQW+SDUEoTO86HwyC5bzyM4DbFq3rOzeo1Tfjn1HGSnpB2gkE/DMvqNlA382tKRTLEynWFhyr2Z3ZzSb/wIIpthD3aUXLOG7P9/PSPbl1zS3NcWrOhdx5oLW/H8moZOLSlRJOe6hwK9jzRSQhZLBwbX5ksEHvvnUpCUDqa6LVnYwry09rvO3eW1pNqzsqOFa5ZkZ89rSzIvuuzwV13St4n8+MUBv/xAjmYB5bWm6Ohdx/SWry25vheAvfAmUfgFMdpOf4m65nfyXR/HjsORYSuj5IykeHXCnsAwvzztbF+sp8OtUswXkdEsGUl2b1i2jq3PRKQG5ad2kdy2tW+mUcf/mjWzfNcgz+46yYWXHpDtMLekULVP/PmlYCvw61WwBWQ8lA5nYdAOyEaRTxmXrl2v7KqLAr1PNFpD1XDKQPAVk89OVtnWqEJDFGjkgCyWD+W1pDJjf4CUDkUZU0R6+mS0GHgTWAHuBa9398ATzdgA7gW+5+82VtJsESa+pikj8Ki3p3Ao84u53mtmt0fBHJpj3T4D/VWF7idGMAamSgUhtVRr41wCbosf3AdspE/hm9s+A5cDfA90VtpkYCkgRiVOlNfzl7r4fIPp9Sr3BzFLAZ4A/qrAtERGpwGn38M3sB8DZZSZ9bIptvA/Y5u79dpqrCcxsC7AFYPXq1VN8ehERmYrTBr67Xz7RNDM7YGYr3H2/ma0ABsvMdinwOjN7H9AOtJnZcXe/tUxbdwN3A3R3dyfkYmcRkeqotIa/FbgBuDP6/Z3SGdz9dwuPzexGoLtc2IuIyOyqtIZ/J3CFme0GroiGMbNuM/typSsnIiLxqWgP390PAZeVGd8D3FRm/L3AvZW0KSIiM6MrbUWkqgq9wA6tupRHdh6YcpfHUjn1pSMiVdNsvcA2Gu3hi0jVjOsF1lLjeoGV2afAF5GqmawXWJl9CnwRqZpm6wW20SjwRQfRpGrUTXZtWen9GetFd3e39/T01Ho1ml7hINpPf7kfT7Uwf06rDqLJrApCb6peYOuNmT3h7mU7qdRZOgnXbLdSlPqnXmBrRyWdhNNBNJHkUOAnnA6iiSSHAj/hdBBNJDlUw0+4ZryVooiUp8AXHUQTSQiVdEREEkKBLyKSEAp8EZGEUOCLiCSEAl9EJCHqti8dMzsIPFvBU5wFvBjT6jSipL9+0HsAeg8gee/Bue6+tNyEug38SplZz0QdCCVB0l8/6D0AvQeg96CYSjoiIgmhwBcRSYhmDvy7a70CNZb01w96D0DvAeg9OKlpa/giIjJeM+/hi4hIEQW+iEhCNF3gm9mVZrbLzPrM7NZar08tmNleM/uFmfWaWSJuDGxm95jZoJk9XTRusZn9g5ntjn6fWct1nG0TvAefMrPno22h18zeXMt1nE1m1mlmPzKznWa2w8w+GI1P1HYwmaYKfDNLA3cBVwEbgOvNbENt16pm3ujuXQk6//he4MqScbcCj7j7WuCRaLiZ3cup7wHAZ6Ntocvdt1V5naopB9zi7uuB1wDvjz7/SdsOJtRUgQ9cAvS5+x53zwAPANfUeJ2kCtz9x8BLJaOvAe6LHt8HvKWqK1VlE7wHieHu+939yejxMWAnsIqEbQeTabbAXwX0Fw0PROOSxoHvm9kTZral1itTQ8vdfT/kwwBI6n0bbzazn0cln0SUM8xsDfBq4DG0HZzUbIFf7r58STzv9LXufjH50tb7zez1tV4hqZkvAhcAXcB+4DO1XZ3ZZ2btwF8DH3L3o7Ven3rSbIE/AHQWDZ8D7KvRutSMu++Lfg8C3yJf6kqiA2a2AiD6PVjj9ak6dz/g7oG7h8Bf0OTbgpm1kg/7r7v730SjE78dFDRb4D8OrDWz88ysDbgO2FrjdaoqM1tgZgsLj4F/BTw9+VJNaytwQ/T4Br3EVpoAAACySURBVOA7NVyXmigEXeTf0MTbgpkZ8BVgp7v/16JJid8OCpruStvotLPPAWngHnf/dI1XqarM7Hzye/WQv0n9N5LwHpjZN4FN5LvCPQB8Evg28BCwGngOeLu7N+1BzQneg03kyzkO7AXeU6hnNxsz+03gJ8AvgDAafRv5On5itoPJNF3gi4hIec1W0hERkQko8EVEEkKBLyKSEAp8EZGEUOCLiCSEAl9EJCEU+CIiCfH/ATewNn2FBB11AAAAAElFTkSuQmCC\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"# Dupliquer les graphiques\n",
"# Regarde: https://stackoverflow.com/questions/21788593/statsmodels-duplicate-charts\n",
"# https://github.com/statsmodels/statsmodels/issues/1265\n",
"fig_first = plot_acf(df[\"Milk First Difference\"].dropna())"
]
},
{
"cell_type": "code",
"execution_count": 34,
"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_seasonal_first = plot_acf(df[\"Seasonal First Difference\"].dropna())"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Pandas a également intégré cette fonctionnalité, mais uniquement pour l'ACF, et non pour le PACF. Je recommande donc l'utilisation de statsmodels, car ACF et PACF sont plus au cœur de sa fonctionnalité que de celle de pandas."
]
},
{
"cell_type": "code",
"execution_count": 35,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x1c21a0b850>"
]
},
"execution_count": 35,
"metadata": {},
"output_type": "execute_result"
},
{
"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": [
"from pandas.plotting import autocorrelation_plot\n",
"autocorrelation_plot(df['Seasonal First Difference'].dropna())"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Autocorrelation Partielle\n",
"\n",
"En général, une corrélation partielle est une corrélation conditionnelle.\n",
"\n",
"C'est la corrélation entre deux variables sous l'hypothèse que nous connaissons et prenons en compte les valeurs d'un autre ensemble de variables.\n",
"\n",
"Par exemple, considérons un contexte de régression dans lequel y = une variable réponse et x1, x2 et x3 sont des variables prédicteurs. La corrélation partielle entre y et x3 est la corrélation entre les variables déterminées en tenant compte de la façon dont y et x3 sont liés à x1 et x2.\n",
"\n",
"Formellement, cette relation est définie comme suit\n",
"\n",
"## $\\frac{\\text{Covariance}(y, x_3|x_1, x_2)}{\\sqrt{\\text{Variance}(y|x_1, x_2)\\text{Variance}(x_3| x_1, x_2)}}$\n",
"\n",
"Regarde ce [lien](http://www.itl.nist.gov/div898/handbook/pmc/section4/pmc4463.htm) pour avoir tous les détails."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Nous pouvons alors tracer cette relation:"
]
},
{
"cell_type": "code",
"execution_count": 36,
"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": [
"result = plot_pacf(df[\"Seasonal First Difference\"].dropna())"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Interprétation\n",
"\n",
"En général, une chute ou baisse brutale après le décalage \"k\" suggère d'utiliser un modèle AR-k (Auto-regressive). S'il y a une baisse graduelle, cela suggère un modèle MA (Moving Average)."
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true
},
"source": [
"### Réflexions finales sur l'Autocorrélation et l'Autocorrélation Partielle\n",
"\n",
"* L'identification d'un modèle AR est souvent mieux faite avec le PACF.\n",
" * Pour un modèle de AR, le PACF théorique \"s'arrête\" au-delà de l'ordre du modèle. L'expression \"s'arrête\" signifie qu'en théorie, les autocorrélations partielles sont égales à 0 au-delà de ce point. Autrement dit, le nombre d'autocorrélations partielles non nulles donne l'ordre du modèle AR. Par \"ordre du modèle\", nous entendons le retard le plus extrême de x qui est utilisé comme prédicteur.\n",
" \n",
" \n",
"* L'identification d'un modèle MA est souvent mieux faite avec l'ACF plutôt qu'avec le PACF.\n",
" * Pour un modèle MA, le PACF théorique ne s'arrête pas, mais se rétrécit plutôt vers 0 d'une manière ou d'une autre. Un modèle plus clair pour un modèle MA se trouve dans le PACF. L'ACF aura des autocorrélations non nulles seulement aux retards impliqués dans le modèle."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"_____\n",
"### Graphiques définitifs de ACF et PACF\n",
"\n",
"Nous avons exploité un certain nombre de graphiques, alors obtenons rapidement nos graphiques \"finaux\" de l'ACF et du PACF. Ce sont ceux que nous allons référencer dans le reste du notebook ci-dessous.\n",
"_____"
]
},
{
"cell_type": "code",
"execution_count": 37,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 864x576 with 2 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"fig = plt.figure(figsize=(12,8))\n",
"ax1 = fig.add_subplot(211)\n",
"fig = sm.graphics.tsa.plot_acf(df['Seasonal First Difference'].iloc[13:], lags=40, ax=ax1)\n",
"ax2 = fig.add_subplot(212)\n",
"fig = sm.graphics.tsa.plot_pacf(df['Seasonal First Difference'].iloc[13:], lags=40, ax=ax2)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## En utilisant le modèle ARIMA Saisonnier\n",
"\n",
"Enfin, nous pouvons utiliser notre modèle ARIMA maintenant que nous avons une bonne compréhension de nos données !"
]
},
{
"cell_type": "code",
"execution_count": 38,
"metadata": {},
"outputs": [],
"source": [
"# Pour des données non sasonnières\n",
"from statsmodels.tsa.arima_model import ARIMA"
]
},
{
"cell_type": "code",
"execution_count": 39,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Help on class ARIMA in module statsmodels.tsa.arima_model:\n",
"\n",
"class ARIMA(ARMA)\n",
" | ARIMA(endog, order, exog=None, dates=None, freq=None, missing='none')\n",
" | \n",
" | Autoregressive Integrated Moving Average ARIMA(p,d,q) Model\n",
" | \n",
" | Parameters\n",
" | ----------\n",
" | endog : array-like\n",
" | The endogenous variable.\n",
" | order : iterable\n",
" | The (p,d,q) order of the model for the number of AR parameters,\n",
" | differences, and MA parameters to use.\n",
" | exog : array-like, optional\n",
" | An optional array of exogenous variables. This should *not* include a\n",
" | constant or trend. You can specify this in the `fit` method.\n",
" | dates : array-like of datetime, optional\n",
" | An array-like object of datetime objects. If a pandas object is given\n",
" | for endog or exog, it is assumed to have a DateIndex.\n",
" | freq : str, optional\n",
" | The frequency of the time-series. A Pandas offset or 'B', 'D', 'W',\n",
" | 'M', 'A', or 'Q'. This is optional if dates are given.\n",
" | \n",
" | \n",
" | Notes\n",
" | -----\n",
" | If exogenous variables are given, then the model that is fit is\n",
" | \n",
" | .. math::\n",
" | \n",
" | \\phi(L)(y_t - X_t\\beta) = \\theta(L)\\epsilon_t\n",
" | \n",
" | where :math:`\\phi` and :math:`\\theta` are polynomials in the lag\n",
" | operator, :math:`L`. This is the regression model with ARMA errors,\n",
" | or ARMAX model. This specification is used, whether or not the model\n",
" | is fit using conditional sum of square or maximum-likelihood, using\n",
" | the `method` argument in\n",
" | :meth:`statsmodels.tsa.arima_model.ARIMA.fit`. Therefore, for\n",
" | now, `css` and `mle` refer to estimation methods only. This may\n",
" | change for the case of the `css` model in future versions.\n",
" | \n",
" | Method resolution order:\n",
" | ARIMA\n",
" | ARMA\n",
" | statsmodels.tsa.base.tsa_model.TimeSeriesModel\n",
" | statsmodels.base.model.LikelihoodModel\n",
" | statsmodels.base.model.Model\n",
" | builtins.object\n",
" | \n",
" | Methods defined here:\n",
" | \n",
" | __getnewargs__(self)\n",
" | \n",
" | __init__(self, endog, order, exog=None, dates=None, freq=None, missing='none')\n",
" | Initialize self. See help(type(self)) for accurate signature.\n",
" | \n",
" | fit(self, start_params=None, trend='c', method='css-mle', transparams=True, solver='lbfgs', maxiter=500, full_output=1, disp=5, callback=None, start_ar_lags=None, **kwargs)\n",
" | Fits ARIMA(p,d,q) model by exact maximum likelihood via Kalman filter.\n",
" | \n",
" | Parameters\n",
" | ----------\n",
" | start_params : array-like, optional\n",
" | Starting parameters for ARMA(p,q). If None, the default is given\n",
" | by ARMA._fit_start_params. See there for more information.\n",
" | transparams : bool, optional\n",
" | Whehter or not to transform the parameters to ensure stationarity.\n",
" | Uses the transformation suggested in Jones (1980). If False,\n",
" | no checking for stationarity or invertibility is done.\n",
" | method : str {'css-mle','mle','css'}\n",
" | This is the loglikelihood to maximize. If \"css-mle\", the\n",
" | conditional sum of squares likelihood is maximized and its values\n",
" | are used as starting values for the computation of the exact\n",
" | likelihood via the Kalman filter. If \"mle\", the exact likelihood\n",
" | is maximized via the Kalman Filter. If \"css\" the conditional sum\n",
" | of squares likelihood is maximized. All three methods use\n",
" | `start_params` as starting parameters. See above for more\n",
" | information.\n",
" | trend : str {'c','nc'}\n",
" | Whether to include a constant or not. 'c' includes constant,\n",
" | 'nc' no constant.\n",
" | solver : str or None, optional\n",
" | Solver to be used. The default is 'lbfgs' (limited memory\n",
" | Broyden-Fletcher-Goldfarb-Shanno). Other choices are 'bfgs',\n",
" | 'newton' (Newton-Raphson), 'nm' (Nelder-Mead), 'cg' -\n",
" | (conjugate gradient), 'ncg' (non-conjugate gradient), and\n",
" | 'powell'. By default, the limited memory BFGS uses m=12 to\n",
" | approximate the Hessian, projected gradient tolerance of 1e-8 and\n",
" | factr = 1e2. You can change these by using kwargs.\n",
" | maxiter : int, optional\n",
" | The maximum number of function evaluations. Default is 500.\n",
" | tol : float\n",
" | The convergence tolerance. Default is 1e-08.\n",
" | full_output : bool, optional\n",
" | If True, all output from solver will be available in\n",
" | the Results object's mle_retvals attribute. Output is dependent\n",
" | on the solver. See Notes for more information.\n",
" | disp : int, optional\n",
" | If True, convergence information is printed. For the default\n",
" | l_bfgs_b solver, disp controls the frequency of the output during\n",
" | the iterations. disp < 0 means no output in this case.\n",
" | callback : function, optional\n",
" | Called after each iteration as callback(xk) where xk is the current\n",
" | parameter vector.\n",
" | start_ar_lags : int, optional\n",
" | Parameter for fitting start_params. When fitting start_params,\n",
" | residuals are obtained from an AR fit, then an ARMA(p,q) model is\n",
" | fit via OLS using these residuals. If start_ar_lags is None, fit\n",
" | an AR process according to best BIC. If start_ar_lags is not None,\n",
" | fits an AR process with a lag length equal to start_ar_lags.\n",
" | See ARMA._fit_start_params_hr for more information.\n",
" | kwargs\n",
" | See Notes for keyword arguments that can be passed to fit.\n",
" | \n",
" | Returns\n",
" | -------\n",
" | `statsmodels.tsa.arima.ARIMAResults` class\n",
" | \n",
" | See Also\n",
" | --------\n",
" | statsmodels.base.model.LikelihoodModel.fit : for more information\n",
" | on using the solvers.\n",
" | ARIMAResults : results class returned by fit\n",
" | \n",
" | Notes\n",
" | -----\n",
" | If fit by 'mle', it is assumed for the Kalman Filter that the initial\n",
" | unknown state is zero, and that the initial variance is\n",
" | P = dot(inv(identity(m**2)-kron(T,T)),dot(R,R.T).ravel('F')).reshape(r,\n",
" | r, order = 'F')\n",
" | \n",
" | predict(self, params, start=None, end=None, exog=None, typ='linear', dynamic=False)\n",
" | ARIMA model in-sample and out-of-sample prediction\n",
" | \n",
" | Parameters\n",
" | ----------\n",
" | params : array-like\n",
" | The fitted parameters of the model.\n",
" | start : int, str, or datetime\n",
" | Zero-indexed observation number at which to start forecasting, ie.,\n",
" | the first forecast is start. Can also be a date string to\n",
" | parse or a datetime type.\n",
" | end : int, str, or datetime\n",
" | Zero-indexed observation number at which to end forecasting, ie.,\n",
" | the first forecast is start. Can also be a date string to\n",
" | parse or a datetime type. However, if the dates index does not\n",
" | have a fixed frequency, end must be an integer index if you\n",
" | want out of sample prediction.\n",
" | exog : array-like, optional\n",
" | If the model is an ARMAX and out-of-sample forecasting is\n",
" | requested, exog must be given. Note that you'll need to pass\n",
" | `k_ar` additional lags for any exogenous variables. E.g., if you\n",
" | fit an ARMAX(2, q) model and want to predict 5 steps, you need 7\n",
" | observations to do this.\n",
" | dynamic : bool, optional\n",
" | The `dynamic` keyword affects in-sample prediction. If dynamic\n",
" | is False, then the in-sample lagged values are used for\n",
" | prediction. If `dynamic` is True, then in-sample forecasts are\n",
" | used in place of lagged dependent variables. The first forecasted\n",
" | value is `start`.\n",
" | typ : str {'linear', 'levels'}\n",
" | \n",
" | - 'linear' : Linear prediction in terms of the differenced\n",
" | endogenous variables.\n",
" | - 'levels' : Predict the levels of the original endogenous\n",
" | variables.\n",
" | \n",
" | \n",
" | Returns\n",
" | -------\n",
" | predict : array\n",
" | The predicted values.\n",
" | \n",
" | \n",
" | \n",
" | Notes\n",
" | -----\n",
" | Use the results predict method instead.\n",
" | \n",
" | ----------------------------------------------------------------------\n",
" | Static methods defined here:\n",
" | \n",
" | __new__(cls, endog, order, exog=None, dates=None, freq=None, missing='none')\n",
" | Create and return a new object. See help(type) for accurate signature.\n",
" | \n",
" | ----------------------------------------------------------------------\n",
" | Methods inherited from ARMA:\n",
" | \n",
" | geterrors(self, params)\n",
" | Get the errors of the ARMA process.\n",
" | \n",
" | Parameters\n",
" | ----------\n",
" | params : array-like\n",
" | The fitted ARMA parameters\n",
" | order : array-like\n",
" | 3 item iterable, with the number of AR, MA, and exogenous\n",
" | parameters, including the trend\n",
" | \n",
" | hessian(self, params)\n",
" | Compute the Hessian at params,\n",
" | \n",
" | Notes\n",
" | -----\n",
" | This is a numerical approximation.\n",
" | \n",
" | loglike(self, params, set_sigma2=True)\n",
" | Compute the log-likelihood for ARMA(p,q) model\n",
" | \n",
" | Notes\n",
" | -----\n",
" | Likelihood used depends on the method set in fit\n",
" | \n",
" | loglike_css(self, params, set_sigma2=True)\n",
" | Conditional Sum of Squares likelihood function.\n",
" | \n",
" | loglike_kalman(self, params, set_sigma2=True)\n",
" | Compute exact loglikelihood for ARMA(p,q) model by the Kalman Filter.\n",
" | \n",
" | score(self, params)\n",
" | Compute the score function at params.\n",
" | \n",
" | Notes\n",
" | -----\n",
" | This is a numerical approximation.\n",
" | \n",
" | ----------------------------------------------------------------------\n",
" | Class methods inherited from ARMA:\n",
" | \n",
" | from_formula(formula, data, subset=None, drop_cols=None, *args, **kwargs) from builtins.type\n",
" | Create a Model from a formula and dataframe.\n",
" | \n",
" | Parameters\n",
" | ----------\n",
" | formula : str or generic Formula object\n",
" | The formula specifying the model\n",
" | data : array-like\n",
" | The data for the model. See Notes.\n",
" | subset : array-like\n",
" | An array-like object of booleans, integers, or index values that\n",
" | indicate the subset of df to use in the model. Assumes df is a\n",
" | `pandas.DataFrame`\n",
" | drop_cols : array-like\n",
" | Columns to drop from the design matrix. Cannot be used to\n",
" | drop terms involving categoricals.\n",
" | args : extra arguments\n",
" | These are passed to the model\n",
" | kwargs : extra keyword arguments\n",
" | These are passed to the model with one exception. The\n",
" | ``eval_env`` keyword is passed to patsy. It can be either a\n",
" | :class:`patsy:patsy.EvalEnvironment` object or an integer\n",
" | indicating the depth of the namespace to use. For example, the\n",
" | default ``eval_env=0`` uses the calling namespace. If you wish\n",
" | to use a \"clean\" environment set ``eval_env=-1``.\n",
" | \n",
" | Returns\n",
" | -------\n",
" | model : Model instance\n",
" | \n",
" | Notes\n",
" | -----\n",
" | data must define __getitem__ with the keys in the formula terms\n",
" | args and kwargs are passed on to the model instantiation. E.g.,\n",
" | a numpy structured or rec array, a dictionary, or a pandas DataFrame.\n",
" | \n",
" | ----------------------------------------------------------------------\n",
" | Data descriptors inherited from statsmodels.tsa.base.tsa_model.TimeSeriesModel:\n",
" | \n",
" | exog_names\n",
" | \n",
" | ----------------------------------------------------------------------\n",
" | Methods inherited from statsmodels.base.model.LikelihoodModel:\n",
" | \n",
" | information(self, params)\n",
" | Fisher information matrix of model\n",
" | \n",
" | Returns -Hessian of loglike evaluated at params.\n",
" | \n",
" | initialize(self)\n",
" | Initialize (possibly re-initialize) a Model instance. For\n",
" | instance, the design matrix of a linear model may change\n",
" | and some things must be recomputed.\n",
" | \n",
" | ----------------------------------------------------------------------\n",
" | Data descriptors inherited from statsmodels.base.model.Model:\n",
" | \n",
" | __dict__\n",
" | dictionary for instance variables (if defined)\n",
" | \n",
" | __weakref__\n",
" | list of weak references to the object (if defined)\n",
" | \n",
" | endog_names\n",
" | Names of endogenous variables\n",
"\n"
]
}
],
"source": [
"# Je vous recommande d'y jeter un coup d'oeil !\n",
"\n",
"# \n",
"help(ARIMA)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Paramètres p, d, q\n",
"\n",
"* p: Le nombre d'observations de retard incluses dans le modèle.\n",
"* d: Le nombre de fois que les observations brutes sont différentes, aussi appelé le degré de différence.\n",
"* q: La taille de la fenêtre de la moyenne mobile, aussi appelée l'ordre de la moyenne mobile."
]
},
{
"cell_type": "code",
"execution_count": 40,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" Statespace Model Results \n",
"==========================================================================================\n",
"Dep. Variable: Milk in pounds per cow No. Observations: 168\n",
"Model: SARIMAX(0, 1, 0)x(1, 1, 1, 12) Log Likelihood -534.065\n",
"Date: Fri, 27 Dec 2019 AIC 1074.131\n",
"Time: 13:58:54 BIC 1083.261\n",
"Sample: 01-01-1962 HQIC 1077.839\n",
" - 12-01-1975 \n",
"Covariance Type: opg \n",
"==============================================================================\n",
" coef std err z P>|z| [0.025 0.975]\n",
"------------------------------------------------------------------------------\n",
"ar.S.L12 -0.0449 0.106 -0.422 0.673 -0.253 0.163\n",
"ma.S.L12 -0.5860 0.102 -5.762 0.000 -0.785 -0.387\n",
"sigma2 55.5118 5.356 10.365 0.000 45.015 66.009\n",
"===================================================================================\n",
"Ljung-Box (Q): 33.48 Jarque-Bera (JB): 32.04\n",
"Prob(Q): 0.76 Prob(JB): 0.00\n",
"Heteroskedasticity (H): 0.69 Skew: 0.77\n",
"Prob(H) (two-sided): 0.18 Kurtosis: 4.60\n",
"===================================================================================\n",
"\n",
"Warnings:\n",
"[1] Covariance matrix calculated using the outer product of gradients (complex-step).\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/rod/opt/anaconda3/envs/pyfinance/lib/python3.7/site-packages/statsmodels/tsa/base/tsa_model.py:165: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
" % freq, ValueWarning)\n"
]
}
],
"source": [
"# Nou avons des données saisonnières!\n",
"model = sm.tsa.statespace.SARIMAX(df['Milk in pounds per cow'],order=(0,1,0), seasonal_order=(1,1,1,12))\n",
"results = model.fit()\n",
"print(results.summary())\n",
"# avertissement obtenu en passant dans la fonction statemodels un dataframe dont l'index est un objet datetime pandas"
]
},
{
"cell_type": "code",
"execution_count": 41,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x1c23bd5810>"
]
},
"execution_count": 41,
"metadata": {},
"output_type": "execute_result"
},
{
"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": [
"results.resid.plot()"
]
},
{
"cell_type": "code",
"execution_count": 42,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x1c23bcab10>"
]
},
"execution_count": 42,
"metadata": {},
"output_type": "execute_result"
},
{
"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": [
"results.resid.plot(kind='kde')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Prédiction des Valeurs Futures\n",
"\n",
"Nous pouvons avoir une idée de la performance de notre modèle en prédisant simplement pour des valeurs que nous connaissons déjà:"
]
},
{
"cell_type": "code",
"execution_count": 43,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x1c23d80c90>"
]
},
"execution_count": 43,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 864x576 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"df['forecast'] = results.predict(start = 150, end= 168, dynamic= True) \n",
"df[['Milk in pounds per cow','forecast']].plot(figsize=(12,8))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Prévision\n",
"Cela demande plus de temps, alors créons-les avec pandas sur notre dataframe original !"
]
},
{
"cell_type": "code",
"execution_count": 44,
"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>Milk in pounds per cow</th>\n",
" <th>Milk First Difference</th>\n",
" <th>Milk Second Difference</th>\n",
" <th>Seasonal Difference</th>\n",
" <th>Seasonal First Difference</th>\n",
" <th>forecast</th>\n",
" </tr>\n",
" <tr>\n",
" <th>Month</th>\n",
" <th></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>1975-08-01</th>\n",
" <td>858.0</td>\n",
" <td>-38.0</td>\n",
" <td>3.0</td>\n",
" <td>-9.0</td>\n",
" <td>3.0</td>\n",
" <td>879.668974</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1975-09-01</th>\n",
" <td>817.0</td>\n",
" <td>-41.0</td>\n",
" <td>-3.0</td>\n",
" <td>2.0</td>\n",
" <td>11.0</td>\n",
" <td>832.328554</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1975-10-01</th>\n",
" <td>827.0</td>\n",
" <td>10.0</td>\n",
" <td>51.0</td>\n",
" <td>15.0</td>\n",
" <td>13.0</td>\n",
" <td>837.722249</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1975-11-01</th>\n",
" <td>797.0</td>\n",
" <td>-30.0</td>\n",
" <td>-40.0</td>\n",
" <td>24.0</td>\n",
" <td>9.0</td>\n",
" <td>802.452736</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1975-12-01</th>\n",
" <td>843.0</td>\n",
" <td>46.0</td>\n",
" <td>76.0</td>\n",
" <td>30.0</td>\n",
" <td>6.0</td>\n",
" <td>842.499870</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Milk in pounds per cow Milk First Difference \\\n",
"Month \n",
"1975-08-01 858.0 -38.0 \n",
"1975-09-01 817.0 -41.0 \n",
"1975-10-01 827.0 10.0 \n",
"1975-11-01 797.0 -30.0 \n",
"1975-12-01 843.0 46.0 \n",
"\n",
" Milk Second Difference Seasonal Difference \\\n",
"Month \n",
"1975-08-01 3.0 -9.0 \n",
"1975-09-01 -3.0 2.0 \n",
"1975-10-01 51.0 15.0 \n",
"1975-11-01 -40.0 24.0 \n",
"1975-12-01 76.0 30.0 \n",
"\n",
" Seasonal First Difference forecast \n",
"Month \n",
"1975-08-01 3.0 879.668974 \n",
"1975-09-01 11.0 832.328554 \n",
"1975-10-01 13.0 837.722249 \n",
"1975-11-01 9.0 802.452736 \n",
"1975-12-01 6.0 842.499870 "
]
},
"execution_count": 44,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.tail()"
]
},
{
"cell_type": "code",
"execution_count": 45,
"metadata": {},
"outputs": [],
"source": [
"# https://pandas.pydata.org/pandas-docs/stable/timeseries.html\n",
"# Alternatives \n",
"# pd.date_range(df.index[-1],periods=12,freq='M')"
]
},
{
"cell_type": "code",
"execution_count": 46,
"metadata": {},
"outputs": [],
"source": [
"from pandas.tseries.offsets import DateOffset"
]
},
{
"cell_type": "code",
"execution_count": 47,
"metadata": {},
"outputs": [],
"source": [
"future_dates = [df.index[-1] + DateOffset(months=x) for x in range(0,24) ]"
]
},
{
"cell_type": "code",
"execution_count": 48,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[Timestamp('1975-12-01 00:00:00'),\n",
" Timestamp('1976-01-01 00:00:00'),\n",
" Timestamp('1976-02-01 00:00:00'),\n",
" Timestamp('1976-03-01 00:00:00'),\n",
" Timestamp('1976-04-01 00:00:00'),\n",
" Timestamp('1976-05-01 00:00:00'),\n",
" Timestamp('1976-06-01 00:00:00'),\n",
" Timestamp('1976-07-01 00:00:00'),\n",
" Timestamp('1976-08-01 00:00:00'),\n",
" Timestamp('1976-09-01 00:00:00'),\n",
" Timestamp('1976-10-01 00:00:00'),\n",
" Timestamp('1976-11-01 00:00:00'),\n",
" Timestamp('1976-12-01 00:00:00'),\n",
" Timestamp('1977-01-01 00:00:00'),\n",
" Timestamp('1977-02-01 00:00:00'),\n",
" Timestamp('1977-03-01 00:00:00'),\n",
" Timestamp('1977-04-01 00:00:00'),\n",
" Timestamp('1977-05-01 00:00:00'),\n",
" Timestamp('1977-06-01 00:00:00'),\n",
" Timestamp('1977-07-01 00:00:00'),\n",
" Timestamp('1977-08-01 00:00:00'),\n",
" Timestamp('1977-09-01 00:00:00'),\n",
" Timestamp('1977-10-01 00:00:00'),\n",
" Timestamp('1977-11-01 00:00:00')]"
]
},
"execution_count": 48,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"future_dates"
]
},
{
"cell_type": "code",
"execution_count": 49,
"metadata": {},
"outputs": [],
"source": [
"future_dates_df = pd.DataFrame(index=future_dates[1:],columns=df.columns)"
]
},
{
"cell_type": "code",
"execution_count": 50,
"metadata": {},
"outputs": [],
"source": [
"future_df = pd.concat([df,future_dates_df])"
]
},
{
"cell_type": "code",
"execution_count": 51,
"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>Milk in pounds per cow</th>\n",
" <th>Milk First Difference</th>\n",
" <th>Milk Second Difference</th>\n",
" <th>Seasonal Difference</th>\n",
" <th>Seasonal First Difference</th>\n",
" <th>forecast</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>1962-01-01</th>\n",
" <td>589.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1962-02-01</th>\n",
" <td>561.0</td>\n",
" <td>-28.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1962-03-01</th>\n",
" <td>640.0</td>\n",
" <td>79.0</td>\n",
" <td>107.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1962-04-01</th>\n",
" <td>656.0</td>\n",
" <td>16.0</td>\n",
" <td>-63.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1962-05-01</th>\n",
" <td>727.0</td>\n",
" <td>71.0</td>\n",
" <td>55.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Milk in pounds per cow Milk First Difference \\\n",
"1962-01-01 589.0 NaN \n",
"1962-02-01 561.0 -28.0 \n",
"1962-03-01 640.0 79.0 \n",
"1962-04-01 656.0 16.0 \n",
"1962-05-01 727.0 71.0 \n",
"\n",
" Milk Second Difference Seasonal Difference \\\n",
"1962-01-01 NaN NaN \n",
"1962-02-01 NaN NaN \n",
"1962-03-01 107.0 NaN \n",
"1962-04-01 -63.0 NaN \n",
"1962-05-01 55.0 NaN \n",
"\n",
" Seasonal First Difference forecast \n",
"1962-01-01 NaN NaN \n",
"1962-02-01 NaN NaN \n",
"1962-03-01 NaN NaN \n",
"1962-04-01 NaN NaN \n",
"1962-05-01 NaN NaN "
]
},
"execution_count": 51,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"future_df.head()"
]
},
{
"cell_type": "code",
"execution_count": 52,
"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>Milk in pounds per cow</th>\n",
" <th>Milk First Difference</th>\n",
" <th>Milk Second Difference</th>\n",
" <th>Seasonal Difference</th>\n",
" <th>Seasonal First Difference</th>\n",
" <th>forecast</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>1977-07-01</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1977-08-01</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1977-09-01</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1977-10-01</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1977-11-01</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Milk in pounds per cow Milk First Difference \\\n",
"1977-07-01 NaN NaN \n",
"1977-08-01 NaN NaN \n",
"1977-09-01 NaN NaN \n",
"1977-10-01 NaN NaN \n",
"1977-11-01 NaN NaN \n",
"\n",
" Milk Second Difference Seasonal Difference \\\n",
"1977-07-01 NaN NaN \n",
"1977-08-01 NaN NaN \n",
"1977-09-01 NaN NaN \n",
"1977-10-01 NaN NaN \n",
"1977-11-01 NaN NaN \n",
"\n",
" Seasonal First Difference forecast \n",
"1977-07-01 NaN NaN \n",
"1977-08-01 NaN NaN \n",
"1977-09-01 NaN NaN \n",
"1977-10-01 NaN NaN \n",
"1977-11-01 NaN NaN "
]
},
"execution_count": 52,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"future_df.tail()"
]
},
{
"cell_type": "code",
"execution_count": 53,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x1c1f38b9d0>"
]
},
"execution_count": 53,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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\n",
"text/plain": [
"<Figure size 864x576 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"future_df['forecast'] = results.predict(start = 168, end = 188, dynamic= True) \n",
"future_df[['Milk in pounds per cow', 'forecast']].plot(figsize=(12, 8)) "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Pas mal ! Plutôt cool en fait ! J'espère que cela vous a aidé à voir le potentiel des modèles ARIMA, malheureusement beaucoup de données financières ne suivront pas ce genre de comportement, en fait il suivra souvent quelque chose indiquant un mouvement brownien, qu'est ce que c'est ? Et bien allez à la prochaine section vidéo et nous allons le découvrir !\n",
"\n",
"# Bon Travail!"
]
}
],
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