Week_9/Day_2/time_series_challenge.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Time Series Challenge\n",
    "In this challenge, we will compare different time series prediction techniques on the dataset we can find [here](https://drive.google.com/file/d/1pgZlxluv5_cbgwR5JSWu4njbYXo_06Eb/view?usp=sharing).\n",
    "- moving average\n",
    "- ARIMA\n",
    "- Prophet\n",
    "- (Stretch) LSTMs"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are several item categories in this sales data. You can choose your own category or predict overal sales.\n",
    "\n",
    "> #### Note\n",
    "> We have one order in every row, so don't forget to transform data into time series format first. The data can be transformed to daily, weekly or monthly timeseries."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 563,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from sklearn.metrics import mean_squared_error\n",
    "\n",
    "from statsmodels.tsa.arima_model import ARIMA\n",
    "from statsmodels.tsa.stattools import adfuller\n",
    "from statsmodels.graphics.tsaplots import plot_acf\n",
    "from statsmodels.graphics.tsaplots import plot_pacf\n",
    "from statsmodels.tsa.seasonal import seasonal_decompose\n",
    "\n",
    "from fbprophet import Prophet\n",
    "from fbprophet.plot import plot_plotly, plot_components_plotly\n",
    "\n",
    "import tensorflow as tf\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Dense\n",
    "from keras.layers import LSTM\n",
    "from keras.layers import Dropout\n",
    "\n",
    "import copy"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 564,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
       "\n",
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       "    }\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>Order ID</th>\n",
       "      <th>Order Date</th>\n",
       "      <th>Ship Date</th>\n",
       "      <th>Ship Mode</th>\n",
       "      <th>Customer ID</th>\n",
       "      <th>Customer Name</th>\n",
       "      <th>Segment</th>\n",
       "      <th>Country</th>\n",
       "      <th>City</th>\n",
       "      <th>State</th>\n",
       "      <th>Postal Code</th>\n",
       "      <th>Region</th>\n",
       "      <th>Product ID</th>\n",
       "      <th>Category</th>\n",
       "      <th>Sub-Category</th>\n",
       "      <th>Product Name</th>\n",
       "      <th>Sales</th>\n",
       "      <th>Quantity</th>\n",
       "      <th>Discount</th>\n",
       "      <th>Profit</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>CA-2016-152156</td>\n",
       "      <td>2016-11-08</td>\n",
       "      <td>2016-11-11</td>\n",
       "      <td>Second Class</td>\n",
       "      <td>CG-12520</td>\n",
       "      <td>Claire Gute</td>\n",
       "      <td>Consumer</td>\n",
       "      <td>United States</td>\n",
       "      <td>Henderson</td>\n",
       "      <td>Kentucky</td>\n",
       "      <td>42420</td>\n",
       "      <td>South</td>\n",
       "      <td>FUR-BO-10001798</td>\n",
       "      <td>Furniture</td>\n",
       "      <td>Bookcases</td>\n",
       "      <td>Bush Somerset Collection Bookcase</td>\n",
       "      <td>261.9600</td>\n",
       "      <td>2</td>\n",
       "      <td>0.00</td>\n",
       "      <td>41.9136</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>CA-2016-152156</td>\n",
       "      <td>2016-11-08</td>\n",
       "      <td>2016-11-11</td>\n",
       "      <td>Second Class</td>\n",
       "      <td>CG-12520</td>\n",
       "      <td>Claire Gute</td>\n",
       "      <td>Consumer</td>\n",
       "      <td>United States</td>\n",
       "      <td>Henderson</td>\n",
       "      <td>Kentucky</td>\n",
       "      <td>42420</td>\n",
       "      <td>South</td>\n",
       "      <td>FUR-CH-10000454</td>\n",
       "      <td>Furniture</td>\n",
       "      <td>Chairs</td>\n",
       "      <td>Hon Deluxe Fabric Upholstered Stacking Chairs,...</td>\n",
       "      <td>731.9400</td>\n",
       "      <td>3</td>\n",
       "      <td>0.00</td>\n",
       "      <td>219.5820</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>CA-2016-138688</td>\n",
       "      <td>2016-06-12</td>\n",
       "      <td>2016-06-16</td>\n",
       "      <td>Second Class</td>\n",
       "      <td>DV-13045</td>\n",
       "      <td>Darrin Van Huff</td>\n",
       "      <td>Corporate</td>\n",
       "      <td>United States</td>\n",
       "      <td>Los Angeles</td>\n",
       "      <td>California</td>\n",
       "      <td>90036</td>\n",
       "      <td>West</td>\n",
       "      <td>OFF-LA-10000240</td>\n",
       "      <td>Office Supplies</td>\n",
       "      <td>Labels</td>\n",
       "      <td>Self-Adhesive Address Labels for Typewriters b...</td>\n",
       "      <td>14.6200</td>\n",
       "      <td>2</td>\n",
       "      <td>0.00</td>\n",
       "      <td>6.8714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>US-2015-108966</td>\n",
       "      <td>2015-10-11</td>\n",
       "      <td>2015-10-18</td>\n",
       "      <td>Standard Class</td>\n",
       "      <td>SO-20335</td>\n",
       "      <td>Sean O'Donnell</td>\n",
       "      <td>Consumer</td>\n",
       "      <td>United States</td>\n",
       "      <td>Fort Lauderdale</td>\n",
       "      <td>Florida</td>\n",
       "      <td>33311</td>\n",
       "      <td>South</td>\n",
       "      <td>FUR-TA-10000577</td>\n",
       "      <td>Furniture</td>\n",
       "      <td>Tables</td>\n",
       "      <td>Bretford CR4500 Series Slim Rectangular Table</td>\n",
       "      <td>957.5775</td>\n",
       "      <td>5</td>\n",
       "      <td>0.45</td>\n",
       "      <td>-383.0310</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>US-2015-108966</td>\n",
       "      <td>2015-10-11</td>\n",
       "      <td>2015-10-18</td>\n",
       "      <td>Standard Class</td>\n",
       "      <td>SO-20335</td>\n",
       "      <td>Sean O'Donnell</td>\n",
       "      <td>Consumer</td>\n",
       "      <td>United States</td>\n",
       "      <td>Fort Lauderdale</td>\n",
       "      <td>Florida</td>\n",
       "      <td>33311</td>\n",
       "      <td>South</td>\n",
       "      <td>OFF-ST-10000760</td>\n",
       "      <td>Office Supplies</td>\n",
       "      <td>Storage</td>\n",
       "      <td>Eldon Fold 'N Roll Cart System</td>\n",
       "      <td>22.3680</td>\n",
       "      <td>2</td>\n",
       "      <td>0.20</td>\n",
       "      <td>2.5164</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Order ID Order Date  Ship Date       Ship Mode Customer ID  \\\n",
       "0  CA-2016-152156 2016-11-08 2016-11-11    Second Class    CG-12520   \n",
       "1  CA-2016-152156 2016-11-08 2016-11-11    Second Class    CG-12520   \n",
       "2  CA-2016-138688 2016-06-12 2016-06-16    Second Class    DV-13045   \n",
       "3  US-2015-108966 2015-10-11 2015-10-18  Standard Class    SO-20335   \n",
       "4  US-2015-108966 2015-10-11 2015-10-18  Standard Class    SO-20335   \n",
       "\n",
       "     Customer Name    Segment        Country             City       State  \\\n",
       "0      Claire Gute   Consumer  United States        Henderson    Kentucky   \n",
       "1      Claire Gute   Consumer  United States        Henderson    Kentucky   \n",
       "2  Darrin Van Huff  Corporate  United States      Los Angeles  California   \n",
       "3   Sean O'Donnell   Consumer  United States  Fort Lauderdale     Florida   \n",
       "4   Sean O'Donnell   Consumer  United States  Fort Lauderdale     Florida   \n",
       "\n",
       "   Postal Code Region       Product ID         Category Sub-Category  \\\n",
       "0        42420  South  FUR-BO-10001798        Furniture    Bookcases   \n",
       "1        42420  South  FUR-CH-10000454        Furniture       Chairs   \n",
       "2        90036   West  OFF-LA-10000240  Office Supplies       Labels   \n",
       "3        33311  South  FUR-TA-10000577        Furniture       Tables   \n",
       "4        33311  South  OFF-ST-10000760  Office Supplies      Storage   \n",
       "\n",
       "                                        Product Name     Sales  Quantity  \\\n",
       "0                  Bush Somerset Collection Bookcase  261.9600         2   \n",
       "1  Hon Deluxe Fabric Upholstered Stacking Chairs,...  731.9400         3   \n",
       "2  Self-Adhesive Address Labels for Typewriters b...   14.6200         2   \n",
       "3      Bretford CR4500 Series Slim Rectangular Table  957.5775         5   \n",
       "4                     Eldon Fold 'N Roll Cart System   22.3680         2   \n",
       "\n",
       "   Discount    Profit  \n",
       "0      0.00   41.9136  \n",
       "1      0.00  219.5820  \n",
       "2      0.00    6.8714  \n",
       "3      0.45 -383.0310  \n",
       "4      0.20    2.5164  "
      ]
     },
     "execution_count": 564,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 9994 rows × 20 columns\n",
    "dataset_train = pd.read_excel('Sample_Superstore.xls')\n",
    "dataset_train.drop('Row ID', axis = 1, inplace = True)\n",
    "dataset_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 565,
   "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>ds</th>\n",
       "      <th>y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>7980</th>\n",
       "      <td>2014-01-03</td>\n",
       "      <td>5.5512</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>739</th>\n",
       "      <td>2014-01-04</td>\n",
       "      <td>4.2717</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>740</th>\n",
       "      <td>2014-01-04</td>\n",
       "      <td>-64.7748</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>741</th>\n",
       "      <td>2014-01-04</td>\n",
       "      <td>-5.4870</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1759</th>\n",
       "      <td>2014-01-05</td>\n",
       "      <td>4.8840</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             ds        y\n",
       "7980 2014-01-03   5.5512\n",
       "739  2014-01-04   4.2717\n",
       "740  2014-01-04 -64.7748\n",
       "741  2014-01-04  -5.4870\n",
       "1759 2014-01-05   4.8840"
      ]
     },
     "execution_count": 565,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 9994 rows × 2 columns\n",
    "var_interest = 'Profit'\n",
    "df = dataset_train[['Order Date',var_interest]]\n",
    "df = df.rename(columns={'Order Date':'ds', var_interest:'y'})\n",
    "df = df.sort_values('ds')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 566,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>y</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ds</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2014-01-03</th>\n",
       "      <td>5.551200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-04</th>\n",
       "      <td>-21.996700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-05</th>\n",
       "      <td>4.884000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-06</th>\n",
       "      <td>150.894711</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-07</th>\n",
       "      <td>-35.981050</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     y\n",
       "ds                    \n",
       "2014-01-03    5.551200\n",
       "2014-01-04  -21.996700\n",
       "2014-01-05    4.884000\n",
       "2014-01-06  150.894711\n",
       "2014-01-07  -35.981050"
      ]
     },
     "execution_count": 566,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 1237 rows × 1 columns\n",
    "index_df = copy.deepcopy(df.groupby(df['ds']).mean())\n",
    "index_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 462,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f9ad40b0d60>]"
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     "execution_count": 462,
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     "data": {
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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(12,8))\n",
    "plt.plot(index_df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Moving Average"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 463,
   "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>y</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ds</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2014-01-03</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-04</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-05</th>\n",
       "      <td>-3.853833</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-06</th>\n",
       "      <td>44.594004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-07</th>\n",
       "      <td>39.932554</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-12-26</th>\n",
       "      <td>42.970812</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-12-27</th>\n",
       "      <td>4.970118</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-12-28</th>\n",
       "      <td>4.204997</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-12-29</th>\n",
       "      <td>17.012586</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-12-30</th>\n",
       "      <td>27.176691</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1237 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                    y\n",
       "ds                   \n",
       "2014-01-03        NaN\n",
       "2014-01-04        NaN\n",
       "2014-01-05  -3.853833\n",
       "2014-01-06  44.594004\n",
       "2014-01-07  39.932554\n",
       "...               ...\n",
       "2017-12-26  42.970812\n",
       "2017-12-27   4.970118\n",
       "2017-12-28   4.204997\n",
       "2017-12-29  17.012586\n",
       "2017-12-30  27.176691\n",
       "\n",
       "[1237 rows x 1 columns]"
      ]
     },
     "execution_count": 463,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rolling = index_df.rolling(window=3)\n",
    "rolling_mean = rolling.mean()\n",
    "rolling_mean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 464,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f9a9846f6a0>]"
      ]
     },
     "execution_count": 464,
     "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": [
    "fig, ax = plt.subplots(figsize=(12,8))\n",
    "ax.plot(index_df)\n",
    "ax.plot(rolling_mean, color='red')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 465,
   "metadata": {},
   "outputs": [],
   "source": [
    "# width = 3\n",
    "# lag1 = index_df.shift(1)\n",
    "# lag3 = index_df.shift(width - 1)\n",
    "# window = lag3.rolling(window=width)\n",
    "# window2 = lag1.rolling(window=width)\n",
    "# means = window.mean()\n",
    "# means2 = window2.mean()\n",
    "# dataframe = pd.concat([means, lag3, means2,lag1, index_df], axis=1)\n",
    "# dataframe.columns = ['mean','t-2','mean_2','t-1', 't+1']\n",
    "# dataframe.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 466,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1235"
      ]
     },
     "execution_count": 466,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(history)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 467,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "371"
      ]
     },
     "execution_count": 467,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 468,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "371"
      ]
     },
     "execution_count": 468,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(predictions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 469,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test MSE: 14198.641\n"
     ]
    }
   ],
   "source": [
    "X = index_df.values\n",
    "window = 3\n",
    "history = [X[i] for i in range(window)]\n",
    "test = [X[i] for i in range(window, len(X))]\n",
    "predictions = list()\n",
    "# walk forward over time steps in test\n",
    "for t in range(len(test)):\n",
    "    length = len(history)\n",
    "    yhat = np.mean([history[i] for i in range(length-window,length)])\n",
    "    obs = test[t]\n",
    "    predictions.append(yhat)\n",
    "    history.append(obs)\n",
    "    #print('predicted=%f, expected=%f' % (yhat, obs))\n",
    "error = mean_squared_error(test, predictions)\n",
    "print('Test MSE: %.3f' % error)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 470,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f9a9854f3a0>]"
      ]
     },
     "execution_count": 470,
     "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": [
    "zoom_start = 800\n",
    "zoom_end = 1000\n",
    "fig, ax = plt.subplots(figsize=(12,8))\n",
    "ax.plot(test[zoom_start:zoom_end])\n",
    "ax.plot(predictions[zoom_start:zoom_end], color='red')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Arima"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 471,
   "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>y</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ds</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2014-01-03</th>\n",
       "      <td>5.551200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-04</th>\n",
       "      <td>-21.996700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-05</th>\n",
       "      <td>4.884000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-06</th>\n",
       "      <td>150.894711</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-07</th>\n",
       "      <td>-35.981050</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     y\n",
       "ds                    \n",
       "2014-01-03    5.551200\n",
       "2014-01-04  -21.996700\n",
       "2014-01-05    4.884000\n",
       "2014-01-06  150.894711\n",
       "2014-01-07  -35.981050"
      ]
     },
     "execution_count": 471,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "index_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 472,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f9a985f69d0>]"
      ]
     },
     "execution_count": 472,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(18,8))\n",
    "plt.plot(index_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 473,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ADF Statistic: -10.828952\n",
      "p-value: 0.000000\n"
     ]
    }
   ],
   "source": [
    "res = adfuller(index_df)\n",
    "print('ADF Statistic: %f' % res[0])\n",
    "print('p-value: %f' % res[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 474,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,8))\n",
    "plt.subplot(211)\n",
    "plot_acf(index_df, ax=plt.gca())\n",
    "plt.subplot(212)\n",
    "plot_pacf(index_df, ax=plt.gca())\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 475,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1237"
      ]
     },
     "execution_count": 475,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(history)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 476,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1234"
      ]
     },
     "execution_count": 476,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 477,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1234"
      ]
     },
     "execution_count": 477,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(predictions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 483,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = index_df['y'].values\n",
    "size = int(len(X) * 0.7)\n",
    "train, test = X[0:size], X[size:len(X)]\n",
    "history = [x for x in train]\n",
    "predictions = list()\n",
    "for t in range(len(test)):\n",
    "    model = ARIMA(history, order=(0,1,0))\n",
    "    model_fit = model.fit(disp=0)\n",
    "    output = model_fit.forecast()\n",
    "    yhat = output[0]\n",
    "    predictions.append(yhat)\n",
    "    obs = test[t]\n",
    "    history.append(obs)\n",
    "    #print('predicted=%f, expected=%f' % (yhat, obs), '\\n')\n",
    "error = mean_squared_error(test, predictions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 484,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test MSE: 20475.329\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f9a83c2dd90>]"
      ]
     },
     "execution_count": 484,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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v0wBYcSYiIqK4wOBMdeJwhbZqWEyKFWciIiKKWwzOVCdhTw40m2ByOiHBwZkVZyIiIooDDM5UJ+GDs4LN7Qxt1TD+z+BMRERELRiDM9VJuFYNq9kEq8sJsUaoOLNVg4iIiFowBmeqk7BLbptNsLicEFaciYiIKA4xOFOdOFxuWC1BUzXMClZ3DT3OrDgTERFRC8bgTHXicAmspqCpGmYTbC4n3BZL4JVZcSYiIqI4wOBMdRJpqgZ7nImIiCheMThTnThcEtKqYTUrWNxOuNjjTERERHGIwZnqxOFyh7RqWD2tGmIOatVgxZmIiIjiAIMz1UnYqRomBauLFWciIiKKTwzOVCfOcK0aFpNu1bAEBWdWnImIiCgOMDhTrYkI7OFODjTpkwPdlqCTA1lxJiIiojjA4Ey15nQLAITpcVawuZxwWcyBN2DFmYiIiOIAgzPVmsPlBoCQVg2L2QSr2wlncKsGK85EREQUBxicqdYcLk/F2Ry8cqBecttlZo8zERERxR8GZ6o1b8XZHLxyoGeqRvA4OlaciYiIKA4wOFOt+YJz6MqBtppaNVhxJiIiohaMwZlqzbmfVg1HcMXZZALMZlaciYiIqEVjcKZas0dq1YAbFnGHVpwB3efMijMRERG1YAzOVGsRWzXcLgCA02QOuQ2sVlaciYiIqEVjcKZaczjDt2pYXbqi7AieqgGw4kxEREQtHoMz1ZrDHb5Vw6g4h/Q4Azo4s+JMRERELRiDM9Wawxm+VcNiVJwjtWqw4kxEREQtGIMz1VqkBVCsTicAwM6KMxEREcUhBmeqtUgLoJhcOjg7TGGCMyvORERE1MIxOFOtRZqqYVSUWXEmIiKieMTgTLUWqVXDG5wVe5yJiIgo/jA4U61FatXwBmdThHF0rDgTERFRC8bgTLUWsVXDU1Gu5lQNIiIiikMMzlRrRquGzVKLVg1WnImIiKiFY3CmWjMqzhZT+FaN6khTNRiciYiIqAVjcKZa87ZqBFeca2rV4JLbRERE1MIxOFOteVs1IkzVqFKsOBMREVH8YXCmWttvq4YK82fFijMRERG1cAzOVGsOlxtKAeYIwbkq0hxnVpyJiIioBWNwplpzuARWswlKBQVnT0U5bKsGK85ERETUwjE4U605XO7Q/mbAV3EO92fFijMRERG1cAzOVGsOlxuW4FUDgZpPDmTFmYiIiFq4BgvOSimzUmqxUirH8//uSqm5SqkNSqmvlVI2z+UJnv9v8Gzv1lD7QE3D4XKHrhoIeINxZbiTA1lxJiIiohauISvO/wGw2u//zwB4UUR6AdgH4FrP5dcC2Oe5/EXP9agFcbikxlaNStQwx1mkkfeOiIiIqHE0SHBWSh0E4HQA73n+rwAcB+A7z1U+BnCO5/OzPf+HZ/vxKuQsM2rO9teqETY4W606NLtcjbx3RERERI2joSrOLwG4B4Db8//WAIpExOn5/3YAnT2fdwaQCwCe7cWe6wdQSt2glFqglFpQWFjYQLtJDSFiq4bdDpfZAqc7TFXZZvPcmH3ORERE1DLVOzgrpc4AUCAiCxtgf7xE5B0RGSEiI9q2bduQd031ZIyjC93ggMtigd0VJjhbrfoj+5yJiIiohQoz/qDWRgE4Syl1GoBEAOkAXgaQqZSyeKrKBwHI81w/D0AXANuVUhYAGQD2NMB+UBPR4+jCt2q4zFY43e7Qbaw4ExERUQtX74qziNwrIgeJSDcAlwD4Q0QuAzANwAWeq10FYLzn8wme/8Oz/Q8RnjHWkuge5/CtGm6rFQ5nmODMijMRERG1cI05x3ksgDuVUhuge5jf91z+PoDWnsvvBDCuEfeBGoHDKbCGqzg7HHBZrHCEa9VgxZmIiIhauIZo1fASkT8B/On5fBOAw8JcpwrAhQ35dalpOdxupFrD/OkYFWe3GyISuCQ3K85ERETUwnHlQKq1mpbcFotVT50LnqzBijMRERG1cAzOVGsOp0Sc4+z2VJZDRtIZwZkVZyIiImqhGJyp1mpacls8wdnuCjpB0GjVYMWZiIiIWigGZ6o1h7vmVg0AcAafIMiKMxEREbVwDM5UazW1aognIDtYcSYiIqI4w+BMtRZNq0ZIcGbFmYiIiFo4BmeqtYjBOaDiHNSqwYozERERtXAMzlRrDpfAZgkfnI2A7GTFmYiIiOIMgzPVmsPlhsUUvscZVh2QOVWDiIiI4g2DM9WKiMDplog9zkjQwZlTNYiIiCjeMDhTrRi9y/tr1Yg4VYPBmYiIiFooBmeqFSMQR2rVUJFODuSS20RERNTCMThTrRjBOVKrhkrYzxxnVpyJiIiohWJwploxKsnWCK0aypYAAHC6I0zVYMWZiIiIWigGZ6oVo5Jsi7ByoLLpyrLdGWGOMyvORERE1EIxOFOt+HqcI1ScjakarDgTERFRnGFwplrx9jgHt2q4XIAITAkJAdfzMpsBpVhxJiIiohaLwZlqxTuOLrhVwxOITZ5WDUdwqwagq86sOBMREVELxeBMtRKxVcMIzkbFObhVA9B9zqw4ExERUQvF4Ey1ErFVw1NJ9gZnZ5jgzIozERERtWAMzlQr3nF0EVo1zInGOLowrRqsOBMREVELxuBMteIbRxe+VcPsmaphDz45EGDFmYiIiFo0BmeqFW+Pc8Tg7Kk4By+5DbDiTERERC0agzPVirGwSUirhtHjnJgApcKMowNYcSYiIqIWjcGZasVY2CRSqwasVljNJm8vdABWnImIiKgFY3CmWvFO1YgUnG02WE2KFWciIiKKOwzOVCvGwiaWCK0asNlgtZjgDBecWXEmIiKiFozBmWrFvp+pGrDZYDGZYA/XqsGKMxEREbVgDM5UK879tWpYrbCZFSvOREREFHcYnKlWjJP+Qlo1/CvOZhN7nImIiCjuMDhTrdgjVZz9e5zNCo5wKwfabKw4ExERUYvF4Ey14vQuub2fcXTOCK0arDgTERFRC8XgTLXicLlhNimYTZFbNaxmE5ysOBMREVGcYXCmWnG43LAEh2YgoFXDYo4wx5kVZyIiImrBGJypVuwud+goOiCk4hzx5EBWnOttzqY9WJ9fGrph+nQgJ6fpd4iIiOgAweBMteJ0CayWGoKz1QpbTUtus+Jcb+O+X4aXpq4P3fD008C4cU2/Q0RERAcIBmeqlYitGgHj6CLMcWbFuUGUVDmxtzzMz7GkBNi7t+l3iIiI6ADB4Ey1Yne5QydqAEHj6CKsHMgFUBpEWbUTRZVhKvclJcC+fU2/Q0RERAcIBmeqFadLYKupVcNi0XOcuQBKo7A73bA73SgJF5xLS4GqKqCysul3jIiI6ADA4Ey14nC5YQ1eNRDQwdlqBZTS4+giTdVwuQB3mG0UlfJqJwCgqCJM5b7Uc8Ig2zWIiIgaBYMz1YrucY7QqmGzAQAspggnB3q2s+pcd2We4Fxud8Huv8iMiG7VANiuQURE1EgYnKlW7DVN1fAEY5ulhjnOxnWpTozgDADF/u0aVVW6mg+w4kxERNRIGJypVpwuN2w1tWrAqDhH6HEGWHGuh/JIwbnUb64zgzMREVGjYHCmWnFEmqrhV3HWPc4RpmoY16U6Caw4+/0cjTYNgMGZiIiokTA4U63YXQJLpHF03uCsYGfFuVH4B+eiiggVZ/Y4ExERNQoGZ6qVaFo1rGYTnG5WnBsDWzWIiIhih8GZaiWaVg2LWcHlFriDwzMrzvVWVu3yfh6x4szgTERE1CgYnKlWHFG1aujtjuB5zaw411tZlV+rhn/F2ehxNpnYqkFERNRILLHeAWpZ7M4aFkDx63EGdMhO8P8LY8W53srtTiRaTbCZTSj2XwTFqDh37syKMxERUSNhcKZacbrdsEVq1fDrcQYQunogK871VlbtRGqCBck2S/ge565dGZyJiIgaCVs1qFYcLomix1lvD5mswYpzvZVVOZGSYEFGkjV8qwaDMxERUaNhcKZacTjdsIRr1fDrcTamboTMcmbFud7KPRXnzGRr6MmBqalAmzbscSYiImokDM5UK3bX/ls1LCbPyYGsODe4smpfxTmkVSMtDWjVCigq8i2/TURERA2GwZlqxenef6uG1WIEZ1acG1q53VdxLg5u1UhLA7Ky9P+LimKyf0RERPGMwZmi5nILXG7Zb6uG1WRM1WDFuaGVVengnJFkRVGF3Tcru7QUSE/3BWf2ORMRETU4BmeKmhGE91tx9k7VYMW5oZVVu5CSYEFmkg1uAcrsnrnO/q0aAPuciYiIGgGDM0XNWEZ7v+PoLJyq0Vj0yYFmZCTrn3WxcYKgEZxZcSYiImo0DM4UNYfTqDjvZwEUkzFVg3OcG5LT5Ualw4XUBCsykjzB2ehzLilhqwYREVEjY3CmqBmtGvtdcjvSyYGsONdLuV1PykhJMCPTE5yLIlWc2apBRETU4BicKWpG68X+x9Ht5+RAVpzrpLxa9zPrqRr6Z1lU6flZBvc4s+JMRETU4BicKWrGyX5WS1CrhkjYkwNDgrPRqsGKc52UeYJzimccHeCpONvtQHW1btWwWvVCKAzO1Ex8/NcW/O+XNaEbvvwSOO20pt8hIqJ6YHCmqHlbNUxBfzbGYhshwTlCqwYrznViBOfUREtgj3Npqb5CWpr+mJXF4EzNxncLt2P8kh2hG376CZg8GSgvb/J9IiKqq3oHZ6VUF6XUNKXUKqXUSqXUfzyXZymlpiil1ns+tvJcrpRSryilNiillimlhtV3H6hp2CONozOCsDc4e04OdAdVnC0W/ZEV5zrxb9VItJqRYDFFDs7scaZmwO0WbCgoQ0FplW/muGHlSv1x586m3zEiojpqiIqzE8BdIjIAwOEAblFKDQAwDsDvItIbwO+e/wPAqQB6e/7dAODNBtgHagJGq4YtuFXDCM7GODpPsLY7g4KzUvo6rDjXSVmVp1XDpt+AZCbrRVBQUqKvYATnVq1YcaZmYfu+SlQ6XHC4BPsq/B73Dgewbp3+nMGZiFqQegdnEdkpIos8n5cCWA2gM4CzAXzsudrHAM7xfH42gE9EmwMgUynVsb77QY0v4gIoIRVnzwIowRUmgMG5HoxWjbRET3BOsgVWnNPT9Ue2alAzsb6g1Pt5QWm1b8OGDb4jTzvCtHEQETVTDdrjrJTqBuAQAHMBtBcRo5SwC0B7z+edAeT63Wy757Lg+7pBKbVAKbWgsLCwIXeT6sgeqcfZeAH0BGdjSe6QkwON67BVo07K/U4OBOBZdputGtR8rcsv836eX1Ll22C0aQAMzkTUojRYcFZKpQL4HsDtIlLiv01EBECY8mNkIvKOiIwQkRFt27ZtqN2kenDUslUj5ORA4zqsONeJb6qGGQCQkWzVFedIrRpSq4ccUYNbn1/qPeehoMSv4rxypa91i60aRNSCNEhwVkpZoUPz5yLyg+fifKMFw/OxwHN5HoAufjc/yHMZNXPOWp4cyIpzwyqrdsFmNiHBooNzZnDF2b9Vw24HKipitKdE2vqCMhzSVc8WLyj1qzivWgV07w507syKMxG1KA0xVUMBeB/AahF5wW/TBABXeT6/CsB4v8uv9EzXOBxAsV9LBzVjEXucg1o1vD3O4YIzK851Vl7t9FabAX1yYMSpGgD7nCmmjIkagzplIDPZivzgivPAgUCnTqw4E+3H9n0VWJJbFLohPx/44YfQy6lRNUTFeRSAKwAcp5Ra4vl3GoD/AjhRKbUewAme/wPAJACbAGwA8C6AmxtgH6gJ2I0FUMwRWjWMHmfPyoH2cK0arDjXWVm109vfDOge50qHC46iYn1BcHBmnzPFkDFRo0/7VLRPS/T1OBsTNQYMADp2ZMWZaD+e/20d/vXpwtANb7wBnH8+UFzc9Dt1ALPs/yo1E5FZAFSEzceHub4AuKW+X5ea3n5bNTw9zkopWM2KFecGVlbtRKp/cPYsu23fWwRrUpJvTjaX3aZmwJio0bt9KtqlJ/imahgTNQYO1O1EU6bEcC+Jmr9teyuwq6QK1U6Xt1UPALBli/6YmwtkZMRk3w5EXDmQohbtODpAT95gj3PDKg8Kzpme1QMdxSW+ajPAVg1qFoyJGr3apaFdWiIKjIqzMVHDaNUoKeHqgUQ12FFUCQDIL64O3JDrGVC2bVsT79GBjcGZoma0XliCWzWCepwB3c7BqRoNK7hVIzNZB2dXpODMVg2KofX5peiQnoiMJCvaeyrObrfoEwOVAvr1060aAPuciSJwutzeNqc8T4D2MgIzg3OTYnCmqDk8KwHa9tOqAeiqNCvODaus2onUxMAeZwBwFxWz4kzNzrqCUvRunwoAaJeWAKfbs3rgypV6okZysq44AwzORBHkl1bDWEtsh39wdrtZcY4RBmeKmtMdfauG1WzyLtEdgBXnOiuvdiLV5t+qoX/eUlrqG0UHACkput+ZwZlixJio0budfkPXPj0RAPRkDWOiBuALzjxBkCgs/7AcEJwLCnyvpbm5oKbD4ExRc3inatQ8jg7Q7RysODes8mpX4FQNT6uGqaw0sOKsFJfdppjavq8SVQ43+hgVZ09wLtxb6puoAfhaNRicicIKCM7FfrPQ/avMrDg3KQZniprdaVScax5HB+h2DoebFeeG4nZLSKtGWoIFJgWYy8oCgzPAZbcpptbl+yZqALpVAwAqVq31TdQA9ASYhAS2ahBFYPQ192qXGlhxNsLyoEEMzk2MwZmi5nS7YTEp6DVv/ETqcXay4txQKhwuAECq3wIoJpNCepIVloqywFYNgBVniqn1Bb6JGgDQLl0HZzEmahgVZ6U4y5moBjuKKtEq2YqebVPCB+fRo4Ht2wGXKzY7eABicKaoOVwS2qYBhB9HZ1benugArDjXSXm1EwACWjUAPZLOVlkeWnFu1YrBmWLGf6IGACRYzMhMtiJh3Rodlvv39125UycGZ6IIdhRVoVNmEjplJmFHUSX0UhjQwTk1FRg8GHA69SqC1CQYnClqdqc7dBQdEGEcnYkrBzag0iodnFODgnOrBBMSqqvCt2owOFOM+E/UMLRPS0T6pnW+iRoGLrtNFNGOokp0ykxC58wklNtdKPG8FmDbNqBrV/3P+D81CQZniprD5Q4dRQdEaNVQ4Vs1WHGuE6PiHBycO5g8T6LhWjXY40wxEDxRw9AuPQHtcjf62jQMbNUgiiivqBKdMhLRKTMJgN/JggzOMcPgTFFz1qJVw2o2hW/VYMW5TiK1arSF52cfrlWjpIQ/a2pywRM1DB2SzOhUkOs7MdDA1QOJwiqpcqC0yolOmUnomKEn03iDc25uYHDmSLomw+BMUXO43LBaomvVsERq1WDFuU5KI1ScIwZnYxGUoqJG3jOiQMETNQx9y/JhdTnh7h9UceYiKERh7SzS4+eMVg3AE5wrK/Uc565dgYwMfcSRFecmw+BMUbO73LCaIlSclQLMvokPNrOCk3OcG0ykVo02Lv3E6k4JDClcdptiJXiihqFX4VYAQHGP3oE34CxnorCM6nKnzCS0SU2A1az0LOft2/UVjGpzly4Mzk2IwZmi5nC5I7dqWK06PHtYTBGW3GbFuU4iTtVwVwMAKhJTAm/AZbcpRoInahg679gMNxR2deoeeANWnInCMmY4d85Mgsmk0DFDT9bwhmQjOHftyuDchBicKWpOl4Rv1bDbA9o0AMBqibDktlFxljDbKKJIrRoZTh2cS6yJgTdo1Up/ZHCmJhZuogYAtN66AbmZ7bHLGfSyw2W3icLaUVQJi0mhrWcBoY4ZiYHBuUsX/bFrV/Y4NyEGZ4qaPVLF2eEIDc4mBXukijOg5042Iy9PXY8pq8LMwXzlFeDFF5t+h4KUVzthNikkWgN//hn2CgBAsTkoOLPiTDEQaaIGAKRtWof1rbugoKQqcENmJlcPJApjR1ElOmQkwmzSBavOmUnYUVSlg7NSQOfO+opdugCFhbr3mRodgzNFzVFTj7M18LCs1VxDxRloVn3ODpcbr0/bgE/nbA3d+NprwMsvN/1OBSmvdiHFZg5ZtTHNoZ8o91kiBGf2OFMTijRRAw4HLBvWY32bbBSUVAduU4qLoBCFYSx+YuiUmYRdJVVwb90KdOig33ACnKzRxBicKWq1adWwmFX4Hmfjes2oz3nrnnLYXW6s3lkSuKGyEti4Edi6NebTKUqrnCFtGgCQYtfBea85IXBDZqb+yIozNSHfRI2givOGDVAOB3Z06ob80qrQG3KWM1GIvKJK7zQNQAdnl1vg2LzVF5YBznJuYgzOFLWIJweGa9Uw13ByoHGbZmLtLj0FoLC0GoWlftWwNWsAYxb10qUx2DOf8monUhNDg3NiVTnsJgv2OYPe0FgsekQRgzM1Id9EjaCK86pVAIB93fsgP7jiDHD1QKIgLrcgv6QKnTJ9RxM7ej6XbRGCMyvOTYLBmaJmdwkskVo1QoKzgqOmVo1mVHFe66mSAQisOq9c6fs81sHZ7gyZqAEAiZUVKEtIRlFFmDciXD2QmlikiRpYuRJQCpU9+6CgNEJwZsWZyKuwtBpOtwS0anTOTAJEYM3LCwzOnTvrlidWnJsEgzNFzeFywxapVSNcj3O4lQON6zWj4Lw+vxRtUnWrwyr/4Lxihd7fNm2AJUtis3MekVo1zOVlqEhIQnFlhODMijM1oUgTNbByJdCtG1q1zQw9ORDQrRpcPZDIK89vhrOhY0YisipLYK6uCgzONpvueWZwbhIMzhQ1Z01znEN6nE1wuAQSPHauGZ4cuDa/FMO6ZqJzZlJgxXnFCqBvX2DYsNhXnKvDB2eUlqIqMRlF4YJzq1YMztRkapqogVWrgIED0S4tAYWl1XC7g54XOMuZKMAOvxnOhrREK3pXe57T/YOz8X8G5ybB4ExRc7gk6h5nm1lXpp3BL5DNrOJc5XBhy+5y9O2Qhv4d07FqR1CrxqBBwNChOkTHMOyXV4dv1UBJCaoTUyK3ajA4UxOpaaIG1q4FBg5E+/REON2CvRVBj3/Oco4LeUWV2LI7zFGD/Hxg3rym36EWzAjOHTMCJyYNcBbrT8IFZ/Y4NwkGZ4qanuMcXauGxROwQ04QbGYV502F5XAL0Kd9GgZ0TMPGwjJUOVxAaSmwZYsOzkOG6O9x7dqY7WdpDRVnR0oqiivDvBFhjzM1oZomasDhAAYMQPt03RKVH9yuYSy7zYpzi3bvD8txyxeLQjc8/jhw3HGAy9X0O9VC7SiqRFqiBWmJga+tvas8z+nG4icGY9ltLi7W6BicKWq1adWweoNz8644Gy/2fTukYUCndLjFc5lnCgAGDtQVZyBmfc4iUmOrhislteYeZz6RUhPY30QNDByItmm6ehZygiArznFhzc4SrC8ogyv4SOOKFbp/na0EUcsrqgpo0zB0LS9ElcWmz70J2NBVj1Dds6eJ9vDAxeBMUatNq4ZRmW7uFee1+aWwmBS6tU5B/47pAKDbNYyJGoMGAX366EHzMQrOVQ433IKIrRru1NTwrRqtWukVGsvKGn8n6YC3v4ka6N/fW3Hm6oHxp6TKgYLSatidbuTtC1rBbvVq/XHNmqbfsRZqR1FlwImBhg5FhchLb4sKR1D1niPpmgyDM0XN7nLDEqlVI0LFOWT1wGZWcV6fX4oebVNgs5jQpVUyUhMs+gTBFSuApCSge3c9E/ngg2N2gmBZtV6ePDXBHLqxtBSSlo6iSkfoiZhcPZCa0P4maiA5GW3TjFYNrh4YbzYW+N6gbyz0e7O+dy9QUKA/Z3CO2o7iyoAZzoasvbuwI62tXnrbHxdBaTIMzhQ1h8sNW6RWjeAeZ1PLqTj38fRkmkwK/Tqk6ZF0K1YA/fsDZk9YHTJEV5xj0PZgBOeQirPbDZSVQaWnwe50o8oR9LM2gjNPEKRG5vJM1OgT3N8M+E6yBZBgMaNVshUF4VYPZHBu0TZECs7+YZnBOSrl1U4UVTjCVpxT83dgR3pb78mDXkbPM4Nzo2Nwpqi43AIRRN3jbLNEODmwGVWcy6udyN1bGfBiP6BTOlbvLIX4vdgD0H3Ou3fH5FByubfiHBScy8sBEVgydItJSJ8zgzM1kdy9FahyuNE3ODgbJ9UOHOi9qH16YvjVAzt2ZKtGC7axsBxWs0JGkhUbC/0maxhtGp06xfQE65ZkZ3HoKDoAQHU1bAX54YNz27a63YnBudExOFNUjABcmyW39e2a7xxn42Qm/+Dcv2M6zEX7oHbsCHixx5Ah+mMM+pzLIgXnUn1io6VVJgCgKHiyRqtW+iODMzUy30SNoFaN9et1n73fm9B26YnhF0FhxblF21BQhm6tU9C7XWpoxTkhATjpJFaco5TnacMIqTjn5QEAdma0xY7ioMeQUhxJ10QYnCkqdm9wjnIcXaRWjWZUcfafqGEY0DEdfXZv1f/xrzgPHqw/xqDPuawqQquGJzjbMjMAIPQEQfY4UxOJOIpuxQr90e9NaLu0hMjLbnP1wBZrU2EZerVLRc+2qdjkH5xXr9YnWA8cqOc58/lov3aEWTUQgLeaXNmhc2jFGeAiKE2EwZmiYpzkF/U4ukitGs2o4rxuVykSLCZ0zUr2Xta3Qxr67vE88fgH54wMfaJgDCrO5XZPxTkxKDiX6MVaErP2E5xZcaZGtja/DAe1Sgo9KrJyJWAyAf36eS9qn66Dc8jqgZzl3GLZnW5s3VuBnm1T0aNtCnaX2VFkLHKzZo0+X8T4G2C7xn7tKKqESQHtPSfTenlCsbtLl/DB2ZjlTI2KwZmiUutWDVPzn+O8Nr8UvdqlwmzyVdETrWaMKNuBysSU0AHzQ4bEpuK8n1aN5NaZAICS4B7npCT9e2Fwpka23u8k2wArVgC9ewOJvukA7dIS4XIL9pQ3zOqBIoJv5udiT1mYKvbXX/vmSFOj2bqnHC63eCvOgO55RlUVsHmzDs1GcGa7xn7lFVWiQ3qidyExL08bhq1b18gV5x07mkVhKp4xOFNU7E4dnKMfR+dZcrsZV5zX55eFnswEYODeXGxs21X3jPkbOhRYt67JDyXvr1Ujpa2uLIf0OCvFZbep0TlcbmwsrGGihv+5AoBvlnPwZA0jONey4ryxsBz3fL8Mz/4aVMmsqgIuvxy44YZa3R/V3ga/xW96tjOCc5nucXe7dcW5WzddOGFw3q9IM5yxbRvQti3atWuFHcVVoSNIu3bVk594rkCjYnCmqBgV55BxdCI6BEdYctveTHuciysc2FVShT4dgl7sRXBQ3iYsb9UFxcGtD0OG6O/X6NtsIuXVTigFJFuD5jh7gnNSVibMJhV+ERQuu02NbMvucjhcgr4dgk4MrKzUy237tzxBnxwIAAXBkzWMVo1avugvzysCAPywOA+F/r3Ty5bpExNnzwbmzq3VfVLtGMG5R9sUdGmVBKtZYVNhuW+iRr9+eh5+795s1YjCzuKqyMG5a1d0ykyC3ekOPWrDkXRNgsGZouJ0R+hxNirHwePoIi2A0kwqzusKdOjsEzwFoKAASSX7sL5NNlbvKgncFqOlt8uqXUixWWAyBVXAPT3OKj0dmUnWmpfdJmok6/J1aOrdLuhN6Jo1utoYVHFu510EJczqgYmJtQ/O20tgNSvYnW58Omerb8PChfpjYiLwwgu1uk+qnY2FZeicmYRkmwUWswndWqfoivPq1frIV9+++or9+rHivB9ut2BnUc3BuWOGfvMZ0q7BRVCaBIMzRcVo1QiZqhEhOFsiLbndTCrOxhSAkMPLnmry2rbZeultf9nZ+iTBJu5zLqt2ICXCqoEAgPR0ZCRbURQuOLdqxeBMjWptfilMSh+mD+C/bL0fY/XAkMkaStVplvOKvGIM6pyBE/q3w2dztqLKWIp44UKgdWvgttuA774Dtmyp1f1S9DYUlqFH2xTv/3u29YykW7NGt2gkeUJgv376KEQzaNVrrnaXV8PucoeuGigSUHEGwgRnVpybBIMzRSXiyYFGAA5q1fDOcQ4+c964XqwrzrtKkWIzhw6Y97zY787urVcQ9KeUbwXBJlRe7Qo9MRDQwdlkApKSdMWZrRoUA+vzS5HdOgWJwa1EK1box3vv3gEXJ1jMyEqxhVacgVrPcna7BSt3FOPgzhm4bkwP7C2344dFetYtFi4Ehg8H/v1v/Th5+eXafmsUBbdbsLGgPOCNU892Kdi2pwKyenXARBX066fbZzZtisGetgzGUtqdMoJem4qKgLIyoGtX7+tWXvCy2ykp+s0iZzk3KgZnisqs9bsBAJ1bBT2YJ0/WH3v0CLjYqEw7nEEVZ7NZv4jFuOK8Nr8UvdunQQWfALhiBZCVhXa9u2J1cHAGdHBetkwfgm4iZdXO8MG5pARISwOUXq0r5ORAgK0a1Oj0svWpoRtWrtSH6IPeVAO6XSPs6oGdOtWq4rxpdznK7S4M6pyBkd2zMKhzOt6btQnuikr9WB4+HDjoIODii4H33tPhgxrUzpIqVDpc3mkagK44u1wuyNq1+sRAAydr7Nf+Zjija1dkJluRZDVjJ0fSxQSDM+1XcaUD787chBP6tw9sbXA6gUceAQ4+GDjjjIDbGBVnZ7iAabPFvOIcaaIGVqwABg3CgM4ZWJ9fFtpqMnSonqqxcWOT7Cegg3PIRA1AV5zT9XLbmcm2yD3OZWUxf6NC8anK4cKW3eU1PpbCaZeeiMLgqRqAbtWoRcV55Y5iAMCgThlQSuH6MT2wqbAcC3Km6+en4cP1Fe+6Sz8O3nsv6vum6Gz0m6hh6NE2FZ2LC2CqqgqsOBu9zgzOERnBOeRoqF9wVkqhY2YidhRzEZRYYHCm/Xp/5iaUVDlx54l9Ajd89pkeN/Too7qK7MfocbYHnxwI6ApUDIPc7rJq7Cm3h52ogZUrdXDumA67Z8xWAGPp7Sbscy6PVHEuLdUVZ0BXnMO1ahjLbjdQu8bsDbt9PaQBG2brVcHogLKpsBxuCbNiYFmZ7ikOOjHQ0L6minMtVg9cvr0YNovJu9T3aQd3RMeMRCwb/4e+ghGcDzkEOPZY3a7B/toGZUzU8K8492ibgl57tuv/+Fec09P1myNO1ogor6gSKTYz0pOCnvP9gjOgg3VIq4axvQUF52qnC5OW7wwt/LjdwJNPNssjpgzOVKO95Xa8P2szTju4AwZ0SvdtcDiAxx4Dhg0Dzjkn5Ha+qRrNr+K8bleEiRrbt+sX7YEDMaCj/l5DThAcOFC3mzRhn/N+WzUAZCZbUVrlDP15N+Cy2yvyinHZe3Px8V9bAjfs2aNDyX331ftrUMsSbtl6AL5FRyJWnBNQWFYNV/A5ELWc5bw8rxj9O6Z7j3BZzSZcPaobUlYshTMjU5/Qa7jrLv0Y//bbqO6borOhsAwZSVa0SfWdIJ6eaMXQcs/v0L/ibPyfFeeIjBnOIW2E27bp18527QDoHuiIi6AUF3unLjV3j/68Cjd/vghHPP07Hv15JXL3VugNzz0HPPAA8MMPsd3BMBicazJ/vg4FB7C3Z2xEhcOFO04IqjZ/+KFeEeqxx0IXCoFvjnNIqwMAt9UKR2WYd8plZUBhYYPsd03WGi/2ESZqYNAgdG+TApvFFNrnnJion/ibsOJcY6uGX8UZAEo8i6V4NeCy2znL9Avh9HVBv6OfftJvhH75RVft6YCxNr8UVrNCt9YpgRuMx1KkinO6Xj1wb/Ac2lrMctYnBpbg4M7pAZdfclhXDC7YhI1d+gY+N516qn7sPv88/04b0MaCMvRqlxoS9AaX7kRxaibQpk3gDYzgzN9BWDtqGkXXpYv36G6nzCQUllaj2hl0BNCYrNECThAcvyQPX8zdhksO7YJTBnbAp39vxdHPTsMLD78Pue8+4MILgWuvjfVuhmBwjqSqSldSR470DXE/wBSWVuOTv7bi7CGdAg/FVlcDTzyhfzannRb2tt6TA4NaNSYu24ldlW78sTwP5dV+Ie+PP/QTav/+jT42al1+GTKTrd6xWF7G+KyBA2Exm9CvQ1roZA1A9zk3YcW5vNqJ1MT99Tjr4BxyuKuBgrOIYOJyHWYWbNmHCrvf7+7rr/XHHTu4vPEBZn1+qfdNZoCVK/WbzKCThg3t0vSorZDJGrWoOG/ZU46yaicO7pwRcHm6cqPf7i34M7ULdvr3gJpMwB13AIsWATNm7Pf+o1HtdGHa2oLQFdzcbmDChAPi3IKNhWXo2TYl5PIee3KxMatz6M+mXz99BKwJiiQtUcRVA3NzfaEY8I6ryy8OanlqIbOcNxaW4b4fluPQbq3wxDmD8MLFQzFz7LG4bUgmLntxHLamtcNVR1yPhdua31QoBudIEhP17M+yMuDww4FJk2K9R03uzT83wu5y4z/B1eZ339UP4ieeCFttBgCrKbDiXFrlwF3fLMUtXywCrFZUlVfinu+WQaqqgHvuAU44QY/ScTqBCy7Qb1waybr8UvRpF2GiRocOepwPgP4d0rFqR0noE/+QIfqQbxMcjah2uuBwyX57nDOT9GHSooqgF+oG6nFetr0YuXsrcfbQTrC73JizyfO9FxbqNz1XXKH//+uv9fo61LLoiRoRTgwcMEC3NYXRzrPsdmHwLOdaVJyX53lODAwKzlixAmanE8s69MJHs7cEbrviCqBtW111bgBvTNuIqz+cjymrgvr7P/8cOPts4MUXG+TrNFdFFXbsLrOHzvAG0GHHFqxpdRB2lwU9J3GyRkRVDhf2lNvROXiGM+Cd4Wzo5B1JF2ERlGZcca60u3DL54uQYDXjlUsP8R6h7piWgNs/fgLtqkuw5Pl3sKHajCpH002wihaDc02OOEK3a/TsqadGPPfcAXN4aWdxJT6buxXnHdIZ3dv4VRMqK4GnngKOOgo4/viItzeZFMwmBadLsHDrXpz2ykz8uHg7bjuuFzq2TcegdslYM20e9hw8HHj2WeDGG3Ul6NNP9fzVW25plJ+1iGDdrlL0CV4eGPCeGGgY0Ckd+yocoScxGSsINkG7Rpmn9SLFFiaA+PU4Z3gqziGLoDRQxXni8p2wmhXuP70/Eq0mzFinxxPixx8Blwu4+279gvjbb/X6OtRyVNidyN1bGT44r1wZsU0D0K0aQP1WD1yRp08MDPn6nhUDWx99BL6Ytw1l/ke2kpKAm28Gfv653ieoFVc48MGszQCA16dt8L3BdjqBxx/Xn7/wAlBRUa+v05wZJ0/7nxgIANi9G4nF+7Ax66DQE6w5WSOiiKPonE4gLy9scA7pc+7YUb9hbcYV50cmrMSaXaV44aIh6Og/r/qZZ4BffoF66SWcc80ZmP5/x+DInq1jt6MRMDjvT5cuwMyZwPnnA//3f8DVV+tWhTj3+rQNcLsFtx0fuHgB3nxTH0Z9/PGI1WaDxaSQs2wHLnzrb4gA39x4BO48qS+UzYYe65dh8ie3Q+Vtx5q3PtX3m5ICnHmmPiHggw8aZXTUrpIqlFY7Q/ub3e6Q4NzfOEFwZ3HgdY3JGk3QrlFerfvXQnqcRQJaNYwe55BFUDIy9O+pHsFZRDBx2U6M6d0W7dISMbJ7a8xY7znM+vXX+oXw4IOBk08Gpk/Xb64o7q33LLUdElyLivSLfIQTAwGgbaqx7HaY1QOjnOW8PK8Y/TukhS7KtHAh0KoVzjtvDEqrnPhmflDl7eabgYQE4KWX9vs1avL+7M0orXbin0d2w9LtxZjpmXWPL7/U04buvBMoKIjrEXgbwoyiA+Btb9zYuktocO7SRb+B4WSNEN7FT4KD844d+jXKLzgby27vDB5JZzYDnTs32+D8w6Lt+HpBLm45tieO6dvOt2HGDP3af/HFwL/+BUCfKxVyZLgZYHCuQUmVQ/9RpqTogPDII8DHH+sJAnE8emv7vgp8PT8XFx3aBV2ykn0bysqA//5Xt1UcddR+78dmNmHLngqcc0hnTP7PGIzo5ql+2mxQeXkwHXsM/nXPh7isoH3gg/+RR3QIu/VWYN68Bv3e1nonagS92G/erAOfX5WsX0d9ndU7SwOv266dflffFBVnT7UsLbjHuapKV3q9rRoRepzNZh2e6xGcF+cWIa+oEmcM1ofRx/Rug02F5dixdjPw55/ARRfpwHPSSXq/Zs6s89eilmNtpIkafucKRGKzmJCVYkNBHWc5u92ClXkloW0agA7Ow4ZhaNdWGNY1E5/O2Qq3//SOdu2Ayy7TR7eKi0NvH4XiCgc+nLUZpw7qgHtP64eOGYl47Y8Nvmrz0KH6COVRRwH/+1/cFls2FpbDZjHhoFbJgRs81eTcDtnYWBA0WtBk0m+2WXEOEc0MZ0Oi1Yw2qbbGH0nXgEd+1+eX4v4fV2Bk96zAgQMFBcCll+qj+++8s9+iXKwxOEcgIjjuuel47td1+gKTCXj4YT3KaMkS4JJLYrp/jenV3zdAKYV/H9crcMNrr+meVuMw5H7854TeePOyYXjhoqFIS/RbPeyuu4B334Xll8l4+uaTUOVw4abPFvnODjabgS++0JWnCy5o0JNIjPFZIcHZeLH3q5KlJ1rRNSsZPy3Owyu/r8f3C7fj7417kLu3Au7Bg3Vbwv33A2+/rSdKrFoV9fzZaJV7TsILqTgbo4aCpmqEneVcz2W3Jy7bCZvZhBMGtAcAHN2nLQAg74MvdBXk4ov1FY8+Wo9LYrvGAWF9fikSLCZ0zQoKTX7TaWpSn9UDt+6tQGmYEwNhtwPLl3vnN19xRDY27y7HXxuDzke46Sb9WP300xq/TiTvz9qE0mon/nNCbyRYzLjxqB6Yt2UvNr70jq42P/ywfvF/4AFdff/44zp9neZuQ0EZerRJgdkUFHRWrwaSkpDYo3toxRngSLoI8ooqoZSvlckrTHAGgI41jaSrRY/z6p0lOObZafj07y2BG7Zv1yNnb7896vuKpMLuxM2fL0KyLbCvGW63Pvdgzx6dr9LTa76jZoDBOQKlFEb2yMKsDYWBJ4ddcIEOfjNm1LlaYRARbAr3pALoJ54mXNbZsGV3Ob5btB3/OKxrYO9RcbGunJx2mj5ZMgrXjemBUw/uGLrh4ouB664DlEKvdql47sIhWJJbhMd+9pvIkJUFfP+9Ds2XXKIrOQ1gXX4Z2qYloFWKLXCD8WI/YEDAxRcOPwhFlQ68MGUd7vp2KS59dw7G/G8a7rAdjN0OBXnmGX1Y6dRTdYUtNVVXmxpohqa3xzk4OJd6quCeJxmL2YS0BEuDL7vtdus2jaP7tkW6581Pr3ap6JiRiIwJP+ifl1FZTEkBxozhCYIHiLX5egxZSGhasUI/DoJe5IO1S08MX3Hu1Gm/FeeaTgyE3e4NzqcO6oisFBs+m7M18HojRgCHHqpbxGpZUSuqsOOD2Vtw2sEd0K+DfvxdclhXtE8yI+XZp/Xj/+yz9ZVPOAE47DDg6afjcuGVjYVl6BnmxECsWQP07YseHdKxaXeE4Lx5c6OeBN4S7SiqRNvUhNApNUZw9puqAejJGjUG5ygyxIq8Ylz67hxs2VOBxyeuxnpPcQlbt+piyJIleuGgegxIyN1bgUvemYMNhWV46ZKhvjcGe/bo88d++w145RVfG2Qzx+Bcg9G92iC/pDr0HfOJJ+o/yGnT6nX/z/+2Dsc9Px2/rAiqrnz4oQ4k//1vve6/Lv47eQ1sZhNuPrZn4IanntJVyyirzbVx6sEdcePRPfD53G34doHfu+Rhw/QL2x9/6MpuA1iXXxp5eeCuXUPe7f77+N6Yf/8JWPP4KZh29zH4/LqR+N/5g5Fw5eUYcd27+HNprn5SmzlTn0l///26heP99xtkf72tGpGCc5rve0lPsob2OAP1Cs6Ltu3DrpIqb5sGoN9UntZG0GvNYrguuDDwBiedpH+WtVg2mVqmdbsiPJaMEwP3c7i1fVoCCsJVnDt23O/qgSvzimEzRz4x0AjOiVYzLhxxEKaszg/tBb3pJn2UqJatRe/P2oyyamfA+R+JVjOerl6BDgXbsemWu33fu1F13rIF+PJLiAh+W7krtKUKAKZOrdUozmqnK3SKDqBfm5rg8VflcCF3b0XoiYGALvz064eebVOwfV9l6Gqj/frpNyzr1zf6frYUVQ4XVuwoCe1vdrmAv/7SE5JSA3/WnTJ1xTlk8lOXLvqN2q5dNX7Nxdv24dJ35yDFZsEPNx+JFJsZd327FM71G3Sb0d69+vExaBBw/fV1OnI5dVU+Tn9lJjbvLsdblw/HmN76iCXmztUrev7+O/DGG/r+WwgG5xqM7qUHt3tP+jAcfriurk2dWuf7/nnpDrw2bQNMCnj1D78zsv/+W1cwTSY9yqiBD/3X5O+Ne/DLyl24+Zie3jmrAICNG/WJNFddpcNsI/i/k/riyJ6t8fCEldhV7FeF+Oc/9cSN//0PmDOnXl+jqMKO9fll3uV5vaqq9PSUGg4tJ1rN6N4mBaN6tcFFh3bBk+cejA7piXhn9lb9JDV6NPCPf+gRfUcfrX9eDVBhMmZd769VA9CznMO+ILdqVedWjZxlO5FgMeH4/u0DLj9301yYIFhz1CmBNzj5ZP2R7RpxrbjSgV0lVaFLbQP6jdN+2jQAoEtWMvJLq1BaFfQ32727/ljDyWPL84rRr2NaaGVu4ULd0+83P/qyw7LhFsGX84IOXV98sX5svPHGfvfVUFRhx4dB1WYAgNOJY755G2s69MRTlqATqs84AxgyBPLUU7j/u6W44dOFeGpi0NoATz+tCzLGWMcoPDJhJUY/Mw0r8oKOfD73nH5OGj8+5DYhAcu3Ieqva9iyRy+3HnJiYEWFrlb274+ebVMhAmzeHfQ6xpF0AQpLq3HJO3OwZlcJ/nGY35Ga8nLgvPP0FJhbbgm5Xb8OaSi3u7A4tyhww8EH649TpkT8mgu27MUV789DVooN3/zrCAzr2gpPnHMwSpetQtWRo/U5TX/8oV/bPv5Y9yHfdlvU35PT5cZ/J6/BdZ8sQJesZEz89xicPLCD/lt79VV9dNJsBmbP1m9im3lfsz8G5xp0yUpGdutkzN4QFJxtNh2O6hicV+QV4/++W4oR2a3w2NmDsHJHiV6Nbft24Nxz9ZPeTz8Bu3c32RnZLrfgsZxV6JyZhOuPClq04J57AKtVV50bicVswn/PGwynW/DfyUEvKs8+q6tQt91W5/aVncWVuPCtv+FyC073bx8pKdFtFhs2AJdfHvX9Wc0mXDO6G/7etAfLtwe9cN11l65Cf/ddnfbVX1mk4BzUqgHo4JxXVBm6jHFWlq48lEVoC4rA5RZMWr4Tx/ZtFzJHut+MyVjTtht+c2cF3ujgg4H27dmuEefWe08MDApNBQW6vaqGEwMNw7q2ggiweFtR4IYjjtAfZ88OezsRwYq84vAnBi5apN/c+70Id22djKP7tMVX87YFrmSanKzfmP/ww34rc4b3Zupq83+OD5pt/8UXMG3cgA033YWpawqxaodfq5ZScI4dB7V2LYo/+wpdspLw05I8vWqiCDBunF6uvmdPYNYs/T3sR5XDhZylO1FW7cQ/P5yPbXv8Rt59+ql+nrzkkoCf4adztuKwp373zWA3/Pyznl9fyz5sY6JGyOIn69bp76tfP281OuSobW/PmwtO1sC6/FKc+8ZsrNlVgjcvG46LDvW0Y+zYoau+OTm6jSHM0d4zBndCWoIFH/+1JXDD6NH6qPWrr4Z9U/T3xj248oN5aJeWgK9vOMJ7MuLp1iL89O19qK6owsZvcnRFGNCPqfvvBz77TGeT/SgoqcI/3puLt6ZvxKWHdcX3Nx2Jrq2T9evtxRfr1/JTTtF/6yNG1ObH1SwwOO/H6F5tMGfT3tClo084QT/o/Rrw35+1Gcc9/2do0P75Zz0e5pNPUFhShes/WYCsZBvevHw4LhrRBR0zEvHur6v0SoUVFXrFqTPP1A+aZ59tktWnvl2Qi9U7SzDu1H5ItPrNDP7zT/3Ccu+9vlW9GknX1sm4YUwP/LRkBxZs8WstSEvTFef58+t0ks2GgjKc/8Zf2FlchY+uOdQ33SM/HzjmGP1i9dln+qzeWrjksK5ITbDg3ZmbAjecfro+a7wB5n4bwTlkAZQwrRonDeiANbtKcfe3SwPD89ln69B82mm+20VhwZa9KCitxumDg/rU8/Jg+Ws2Fow8wTeWzmAy6XaNKVNi0qNvqHa68PD4FZgbHBJE9AvAN9/EZsfixLpIo+jCnGQbydCumTCbFBZsDToa0qWL/hchOG/bW4GSqjAnBjocwLJl3jYNf1ccno2C0urQhUr+9S99uyhaq/aV2/HRX1tw+sEdAyeJ+E3SGHP3tUhLsOD1Pzd4N1faXbi+Ihsbsw7CoyvH4/0rR6Da6cZXf2/WlbZnntEf58/XRzJffnm/+zJz/W6UVjvx4BkD4HS7ceUHc7G7rFq3nqxYoU9O7NoVOPNMyMqVeHHKOjz40woUVzhw02cLfUH7o490saa4WB8qr0XbyoaCMigF9GgTfhQd+vdH9zYpUAqhkzWSk4Hs7DpVnDcVlgVOSTFs3NjieqZnri/E+W/8hWqnG1/fcAROGdRBb1i6VK/Mu26dzgP//nfY26ckWHDBiIMwafnOwPMFlNJTqRYuDDlSO2v9blz90Tx0zkzCVzcejg6esXZYvhw4+mikJVjxr2ufxy3LHbA7/Z7D779fB+kbb9RFvQiW5BbhtFdmYfn2Yrx48RA8fd7BOlNs2qTPK/jhB/03/9NPvgW6WhgG5/0Y3asNyqqdWBp8KOTEE/VHT9VZRPDRX5uxqbAcl703F4/+vNLX1/X66/rd41VXYePRpwCFhXjnyhFom6ZPArhhTHdc+PYjkEWLdJ+scYLafffpM7LreOZ3tEqrHHjut7UYkd0qoJcVLpc+mzY7W88kbQI3H9sTHdIT8fCElYHh77LLdCVq3LhanZS5JLcIF771F+wuN7664XAc2VO332DTJmDUKP3m5+ef9f3XUnqiFZce1gUTl+/E9n1+FR+TSVedFy3SbzzqobzaiSSrOfQErDCtGlcd2Q13ndgHPy7Owx1fL4HTeLN38sl6Sslff+nqepQnLuYs24lEqwnH928XuMFTSbefez6W5haF9lWffLI+6SOKylljeevPTfj4763454fzA9+EffmlPnJyySX6c4qshjd96/JLkWIzh47NMk6yjaLinJpgQf+OaYG/H8Po0foNbZh9ME4MDAnOK1fqsW9hgvMxfduhc2ZS6EmCffroIsjbb+vnuxq8N2tTSG8zAP2cvWED8MgjyEi24cojszFp+U5sKChDSZUDV30wD39u2IuCW+9Em41r0Gf+dBzdLRO97rpJf91779WvEa1a6Qr4V1/td9zpxGU7kJlsxZVHZOP9qw7FrpIqXPPRfNi//FqHphtvBH79FZKQgJKjj8fXP/6NC4YfhEn/GQMBcO3H81H11H/1ugTHHad7jbt3120BmzfX+LUNGwvL0TkzCUnBizOtWaOfA3v3RpLnb6ShJmuMX5KH456fjmd/C6pUz5ql76+WxY/6Mk6evumzhaE99L//rtd/MPrug3w5bxv++eF8dG6VhJ9uGYUhXTL1hkmT9N+/iP6+Tj+9xn248ohucLgEX8wNGj93xRW6benVV70XrcsvxTUfz0e31in46obDfS2Zu3frBc1sNphmTMcN/zoDa3aV4tU//HrQrVZduNq3T7/RC/PYdLkF93y3FAkWE8bfOgrnHnKQb+Mdd+gjO3/8oY9im1pw/BSRZv9v+PDhEitF5XbpNi5HXvhtbeAGt1ukfXuRSy8VEZHF2/ZJ9tgc+eTvLfLw+BWSPTZHjntumqxYsl7EbBb3//2f5Fx+h1SZLVLZuq3Izz9778r+5NMigHx73k2hX2PYMJHevUWczkb7Hp+atEqyx+bI0tx9gRvefVcEEPn660b72uGMX5In2WNz5PM5WwM3LFggopTInXdGdT8z1hVI/wcny+hnfpfNhWW+DUuXinToIJKVJfL33/Xa17x9FdLz3ony2M8rAzdUVoq0aydy2mn1uv9x3y+V4Y9PCd3w7LP6d1NcHLLp9WnrJXtsjtz8+UKxO12+Dd9+K2KxiBx+uEhRUY1f1+lyy/DHf5ObP1sYuvGII0SGDpX5m/dI9tgcmbhsR+D2/Hy9b08+Gc232OA2FpRK7/snydUfzpNjn50mAx/6RZZs2yeyd6/+nYwYIXLMMSJms8gPP8RkH5uLuZv2yKCHf5G1u0oCN2zcKJKZGfA85e+St/+Ws1+bFbrhxhtFWrXSz11ReHj8Cun/4OTAv1MRkdde039DW7aE3OapSauk932TpNoRdJv33tO3Wbcu7Nd67Q/9uFifXxq44fvv9e3Gj4+4n3vLqmXAg5NDHw8Oh0jPniKHHOL9nneXVkm/BybLdR/Pl9NfmSE9750o45fkidjtIt27i4wYIYVHnyACyMr/3Bd4f2vW6H159NGI+1Jpd8qAByfL2O+Wei+bumqX9Lh3ouR26i6uo44WEZEqh1OefPJLKbYlS0F2L3Hv2SMiIrPXF8g7I88TAcR10cUiVVX6Ttau1b/zQYNESkqCv2yIU16aIVd9MDd0w4UX6p+JxxXvz5XTXp4Rer3//EckJSXqv5Xl24uk7wOTpPf9k6TXfRNlQ4Hn97h9u34tTkzUP7vJk6O6v/pwu93y++pdcupLMyR7bI5kj82RZ39ZozfOni1y7LF6XwCRCy4Iuf3zv66R7LE5cuX7c6Wk0u7b8MEHIiaT/nvavj3q/bnqg7ky4okpoY+J22/Xz/k7dojb7ZbL35sjBz/8ixSWVgVe79Zb9fPhsmXei+78eon0uHeifu7099RT+vv68suQ/fhp8XbJHpsjE5bkBW6YO1ff5vHHo/6eYg3AAomQSVtw5G8aGclWDO6cEdp+oZSuVEydCrjd+HnpDtjMJpw9tBMeOWsgPrt2JCrsLnw59iXA5cKPA47BLZ2Px5evfo/Ezh11K8b11wPffAPrA/dh/bGn4+5ep2HljuLAr3Hvvboa8P33jfL9bd1Tjg9nbcEFww/C4IMyfRtKSvShmVGjgAsvjHj7xnDm4I44rHsWnv11TWA1c/hwPcbulVd8hwMj+HnpDlzz0Xx0zUrG9/86Et2MZcNnztQtMBaL/jzK0XqRdMpMwhmDO+KredsCT8xLTNSHyiZN0odPw6h2urAktwg/Lt4eeNa506kP1X36KVBQGLr4CeBruQg6yxoAbj6mF+47rR8mLtuJ275c7GszuuAC3aKwYIFupygqivh9zd20B7vL7IFHIADdu/3338BFF2FIl0ykJVgwY11Qu0a7dvqQXgz6nEUED/y0AgkWE/573sH4/PqRaJVixZUfzMO+/9ylKyvvvKMPfx52mO63mzy5SfZtY2EZxn2/LPDkV0BXYW6+uUEq9HlFlfgr+LkK0FWiH38MqRI9/9talFY58eW8oGrV00/rv48IJ86tLyhFn+CTbAHfiYFRnugzolsrVNhdWL0z6CjIqFH6Y5h2jRV5xejbIcKJgenpulc4jIsP7QKrWeHzuUFV57PO0m1ob74ZcT+/mp+LcrsrtNr888+6RcCY2wygdWoCLhvZFVNW5WN9fhnevXIEzhrSSVfsxo0DFixA6xm/4/nz7sD9fU4LvL++ffVRoTffjNiiN31dIcrtroAWquP7t8drg204aMdm/NDrSJRWOXDNR/PxTkkaZjzzDtru3AZ1zjlAaSmOfOL/cP3cH/DxsNPx38vu16soArr6/u23+rn1sstqrMC73XqUatiJGmvWAP37e//bs20KNhWWh7ZX9OsHlJfjjc+moyT4BNHcXL1Sr2cM295yO278dCFaJdsw4dZRSLSa8ciElZCqKl3VLSvTR9T69NFtDY244MxfG3bj/Df/wjUfLUBZtRMvXDQEY3q3wYqJf0JOO13/7a5cqVtubrhB/434Pddu31eBV6dtwDlDO+H9q0b41jiorNRHdkeP1uNuO3eOep+uOrIbCkurMTl4Qtctt+jf49tv4891hZi5fjduO7432nhW7gSgf99vvqn31TipEMBDZw5A29QE3PXt0sDXp//7P/3cecstAecGOF1uvDR1Pfp1SAs8jwgAHnoIaN0a+M9/ov6emrVIibo5/YtlxVlE5H+/rJYe904MfGcoIvLRR/pd++IlctiTU+T6j+cHbC6qsMv6/sNlXesukn3Pz3LtR/PF5XLrd/j33qvfWQIihxwiRbuLZNBDv8jNnwdVNJxOkb59RYYMifqdeW3c8Ml86f/gZNlVXBm44Z579L7Nnx/+ho1sZV6xdB+XIw+PXxG4oaBAJCND5KSTwv48XC63vDJ1nXQblyMXvvWXFFX4/c4++kgkIUH/PLduDbltXa3IK5LssTny5p8bAjcUFookJYlce62I6Erodwty5cGflstZr86UXvdNlOyxOXL4TR/K9Mde1ZX00aP1bTzVit9OuFhOfyVMteaOO0RSU2vcr3dnbJTssTly/cfzAysR48eLWK268rp3b9jb3vvDMun/4GSpqA460vH883rfNujv9YZP5suRT/8u7uDfxdixIhaL2PcViSO4migi4nL5Kl0N6IdFud4jP4Zte8rl2htfEQFk3423+K68b58+opOYKPL77w2+L/6+X5gr/R+cLNljc+R5/6NXU6boKjggcvHF9f46d3+jq0Tr84Mqhk/ro1r+FeS5m/QRg0EP/yLDHvvNV/XdulX/fWRk6OeoHYFHFApLqyR7bI68O2Nj4Ndwu3XF8l//inp/dxZVSvbYHHl/5qbADU6nSFqayE2BR+HcbrcMfuRXGff9Mglx2GH6SEIN/v3FIhn08C9SXu0I3PDIIwF/18Gu/nCeHP/8n6Eb7rhD//3YA18bCkqq5PqP58ucjbsDr19VJfKPf4h88418MGuTZI/NkcXBFb1fftH78umnEb+HoY/+Gvq4eughcZlMMuKWT+WQx36THvdOlO8W5OptX3+tj9a1aeOtaD/04zLJHpsjX8/fFng/RrX/nnvCfn0R/ZjKHpsjX8wNeh51OvVz7N13ey/69O8tkj02R/L2VQRcdfWX40UA+cfFT8hxz02TTcZRwbVrRbp00fvQrp04ps+Qf7z7t/S+f5K3+vmh52e39fzL9PW++07f9tdfG+VoV1GFXX5avF0ufedv/Zz91FT5fM5WsZeUiowfL9uPP00EEEdGpn6slXm+l/nz9f688473vl74ba10G5cjuXvLA7/IZ5/p69bhucjlcssxz06Tc14PcxTotNPE3b69nPzMFDnm2WmhVenTThNJT9evrUH+XFsg2WNz5PHgI6qrV+u/+9NP974Ofz1/m2SPzZFfV+wMvO6MGfr7+t//av19xRJYca6fUb3awOUWzN0U1It3wgkAgNxvxiO/pBpnDgk8eS5jbwF6rVkEufhiXDiiC168eAhMJqXf4T/1lH5XefnlwPjxyGidgSuO0L1xAYuimM26SrF0aYNXxv7auBu/rszHLcf2ClypyBg/989/xuyM1wGd0nHZyGx8Omcr1uzyq0a1bQs89pgedzZhQsBtSqsc+NdnC/H8lHU4Z2hnfHLNYXpFPYdDVyH++U9dDZg1a7+LM9TGwE4ZGN2rDT6cvTnwZIo2bYCrr4Z8+ime/mAajnt+Ou76dim+X7gdSTYzrjkyG7+Vz8Sst67FUQ/9W/c5ulz6nf/nnwNHHIHeqxcixRam4lxSEtDfHM51Y3rgkTMH4LdV+fjXZwtR4VmFEGedpU/QWLZM97UFTRRwutz4ZcUunNC/fWj/4tdf68q/p6p3VJ+2yCuqxMbCoJN/Tj4ZcDrxwG2v4rL35gaeXLt1q/67GjSoXisaBiuqsOOJnNUY2iUTl/mNdOqSZsXrM97GrvS2OLvdydhijMbKzNR/R7166Z9JhJPR6qO82om7vlmKO79ZikGdMjCwU7o+Qc3p1PN9TzpJ/52cfLJ+fNfzRODVu0rgcguemhTUO2osD/9//+cdk/jqH+vRJtWGp849GHvK7Zi+1nPk4Jln9MfvvtMneH7xRcBdrYu01PaOHbqyFsWJgYYOGYk4qFUSFmwNem41m/U5DUG/k9y9lSiudGBQ56DVxRwO/T2G6W/2d8UR2SitcuLnpUFzjq+7Tn/Nt98OuY2IYPG2fTjE6EH1N2eO/lu2WgMubpuWgHeuHIGRPVoHXj8hQT+2L7wQFww/CKkJFnw4O6in+KSTdDX25ZdDjhBUOVyYujofpwzq6Ft5Te8k8M03UEcfjdNPOgSVdhfevXI4zh/u6TG96CJ9f8XFurr40EN48MyBGNO7De7/cTnmbfb7+d98sz5p8n//Az75JNyPERs8r1Eho+i2bNHV3oCKc+hkjT/XFuD6OfrvaGx3hb3ldpz92iwsGu8Zf1ZVpZ9r0tOhjj8OnX74Ek+cM8jbB3z54dm4c/Of6Pr953DcM1ZXnY2f3Xnn6bGg9VxyOndvBT6YtRn/eHcOhj8+Bf/5agk2FJTh6SPaYkbWRvzjyVthbdcWOPtsdFowG2+MuRSPv/yzfr1O8RzhHD5c/y495ym53IJvF+RiTO+2ocuUv/eeHqN4zDG13leTSeHKI7KxeFsRlm0vCtz4739D5eej78xfcO+p/QKP1Pz2mz4q+sAD+rU1yNF92uKKw7Px3qzN+GONX999v376eWLiRODll2F3uvHy1PUYfFAGThzgN7pUBHjwQT1pKcw4vRYrUqJuTv9iXXGucjil7wOTQqufIiL9+8vaQ0ZJvwcmh1YxXnxRv9Nasyaqr1NYWiV97p8k//ftksANdrtI165iP/wIee33dfLujI2yeNs+X4WorEzkpZdEnnhCZNo0kfLykPsO5nS55eQXp8uRT/8ulfagquJ55+nes7y88DduInvLqmXIo7/KJW//HVjRtNtFBg7U/YKVulK+Pr9UjntumvS4d6J8MGuT7/q7domMGaN/D3fdpXsSG4Hxztxb4fHYsWCZuJSSV464WB7/eaWs3VUiTpdbV3rPOEMEkIVHniz/uOkNcVdXB97pY4+JSym57bUwPc4XXaQr51H49O8t0n1cjpz60gzZUeRX9Zk0SSQ5WVd3lui/uYpqp9z1zZLQyoHTKfLQQyGVA6Py9MGswIrhz/M2SZk1Ub4+7EzJHpsjT01cpTdMn66rXhkZuvfurLMa7EjK2O+WSo97J8rKvKC+7//9TwSQ7R98IUMf/VWOfPr3wGrPzp0iffroqsu8eSH3W1blkDU7Pb83f1VVul8+QuV81Y5iOe65adJtXI48/+sacThd8s70jTLypo+k6vAj9c/y2mv143XCBP3/336r8/fvcLqk9/2TZPjjv0n22ByZua7Qt3HAAF9l+/XXZdHWvZI9Nkfenr5B7E6XDHvsN7npswX6MZ+QIHL99fp2I0eKDB4c8HU+mr1ZssfmhB6lMqp906bVar9v/2qxHPrElNCjFo89pquk+/Z5L8pZuiP8+RhLluiv/cUXNX4tt9stJ70wXU5/ZUbo1zv/fJHWrb3PKYbNhWXhz7moqgqprtbWIxNWSM97J4b+LN94Q38/s2cHXDx5uf7+Z60vDLz+smX6+m++qXfNEeGcmKC/1aJyuxz77DQ55LHfZNz3S+X+H5fJw+NXyJM/LpHNQ0aKw2qV3HMuEcd1N+g+2NtvF7n7bll8+U1y++l3yt5tQdXFn38O2e/8En1U4UPPc8SEJXnS676JctpL08WVni5y662ybU+53HHrq1KSkCyl7TqKe/Vq/f1OXyEzsofq+7z9dt/z919/ictqlT+7D5P/5QS9Lm/dqo/anX9++J/BfkxatkNOfnG6t3f5hOf/lP9OXi2rv50k7sMP9x4NlOxskdtuE5k6VaS6Wu78eokMeuiX0NfTJ5/U19+0Sf5Ykx/+vJB16/R1nnqqTvssIlJSaZcBD06WO75eHHB5UVmVbGndWdZ1Hxj4N+9w6H727t1rPPpXaXfKKS/NkKGP/hr4+uF2i5x9tojVKjkfTJDssTkybU1+4I2nTtXf1yuv1Pn7ihXUUHGOeSiO5l+sg7OIPsEh3KE61623SoU1QW7/eE7ojUaO1E3+tfDweP1Eut3vsFZJpV1+v+VBEUAu/Md/vQ/oQePGyweX3Cllrdr4HsyADiQjR+qg+OOPYU8g+2reVskemyM5S4MewNOn6/t44ola7Xdj+cRzmC/kicZ4QD75pPy6YqcMfEgfbv7b/9DonDkinTvrJ9H9vKDWl/GCfPKL071PTn+uLZAhj/4qv/UbJdXpmb7Dd4sW6Scri0Xk1Vflyzn6e/Se7GLw/C7euevF0C946qm61SJKf6zOl4EP/SIjnpgSeLLHokX6Z5SSInkffyUnvTBduo3Lked+XeN7kt2xw3eyyz//GRIsjnl2mvzTc5KQ0+WW/05eLdljc2TBoCPF2bOn3O85JLzqoWf099y3rz4ca7yxfO65qL+PSOZ5TlR80gjohs2b9ZuDs84SEX2C0aCHf5FDn5gSGL5yc/XvJDU1oJ1h9c5iOfLp3yV7bI4MeHCyXP7eHHlxylqZN3OZOIcN0/ufkCAyapS47rlHyr79QXZszJVP/t4ive+fJCOemCKz1+brx2Burux6/1PZm5gm9qQUfWjWUFGh/05vvbXOP4P1+aXegDfqv7/LyS9O12G/qkqf+HPffSJHHy3Spo3c/PofMvTRX6WsSgeRRyaskN73TZLKW/6tr7vR04bx+uv6e1y82Pt17v1hmQx+5NfQ4Gm08YQ55FsT41D+1t1Bb/h//12CT/Z6etJq6XXfxNBg+P77+rprg07iDsN4TglpkTCeUz75JOBio/1n1Y6g51HjhCejTaAOtuwu8z7eApSW6jeXF10UcPEtny+UYY/9Ftqm8cADuq0mPyi4RGFjQamc9vIMOfSJKTLssd9k8CO/yoAHJ8uhd34ts7sOlh2praUwJVNKUzLEnpIq7qQkcZrM+ns3m0WOP163d2zf7n2TKp4TEUX0c+Ogh3+RB35cLl/M3arb6N78S4or7bq95oQTRCZPFndSkuzs0FWOuOkDuefbpbI0d5/0e2CyXPTadHHe+m99vyedJLJqlUjHjiI9esj97/8pve6bKBuDnzufeEIEkM1f/ijnvzFbrv1onuws8nve2rJF5Mwz9YnOy5eLiH7uenqSfu465aUZ8u6MjfqkcrdbPw4sFv0c8fjj+g1z0N//rPWFkj02R35eGlRw2rJF7/tjj8mNnyyQYY/9FtouMW6c/lnWs1j14E/Lpfd9kwJO/nsiZ6U8csINeh/8CwNvv60v+/bb/d7vxoJS6f/gZLnwzb8C//b27BF3ly6Sm9VRrng+6DnB7dYnoh90UMhrRkvQLIMzgFMArAWwAcC4mq7bHILz29M3SPbYnMAHn4gsf+MTEUDmffB94A02bdI/3meeqdXX2e6Z0vDw+BVSaXfK29M3yNBHf5U+d34vxelZUnbsCbJrT6kseexF2deukwggf3cZJOde9qyuTObk6AfhmDH6BR3Q1Sa/SqvD6ZIxz/whZ706M/TF78QT9cSJigppDpwut5zy0gwZ9NAvctFbf8ltXy6SpyaukvdnbpKdx58mDqtV5nfuLzMPOVZKb7pVT5v44gsdymw2/UTnqaY2tm8X5Hrfdb80RfdZn/TCdMmb5AkAr76qz5pOTNRh9a+/RERkU6SKVmWlVFmsMuPMK0O/2KhROszWwtpdJTLqv79Ln/snBT655+XJ3gGDxQUlL514nUz3rxpMnarPWE9KEvnww7D3+9BPy6XfA5OlsLRK/vnBXMkemyP3/rBMHC++LAJI1fKVMmnUWSKAVJx0im+ih9stcu65+gVjVpjevChVO1xy4gt/ypFP/x541Mft1j14KSkBPe1GGO77wCSZ5P+GbPt23fOslMgLL8iUFTt1gHhiinzy9xZ54MflcvKL0+Wsq16QXalZUmpLklfOvFk+HXWBLO7cT6pNFu+b19z0drIns6240tIC39QCsr5TL7nr8W9Cv5Gzzxbp2rXOFXijGrt8e5FM8Eym+XreNh16AZGvvhJZuFDcSsmbI8+X1/5Y773t8u1FMuzWz8SRkChy1VW+O92zR/c733GH96Lz35gtF775V+gOXH21/luppTU7SyR7bI58vzDwaI2Ulem/jfvv9150+Xtzwk9ouOUW3RPtCtNPH6S0yiEDHpwsd38T9Lzgdus3daNGBVz84E/Lpf+Dk0OPOLys/75rM/0gnGs/mifDHvsttFJ51136+8/VP5eKaqf0e2Cy3PdDUH+3262PmBx/fL32Ixy70yWz1hfKQz8tl8OfmirZY3Okx70Tpf/9E2XcPe/o15q+fX1/32lp+shGkLNfmyUHP/yLZI/NkX9+MNd37sSVV+rHp9Uqcsgh4tq5S57zTJzoPi5HRj45VfJLPK+5776rr2cy6TfDS5dKQUmVDHroF7n8vTkBr2WVpeWyp1NX2ZB1kBz20ETp98BkOfjhX+SH+VvE/dJL+mumpOijXzabVDz2pFz57l/e5y5vsK2q0keFAP1c4nf0I5jT5ZaRT06Vqz8MPWolRx8tjp69pOe4MG/u7Xb9mut5c18fxpvnV6bqyTJbdpdJr/smygMfz9JFgSuu0FcsLta/p9Gjo36+Md5APh/0Jm/CG9+KQ5mk8LSzA+9r4kT9c3v77Xp/X7HQ7IIzADOAjQB6ALABWApgQKTrN4fgvDKvWLLH5si3QYfi7/toljiUSRz33ht4A+NknM2ba/217v5mifS5f5Ic9uQUyR6bI1e8P1dXx4z77NlTfxwxQuS336S00u59p7xoq9/JXlVV+qSEoCqKMTLml+Am/gUL6hT2G9u6XSXy7y8WyQVvzpbRz/wuve+bJNljc2TELZ/Il4NPkvUDDxVX7z76idA/pJx4osju3fv/Ag2k2uGSw56cIn3u1/t3+1eLfUHuyCN945KOPTagMuR2u2XEE1Pkti8Xhdzn3K6DZEfvQaFfbPBgHbRqaXdplZz/xmzJHpsjL01ZJxXVThn73VLpe+d3Mnv48Xr/rr5av3F65BEdIvv3F1kRpk3JY8rKXZI9NkcOeew36XnvRPnUODHPGK3lOSHpndEXy9kv/xlYLdy3T6RHD/1GopaVSoMxfm/qql2BG777Tn/9558PuU1BSZWc8/osyR6bI6/9sd73oltWJu7z9Kiuz4eeIme/OC3wzfKXX4o7MVEqOneVj94aL7d8vlDGfrdUHv95pbySs1R+fuVLWXrzPbLr9HPFfc01OnA+/LDeh3ffFfnhB/nfT0uk570TA09cFfFVTf2qu7Xx3K9rpMe9E6XS7hS32y3nvD5LRjwxRare/1Df70p9gs/sUadLtdkqpWt8Y9vcbrd8feyl4lSm0KrteefpF1m7XcqrHTLooV9Cw5vLpdunjjuu1vvtcrnl4Id/CX/CnzE60LOPQx79NWAMm9fhh4scdVTUX/O2LxfJ8MfDtIcYf/N+FdMzXpkpl7wdZmzlpZfqalo9GZXKb4JP0tu8WYdEz2vLxGX6jdFfG4Ke04w2lUYOKG63W5bm7pP//bJaTnlphrznf0Ln6tW6zWDkSN2+EOTOr3X7161fLAqsthqjzUaPDhiROWFJnpz84vTA1zMRfaLZ4MEBVf73Z+oTBScv12+CZ28olKP/94dcdcEj+s36Y0/K5sIyuf2BT2VRRx3yq048WVeCCwqk+HT9pn5h537y87fTfF9r+3b9/QC6oh/Fm7KnJq2SHvdODB335hmVePYVz4eOQ/zpJ/01JkzY7/1H4/L35shhT04Ru9MlN36yQPo/OFnyiyv10SybTb/2jBsnIRXoKNz9zRLpNs7XKlRW5ZDhj/8mX553U+DfoNutj7Z37x5y4mxL0RyD8xEAfvX7/70A7o10/eYQnF0utwx77De5/avF3suqHE4Z9NAvsqnvEH3Iyd+QIfowUB1sKCiVPvdPkvPfmB14VnZRka7oDBig58/6PemXVTlk6KO/hs7VdLv1vvTpI+LUL6gnvzhdjn/+Tz3hw98FF+jDg2FaO5oTt9ste8qqZdWOYlm+vcj34ud265/RypV6PnMjzr6O5INZm6TXfRPlk7+3BL4oT5yoXwTHjQvbZ33L5wtl5JNTA25jd7rkpSMvEZfJFPo76d5d5PLL67SPVQ6n3PH1YskemyODH/nVO4PU4XDqkAfoWbyArlCUltZ4f2VVDul9/yQ55LHfAv9e3W69n0lJIl9+KZOX75TssTny0E/LA+9g4UJ9dOTkkyO+OLlcbpm7aY/8uGi7vD19gzz+80r59xeL5OK3Zss517ws79z8lD7KcM89ep9POEH/LQ8dGrGvvdLulH9/sUiyx+bIHV8vliqHUyrtTrnzy4Xy2uEXigDiPPY43Y/ucukXT0AfzaljyBcRWbBF9xePD551mp+vQ1sNM3xrct3H8wPayYyvM+/Ca/ULpsMhq3YUy8ibPhJ7QqLIJZf4brx7t9gTk+Wn/keHHvY2Xthz9Nn12WNzZN7mPYHXeestfZ0PPqjTvv/zg7ly4gt/hm74z3/034/d7u2n/9RvYoqI6N9vUlJAVXx/vvFMAAhpv5g9W/wPX1dUO6XnvRPlmcmrQ++ke/ewM3pry2jzOvWlMH3X556r+64rKuSmzxbI8MenhFa+77tPV6br8TfZ2JZvL5J3pm8M3fetW/XfexTn5UTicLq85+uM/W6pZI/NkTHP/KF7/M8+WxdU7rpL3FarVGS0kjvO/j855NFfZeKyHTJx2Q7p/8Akuf/Ce/VEjKQkfSRh5kz9Wpuaqud8R8k4ehJ8zod73z6psthk8tHnhd7ojDN060kDnX8zddUu7/Osf/XZW8i47jr9fGtUn2uhvNohxz//pwx/fIoUlFR5ixYLNu3WbTSJibrf3piN/tFHDfI9xUJzDM4XAHjP7/9XAHgt0vWbQ3AW0WOARvidxPLrCh0ENt96tw5FxmivVav0j/bll+v8tcqrHaFPoiK6EhghXBgD/kN694zK22efyR+r88NWzmXtWv2ifV/QQH6qFbfbHTrCzVDDi8MnYfo895VXy6UX6149mTQp8AatW4eM6qrtfr4xbYMc++w0+XNt0Avul1/qKvB770V9GG9ZbpGuagRbvVpkva8lwAheIX2ARvCK0Fs/yVNtM/71fWCSjHnmD7nj/k8CjzLYbLrdYeRIHWpWrQp7f/4/h5emrJPssTlywZuz5VxPFfrlqevE/dFH+tBwnz76BRgQueYakeCTOGvJWFzm1i9CjzDIkUfqdpE6GP3M7yHjLG/5fKHM6DFc7AcPERGRmz9fKAMf+kUqx92nvx9jASDPm4KTrn3dt5CDobpapE0b2XvGudJ9XE5otXn7dn1i5XHH1bnNxHju2lce9LP99lu9n3Pn+i1gEjRqz3h+q2EBk2DGGLx3pgeN1HM4dLvBDTeIiK93fsrKoKMZu3ZJQ/Xni4h8MVefcxIyvu7PP3WF9I23pO8Dk+TB4DedbrdIr176jeIBzBit2H2cPhHZ+xy8ebPvSN8VV4gUFsq6XSVy5qszvc8l574+S5+cmZenR7MZzyW9etV4pC2S016eIWe8MjPgsjkbd8uEfmOkKqNV4PNHbq7ODg34uut0uWXMM39I9tgcOeKpqYEtQCedpL+3pCRvC1BtrdlZIn3unyQXv/2XDH7kV19rSn6+bjnp108fferbt9FOxm8KLTI4A7gBwAIAC7p27dp4P51a+HqerlKs2amfuG/9YpEc8thv4vjTc0Kd8c704Yd1CA2af9rYSqscMuTRX0N7rFwuffZsv35y0esz5YinpoaenHDddfoJZlfQCwQ1iXW7SkJmqubuLZd+d3wnLrNFV6r92Ww1zlltruxOl5z7+iwZ+NAvgZVNt1vPtzWZRP74I+R2xszbRVv3SnGl3femcvJk/dj78kvdllPH4DZhSZ70uX+S9H1gUuCJqDNm6DcpJpNut2igCSD3fLtUBj38S+jj8Jln9PfjeVHbvq8itKVDRFcXd/parUqrHJI9Nkde/T1w1bxte8plV2prmXfUGbI+v0S6jcvR1dPSUv0id8QRul0mPV3k/PPlivfnypFP/x5yNMp5y61SbbHJCQ/8GDjP3u3WvZlJSRFnIEdjzsbdkj02R35fHfT8s2OHCCCOZ5+TEU9MkSveD3NEbeRI3b5WyyNMxz//p1wZfH8i+vvp0UNEfOe2hBx6H69nENenN99fRbVThj32m4x4YkrgG1nPIW9HcorcddrtMmdD0DSNRYv0fvjNCT5QTV6+Q1bkFYVu+P33kEkvDqdL3vxzgzw1aVVg65jbrY+a3HhjxBn3+2PMz/d/g3fHV4vlpkse1b+rn37yXfnxx/VlGzeGuae6M/bhx0VB/fc5OfrrPfRQve7/S88bPeOcCq8//tDZx3hObsFqCs6xmuOcB6CL3/8P8lzmJSLviMgIERnRNsx8wVgY1bsNAGDWht2osDsxdVU+Th3UAZYjj9AruE2dqt+rfvWVnsXYsWPNd9jAUhMsuG50d/yxpgDLt/utQGgy6ZV71qxBu18m4PqjegTOcszL02vQX3ONnrdITa5Xu1RkpdgCZoWXV7tQaUtE8aAhwPTpvitXV+t5v+npYe6pebOaTXjtH8NgNSs88NMK3wal9AzdPn2AK6/UjyM/xqqMB3fOQHqiFcpYmc6YAz1kiF6ZKsoV64KdOaQTJt42BpP/cxRO81/1aswYPR94/ny9qlcd7z/YCQPao7TKGTg/F9DzpAHg55+xt9yO016eiUcnrAy8jsOhn1/OPtt70dpdxmzlwL+JLlKJ9mV7MMXcDvd8twyJFjOuHd1dP189/rheBfLMM/Vc8AcewPnDOiOvqBJzNu8JuJ9vBh4Hm9OOV8wbfCudAXqluQkT9Gz1CCv2RWNIl0xYzQrzt+wL3NCxI9CjB3ZN+h2FpdW4YUyPwO1//QXMnQvccYeew1wLo3u1wdzNe1DtDFoh78QTgU2bgE2bsHhbEbpmJQeutAbo+c0WCzBsWK2+ZiRJNjM+u24kWiVbcdUH8/DIhJV6tTalgPHjsTG7H56b9BIOG3sTsMfvd/PNN/r7PvfcBtmPluyUQR0xsFNG6IbjjguZjWwxm/Cvo3vi3lP7I8Hi93ejFHD11cBbbwGtWtVpP84a2gkmBfywSEea4koHJi7fiTbnn6FnJXtmOsPtBt5/X8/S79GjhnusvauO7IbPrh2Js4cGri2B007Ts5sfeKBe93/xoV1w3ejuuH5Mdwzq7PczP/ZYvQbEBRfo2eFxKlbBeT6A3kqp7kopG4BLAEzYz21irnNmEnq0ScGs9YX4fXUBKh0uveiJ1aofmFOn6hfZtWuBSy6JyT5edWQ3ZCRZ8fLv6wM3nH8+8jr3wO1zvsbFw4OW8nzxRf0gvvvupttRCqCUwmHdsjDXL7CUVeuwWHLYkTq4lXsW7jCW297PAijNVafMJJw4oH3AgggAdJj75z+B7duBioqATcWVDqQmWAIXfQB8wbmOL3L+erVLRXdjaXZ/nTs3WEAyjO7VBolWE6auzg/c0Lcv0Ls3MH48XpiyFsWVDszeuNs4Cqe98YZexn3+fL2gBXzBuV/woiTLlwMA8g7qhUXbinD54V3R2giBV1+tl9idNUuH56FDcdKADkhNsHhf9AFgQ0EpHs5NwM7OPdB/yo+++96zRy8sNHw4cPvt9fp5JFrNGNQ5AwuDgzMAGTUKyQvmoH+HNIzqFbSgyHPPAVlZ+u+mlkb3aoMqhxsLtwZ9zRNP1B+nTMHibUU4pGtm6I3nzAGGDgWSkmr9dSPp3zEdE24djatHdcNHf23Bma/OwsodxShr1xHnnPMIplx1J9SE8cDgwcCUKfrN5bff6uDVpk2D7QfVT7u0RBzVpy1+WpwHt1swYUkeqp1uXHRET+DSS/US3Pv2AX/8oReMue66Bt8Hq9mE0b3b+AoMBqX033fQgj21pZTCA2cMwP2nDwjdeNtt+u/SFL/r68XkOxMRJ4BbAfwKYDWAb0RkZc23ah5G926DuZv34odF29E+PQGHdsvSG044AVi/Xq+mY7Ho1YtiIC3RimtHd8fU1flYkeerOq8pKMPTwy9Az8JtSP55vO8Ge/fqSt8llwDdu8dgj8kwskcWtu+rRF5RJQCgrFpXwqpHjdErzf39t75iCw/OAJCZbPNWkQNkeR5PQSsKFlc69CqQwRowODelJJsZo3u1xZRV+YGhWCng7LMhf/yBCTPWoGNGIvJLqrFtr+eNRGEh8PDDOsyL6IorgDW7SpCaYMFBrYKCnCc4n3zpiWidYsP1R/lVtsxmvZpcWpq+T89+nX5wR0xevhMVdifcbsHY75cjOdGC9Buu0V9vwwZ9+7vu0s8f77+vn/PqaUR2KyzZXhRSAV7bazCySvfh9u4qMAisWweMH69XuksJ84ZnPw7v2RoWk8Ks9bsDN/TpA3TpgspJv2BXSVXoioEuFzBvHjByZK2/5v4kWs14+MyB+OSaw1Bc6cA5r8/GHV8vQaVbIfOR+3VgT0/XK+Rdcole5TWOK3st1bmHdMaO4irM2bwHX87LxcBO6boye+WV+mjht9/qlQKzsoBzzon17lItxewtgYhMEpE+ItJTRJ6M1X7U1qhebVBhd2Ha2kKcfnAnmE2eJ3KjSvHVV/rzGFYA/jmqG9ITLXjFr+r81p8bMf3go+Dq21cfVnV7lkB+/XWgrAwYOzZGe0uGkd11NW2ep+pcXu1ZIvvII/S79xkz9P+N4NwCWzUMGUlWVDnc+nC0PyMABwXnkkoH0iMF56QkvZRxC3PigHbIK6rE6p2lAZfLmWdCORw4OW8pXrn0EADAXKOl46GH9OP1p590WPX8TazZVYo+7VNDK0zLlgGtW+OsU4Zj4YMnol1aYuD2Y4/VbRp+S1WfN6wzyu0u/LpyFz6dsxULt+7Dg6cPQMq1/9TB/tNP9eHejz8G7rlHt8k0gBHdsmB3urEiryTg8vfd+nDz8Xs2BN7gxRd15ezWW+v09VITLDikayZmbQgKzp6qnPnPP2Fyu3BI16A3ZStX6qM/hx9ep68bjaP6tMUvtx+F4/q1w5RV+WifnoDhXVvpIx8LF+rli7/5Rv8NMHg1O8aRm/9OXoNVO0twyWFd9YZhw/Ry5K+/Dvz4I3DFFUBiYs13Rs1O/NbSG8kRPVvDyMpnDvHrhezfH+jk6SeKUZuGIT3RimtGd8dvq/KxakcJcvdW4OdlO3HJEd1hfughYMUK/aAtL9cVpzPO0IdsKab6dkhDeqLF2+dc5gnOSa2z9BOu0eccBxVnIwSXBFedjeC8N7D3t7jSgfTEMFXNoiIgM7Phd7AJHNevPZQCpqwKbNf4Na079ial46bilRiR3QpZKTbM37xXt4G9844OTSNG6H8zZkBEsHZXKfp1DPNGavly/diuRW/2od2y0CUrCe/P2oz//bIGY3q3wXnDOusq9wkn6MB84426reTBB+v7Y/Aanq1/9wu2+H73y7cX47vKNFSlZcDy91++KxcWAh99pCt49TgvY3SvtlieV4x95fbADSecAFtJEQ7ZvRn9g3+uc+boj40YnAEgK8WGty4fjtf/MQwvXDQUJuOFJzkZeO013a7x2We6t5+alSSbGacO6oBl24uRaDXhrCGebKCUDsvLlunK87XXxnZHqU4YnGspPdGKQ7q2QpesJAz1P4SnlD58lpgYcNJOrFw9qjvSPFXnd2ZsgkkB147uAVx8sX7Be+wxfahozx5g3LhY7y4BMJsUDuue5a0ullXp4JyaYAGOPlqfBFVVpSuEQIsOzpme4BzSrhGx4uyM3KrRwto0DG3TEnBIl8yAPucqhwtP/LYeCwcdie7zpkO5XDi0Wyt9FOL22/X3+sgj+spHHQXMn49d+ftQXOkI7W92u/Wb5MGDa7VfJpPCuYcchBV5JRAAT517sK+SfdVVwNatujfz3XcbtFrWJjUBPdqkYIFfz/G7MzchJdEG8+hRwOzZviu/8YZ+LNx5Z72+5ujebXTHy8bAkyFx/PEAgPP2rAo8kRrQwbl163qdDBktpRROH9wRo3qFOYJ5wgn6+ZyapXOH6XOJTju4Y+Bz12WX6Y8jR7Jg1UIxONfBixcNxUdXHxZ6WPS//9WHTjPCnNnbxDKSrLh6VHf8snIXvp6fi/MOOQgdMhJ1X+MDD+h3vPfeC4weDYwaFevdJY+R3Vtj8+5yFJRUeVs1UhIsOiRVV+veyjhp1QDCBOe69Di30OAMACcO6IDlecXYWaz72t+buQnb91Wiy1UXQ+3bB8yahUO7ZaH/31OBP/8EnnjC9/0edRTgcGDXb/pIRL+giRrYvFkfVarDi/MFww6CzWLCuFP7oUtWsm/DOefoyQC33qonjjSw4dmtsHDrPogItu+rwMTlO3HJoV1gHTMaWL1av9GvrNQV19NP10f66mHIQRlIS7Bg1obCgMvtWW2wqn0PjNq8OPRGc+boanMDTVih+HR499a4+6Q+uP34PoEbunbVb/xeeikm+0X1x+BcB11bJ6Nn29TQDe3bA4ce2vQ7FME1o7ohNcECh9uNG4/2Oynokkv0mfuVlTo8U7MxsocOjnM370WZ3QmbxaQrXmPG6Bfq6dPjolUjYnCOUHGO3+DcDgAwdXUBdhZX4vVpG3HKwA7od9UFum97wgQc3iEZ90/7AMV9+gPXX++78ahRgFJwTNPBuW/78BM1altxBvRz3OIHT8SVR3QL3JCSoke1vfxyre8zGod2y8Lecjs27S7Hh7O3AACuHt1dv8EH9MmJn3wC7N7dIFOALGYTDu/ZOqTPec2uEszIHoquq5f4ptkAujVo9epGb9Ogls9kUrj1uN7o2jo5dONNN/FvqAVjcI5jmck2PHLWQNx9Ul/08A/6Fouu2Nx0E3DqqbHbQQoxoGM6UhMsmLt5D8qqnLpNA9DhcPBgHZzjoFXDCMFFFUHBOS1Nnwjp1+Nsd7pR6XDFZXDu2VaPwJuyKh/PTF4DlwjuP72/Hs13/PHA+PHo/+W76FKcj+8uvytwVnFmJjB4MDIW/I1OGYnISA76+Sxbpt9sDRxYp31LSYgwKSM1tdFGTQ3vpn+Xf6wuwFfztuGMwR3ROTNJ93NbrcDMmcALL+iTGY8+ukG+5pjebZC7txJb9/gC8uJtRZjdbShMTof+moZ58/RHhh6iAxaDc5y7YPhBuOXYXqEbTjpJHy7i4cZmxWI2YXh2K8zdtBfl1X7BGdBB4a+/fKGyBQfnzOQIFWeTSQdCv4qzcZ2QYAjo67XQkwMB3cN6Qv92mL1hN35asgM3jOnha4046yxg0yaYn3gc84cfi29Se4fewVFHIXvdMgxoE2ae8PLlemGFOoxqi5UebVKQlWLDS1PXodzuwvXGgidJnvD85pt6DN3ddzfYc9doT//wTL+xdIu37cPWAcMgCQn6JDzDnDn66zajI4tE1LQYnImamZE9srC+oAzb9lYEVv2OOkq310ybpoNEA8zOjRVj9bmIs5zDBefgirPLpavvLbjiDOg+Z5db0D49ATcd43fC2Zln6o8mE1bf+SDW5peGTH9wjBqNRHsVxpTlht7xsmV1atOIJaUUhme3QrndhSN7tg5clWzUKD2Kr2tXvTJZA+neJgWdM5MC5jkvzi3CgB4doEaPDg3OAwY0i/NYiCg2GJyJmhljnvPi3CKkJvgdmj/qKP3x779bdLUZ0BNE0hIt4YNzq1YBwbmkSl8nPTEoOHtWzWvpwXlY10ycOKA9njr34MA3Sp066bFvzz6L/kfqWcnztwSO6dsyQK9oOGLb8sA7razUC5W0wLP2D/W0awQs1gL4+pxvv71B3zQqpTC6Vxv8tXE3XG7BnrJqbN1ToVcMPPFEXbnftUsvOGOcGEhEB6yWW7IiilMHd85AktWMSocrsFWjbVtd7Vq1qsUHZ0BXkCMGZ78eZ+M6IQugtNBVA4NZzCa8e+WI8BvfegsAMNjpgs1iwrzNe3HSwA7ezatcSTBldUbXVYsCb7dqlR5H18IqzgBw6WFd0TEjCcf0aRu44fTT9ezmRpiTP7p3G3y9IBfLthdhT5mu6h/StRWQdqIe1zl1KnDYYfpvjsGZ6IDGijNRM2OzmDAsOxNAmBO0jKpzCx5FZ8hMriE4+1ecI7VqxElwjkaCxYxDumRiXlDFefXOUizoOgip8/72rQYK6DYNoEVWnNMSrThzSKfQcZ8Wi54j3QirRB7ZUx/lmbV+Nxbn7oPZpHBw5wxg6FA9s3nq1CZb+ISImjcGZ6JmyGjXSA0OzsYkgXiuOEfb43wABWcAGNk9Cyt3lHhXlASAtbtKsG3AMKiiIr3YiWH5ct0H3wSLdMSD1qkJGNgpHTM37MbibUXo3zENSTazPln1+ON1n7PRIlXP2dFE1LIxOBM1QyO763nO8R6ciyrsoRuMirMIAKC4IkJwLirSH1vwVI3aOLR7FlxuwSK/lfXW7ipF5eGeBYxmzPBdedkyPYbOf3wd1Wh07zZYvG0fluQW4ZAufm/GTjwR2LED+PZb3a7BnynRAY3BmagZGtIlE61TbIGrtgFAx47AIYcA3brFZL8akq44O0M3tGqlJ2Z4FnoprnQgyWoOXfr4AKs4D+vaCmaTwjzPkuzFFQ7sKK5C20F9gS5dAucNL1/eIts0YmlMr7ZwuAQVdpc+MdBw4on64549bNMgIp4cSNQcJVrNmHHPsUiyhqluzZgB2GxNv1MNLCPJhpJKB0QksJ/Vf/XA9HSUVNWwaqD/9eNcSoIFgzpneIPz2nz9xqJfx3Td+/7777pKX1Cg/7XAEwNjaUS3VkiwmFDtdOsTAw3Z2Xql1fXrGZyJiBVnouYqJcECkynMIg+pqXESnK2wu9yocrgDNwQtu11c6UB6Upj3+Pv26dXkksMsaRunRnbPwpLcIlQ5XFizS68g2a9jml6SfdcuPYLOWGqbFedaSbSacVj3LLRKtqJb8DLJRtV55Mim3zEialZYcSaimPAuu11pR5LNb+W7LN3f7R+ca1xu+wBa/fLQbll4Z8YmLNtejDW7SpGeaEGH9ETftJWZM33zrRmca+3RswZiT7k9dKLHAw8AJ5ygR0IS0QGNwZmIYsIIw8WVDnTM8AvORsXZM8u5uNKJzpmJoXdgBOcDiLE4yLzNe7BmZwn6dUzXIa9fP6BNG93GoxTQvj3Qrl2M97bl6dE2FT3CZeOOHYFzz23y/SGi5oetGkQUE5nJnuBcETSSLqhVo6TSEbr4CaCnahwgEzUMmck29OuQhjmb9mJdfhn6dfBMV1FKt2vMmMETA4mIGhGDMxHFhK9Vo+bgvN9WjQPMYd2z8PemPSirdqJfB7+FcI46Cti8GVi6lCcGEhE1EgZnIooJ/1aNAKmpepW4ffvgdLlRVu1kcPZzaDc9zxkA+nbwm+dt9Dk7naw4ExE1EgZnIooJo/2iJDg4K6UD8d69KK3Sc54ZnH0O8yyOAwQF5yFDfAvjMDgTETUKBmciiom0BAtMKkzFGfCuHmhsS08MCs5ut+5xPgCDc/v0RHRrnYwuWUmBK0uazcCoUXqZ6AEDYreDRERxjFM1iCgmTCaF9CQrioJPDgRCgnNIxbm0VIfnAzA4A8DdJ/dFdfD8awC4+24dnpOSQrcREVG9MTgTUczoZbfDBOesLKCw0Beck4OCc1GR/niATdUwnDG4U/gNxx+v/xERUaNgqwYRxUzE4OzpcY5YcT7AltsmIqLmgcGZiGKmxuBcU6sGgzMREcUAgzMRxUyNwbmoCCUV1d7rBWBwJiKiGGBwJqKYqbHHWQRVe/bBZjEh0WoO3M7gTEREMcDgTEQxYwRnEQnc4AnEzsI9oaPoAN/JgQzORETUhBiciShmMpOtcLkF5XZX4AZPIHbt3YuMpDDDf/bt0/OKU1ObYC+JiIg0Bmciihmjd7mowh64wROcZe/eyKsGZmbq8ExERNRE+KpDRDFjhOKQPucsvay0KiricttERNRsMDgTUcxkJNkAhAnOnlBsZXAmIqJmhMGZiGLGW3EOXnbbCM4lxQzORETUbDA4E1HMGEtph1Sck5IgNhsSyiIE56KiA3a5bSIiip0wp6sTETWNiD3OSkFaZSG9qgwWVpyJiKiZYHAmophJsZlhMamwi6C4MjKRWVkaGpxFGJyJiCgm2KpBRDGjlEJGkhVFYYKzPT0DGdVloa0aFRWAw8HgTERETY7BmYhiKtKy29Wp6cioKg8Nzlxum4iIYoStGkQUU+lJVpSECc6VqRnIqCqDOTg4c7ltIiKKEVaciSimMpPDV5zLktOQUVkaueLMqRpERNTEGJyJKKYykqwoCp7jDKA0KRXp9gpk2IKeptiqQUREMcLgTEQxFanHuTgxFQCQXFEauIHBmYiIYoTBmYhiKiPJipIqB9xuCbh8ry0FAKCMnmYDgzMREcUIgzMRxVRGkhUiQGmVM+DyPQm64oy9ewNvYATnjIwm2DsiIiIfBmciiqlIqwcWWpL1J0ZQNhQV6dBsNjfB3hEREfkwOBNRTEUKzvnmJP1JcHDet48TNYiIKCYYnIkopiIF552mGoIz+5uJiCgGGJyJKKYyk20AgKJKe8DleUjUn4TrcWZwJiKiGGBwJqKYCldxFhEUOhUcCYmsOBMRUbPB4ExEMRUuOJfbXXC5Bfa0DAZnIiJqNhiciSimEq0m2MymgOBc4vnckR4mOBcVMTgTEVFMMDgTUUwppZCRbEWx37LbRoh2Z7YK7HGurgYqKzlVg4iIYoLBmYhiLnjZbe/nrVoFVpy5aiAREcUQgzMRxVyk4KyyGJyJiKj5YHAmopiLFJwtrbMYnImIqNlgcCaimMtMsqKoIvTkQGvbNkBZGeDwbCsq0h8ZnImIKAYYnIko5tKTrN6wDOjgrBRga9taX2BUmo2PPDmQiIhigMGZiGIuI8mK0monnC43AN2qkZ5ohSkrS18hODiz4kxERDHA4ExEMWcsglJS5QSgg3NGktUXkBmciYioGWBwJqKYy0wOXD3QG5yNirMxy3nfPiAlBbBaY7GbRER0gLPEegeIiIKX3fZVnDP1Ffwrzqw2ExFRjNSr4qyUelYptUYptUwp9aNSKtNv271KqQ1KqbVKqZP9Lj/Fc9kGpdS4+nx9IooP4YJzepIltFWDy20TEVEM1bdVYwqAQSIyGMA6APcCgFJqAIBLAAwEcAqAN5RSZqWUGcDrAE4FMADApZ7rEtEBzGjVKKqwA9C9zhF7nDlRg4iIYqRewVlEfhMRp+e/cwAc5Pn8bABfiUi1iGwGsAHAYZ5/G0Rkk4jYAXzluS4RHcDSjZMDAyrOVt3LnJoa2OPMijMREcVIQ54ceA2AyZ7POwPI9du23XNZpMuJ6ADm36pR5XDB7nR7L0OrVuxxJiKiZmG/JwcqpaYC6BBm0/0iMt5znfsBOAF83lA7ppS6AcANANC1a9eGulsiaoYSLGYkWk0ornR4+5wZnImIqLnZb3AWkRNq2q6U+ieAMwAcLyLiuTgPQBe/qx3kuQw1XB78dd8B8A4AjBgxQsJdh4jiR2aSDUUVNQRnh0Mvv83gTEREMVLfqRqnALgHwFkiUuG3aQKAS5RSCUqp7gB6A5gHYD6A3kqp7kopG/QJhBPqsw9EFB8ykqzhK85ZWbrHubhY/5/BmYiIYqS+c5xfA5AAYIpSCgDmiMi/RGSlUuobAKugWzhuEREXACilbgXwKwAzgA9EZGU994GI4oARnI0TBNMTgyrORrsGp2oQEVGM1Cs4i0ivGrY9CeDJMJdPAjCpPl+XiOJPepIV2/dVRG7V4HLbREQUY1xym4iahczkCK0arVoBlZXArl2+/xMREcUAgzMRNQvBPc7p/sEZADZtCvw/ERFRE2NwJqJmISPJigq7C3vK7EhLsMBsUnpDVpb+yOBMREQxxuBMRM2C0ZqxbW+Fr9oMsOJMRETNBoMzETULmck6LOfurfD1NwOBwTkhAUhMjMHeERERMTgTUTNhVJm376tEepLfwB8jOG/ezGozERHFFIMzETULRpXZ7nIHVpyNHueqKgZnIiKKKQZnImoW/MNyQHD2X/CEwZmIiGKIwZmImoXMSMHZbAbS0/XnDM5ERBRDDM5E1CykRwrOgC8wMzgTEVEMMTgTUbNgNZuQYjMDCBOcjT5n/7YNIiKiJsbgTETNhhGY01lxJiKiZojBmYiajYxkGwAGZyIiap4YnImo2cjwzG9mjzMRETVHDM5E1GwYgTlijzODMxERxRCDMxE1GxGDsxGYeXIgERHFEIMzETUbmUaPcyJbNYiIqPmxxHoHiIgMZw/thIwkK2yWoPf0J54I/OMfQN++sdkxIiIiMDgTUTMysFMGBnbKCN3Qowfw+edNv0NERER+2KpBRERERBQFBmciIiIioigwOBMRERERRYHBmYiIiIgoCgzORERERERRYHAmIiIiIooCgzMRERERURQYnImIiIiIosDgTEREREQUBQZnIiIiIqIoMDgTEREREUWBwZmIiIiIKAoMzkREREREUWBwJiIiIiKKAoMzEREREVEUGJyJiIiIiKLA4ExEREREFAUGZyIiIiKiKCgRifU+7JdSqhDA1hh9+TYAdsfoa1PT4u/6wMHf9YGDv+sDB3/XB47G/l1ni0jbcBtaRHCOJaXUAhEZEev9oMbH3/WBg7/rAwd/1wcO/q4PHLH8XbNVg4iIiIgoCgzORERERERRYHDev3divQPUZPi7PnDwd33g4O/6wMHf9YEjZr9r9jgTEREREUWBFWciIiIioigwOEeglDpFKbVWKbVBKTUu1vtDDUcp1UUpNU0ptUoptVIp9R/P5VlKqSlKqfWej61iva/UMJRSZqXUYqVUjuf/3ZVScz2P76+VUrZY7yPVn1IqUyn1nVJqjVJqtVLqCD6u45NS6g7P8/cKpdSXSqlEPq7jg1LqA6VUgVJqhd9lYR/HSnvF8ztfppQa1tj7x+AchlLKDOB1AKcCGADgUqXUgNjuFTUgJ4C7RGQAgMMB3OL5/Y4D8LuI9Abwu+f/FB/+A2C13/+fAfCiiPQCsA/AtTHZK2poLwP4RUT6ARgC/Tvn4zrOKKU6A7gNwAgRGQTADOAS8HEdLz4CcErQZZEex6cC6O35dwOANxt75xicwzsMwAYR2SQidgBfATg7xvtEDUREdorIIs/npdAvrp2hf8cfe672MYBzYrKD1KCUUgcBOB3Ae57/KwDHAfjOcxX+ruOAUioDwFEA3gcAEbGLSBH4uI5XFgBJSikLgGQAO8HHdVwQkRkA9gZdHOlxfDaAT0SbAyBTKdWxMfePwTm8zgBy/f6/3XMZxRmlVDcAhwCYC6C9iOz0bNoFoH2s9osa1EsA7gHg9vy/NYAiEXF6/s/Hd3zoDqAQwIeetpz3lFIp4OM67ohIHoDnAGyDDszFABaCj+t4Fulx3OR5jcGZDlhKqVQA3wO4XURK/LeJHjfDkTMtnFLqDAAFIrIw1vtCjc4CYBiAN0XkEADlCGrL4OM6Pnj6W8+GfrPUCUAKQg/tU5yK9eOYwTm8PABd/P5/kOcyihNKKSt0aP5cRH7wXJxvHOLxfCyI1f5RgxkF4Cyl1BbolqvjoPtgMz2HeAE+vuPFdgDbRWSu5//fQQdpPq7jzwkANotIoYg4APwA/Vjn4zp+RXocN3leY3AObz6A3p4zdG3QJx1MiPE+UQPx9Li+D2C1iLzgt2kCgKs8n18FYHxT7xs1LBG5V0QOEpFu0I/jP0TkMgDTAFzguRp/13FARHYByFVK9fVcdDyAVeDjOh5tA3C4UirZ83xu/K75uI5fkR7HEwBc6ZmucTiAYr+WjkbBBVAiUEqdBt0baQbwgYg8Gds9ooailBoNYCaA5fD1vd4H3ef8DYCuALYCuEhEgk9QoBZKKXUMgLtF5AylVA/oCnQWgMUALheR6hjuHjUApdRQ6JNAbQA2AbgaukDEx3WcUUo9CuBi6ClJiwFcB93bysd1C6eU+hLAMQDaAMgH8DCAnxDmcex54/QadKtOBYCrRWRBo+4fgzMRERER0f6xVYOIiIiIKAoMzkREREREUWBwJiIiIiKKAoMzEREREVEUGJyJiIiIiKLA4ExEREREFAUGZyIiIiKiKDA4ExERERFF4f8BJHrBZiJkq54AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "print('Test MSE: %.3f' % error)\n",
    "zoom_start = 100\n",
    "zoom_end = 200\n",
    "fig, ax = plt.subplots(figsize=(12,8))\n",
    "ax.plot(test[zoom_start:zoom_end])\n",
    "ax.plot(predictions[zoom_start:zoom_end], color='red')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Prophet"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 220,
   "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>ds</th>\n",
       "      <th>y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2014-01-03</td>\n",
       "      <td>5.551200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2014-01-04</td>\n",
       "      <td>-21.996700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2014-01-05</td>\n",
       "      <td>4.884000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2014-01-06</td>\n",
       "      <td>150.894711</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2014-01-07</td>\n",
       "      <td>-35.981050</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1232</th>\n",
       "      <td>2017-12-26</td>\n",
       "      <td>15.280050</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1233</th>\n",
       "      <td>2017-12-27</td>\n",
       "      <td>-15.987100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1234</th>\n",
       "      <td>2017-12-28</td>\n",
       "      <td>13.322042</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1235</th>\n",
       "      <td>2017-12-29</td>\n",
       "      <td>53.702817</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1236</th>\n",
       "      <td>2017-12-30</td>\n",
       "      <td>14.505214</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1237 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             ds           y\n",
       "0    2014-01-03    5.551200\n",
       "1    2014-01-04  -21.996700\n",
       "2    2014-01-05    4.884000\n",
       "3    2014-01-06  150.894711\n",
       "4    2014-01-07  -35.981050\n",
       "...         ...         ...\n",
       "1232 2017-12-26   15.280050\n",
       "1233 2017-12-27  -15.987100\n",
       "1234 2017-12-28   13.322042\n",
       "1235 2017-12-29   53.702817\n",
       "1236 2017-12-30   14.505214\n",
       "\n",
       "[1237 rows x 2 columns]"
      ]
     },
     "execution_count": 220,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prophet_df = copy.deepcopy(index_df.reset_index())\n",
    "prophet_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 221,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:fbprophet:Disabling daily seasonality. Run prophet with daily_seasonality=True to override this.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<fbprophet.forecaster.Prophet at 0x7f9b11127df0>"
      ]
     },
     "execution_count": 221,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fb = Prophet()\n",
    "fb.fit(prophet_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 222,
   "metadata": {},
   "outputs": [],
   "source": [
    "future_df = fb.make_future_dataframe(periods =  365)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 223,
   "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>ds</th>\n",
       "      <th>yhat</th>\n",
       "      <th>yhat_lower</th>\n",
       "      <th>yhat_upper</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2014-01-03</td>\n",
       "      <td>7.824874</td>\n",
       "      <td>-119.768727</td>\n",
       "      <td>134.548907</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2014-01-04</td>\n",
       "      <td>8.054407</td>\n",
       "      <td>-116.111966</td>\n",
       "      <td>143.632211</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2014-01-05</td>\n",
       "      <td>6.004004</td>\n",
       "      <td>-123.044028</td>\n",
       "      <td>138.609284</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2014-01-06</td>\n",
       "      <td>6.822039</td>\n",
       "      <td>-120.954027</td>\n",
       "      <td>134.014443</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2014-01-07</td>\n",
       "      <td>3.193853</td>\n",
       "      <td>-124.013189</td>\n",
       "      <td>133.834311</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1597</th>\n",
       "      <td>2018-12-26</td>\n",
       "      <td>23.419262</td>\n",
       "      <td>-113.010204</td>\n",
       "      <td>154.161701</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1598</th>\n",
       "      <td>2018-12-27</td>\n",
       "      <td>22.944420</td>\n",
       "      <td>-101.665918</td>\n",
       "      <td>149.398608</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1599</th>\n",
       "      <td>2018-12-28</td>\n",
       "      <td>22.847432</td>\n",
       "      <td>-115.125951</td>\n",
       "      <td>162.484750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1600</th>\n",
       "      <td>2018-12-29</td>\n",
       "      <td>23.516178</td>\n",
       "      <td>-116.529456</td>\n",
       "      <td>149.120306</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1601</th>\n",
       "      <td>2018-12-30</td>\n",
       "      <td>21.734648</td>\n",
       "      <td>-108.707729</td>\n",
       "      <td>156.326805</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1602 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             ds       yhat  yhat_lower  yhat_upper\n",
       "0    2014-01-03   7.824874 -119.768727  134.548907\n",
       "1    2014-01-04   8.054407 -116.111966  143.632211\n",
       "2    2014-01-05   6.004004 -123.044028  138.609284\n",
       "3    2014-01-06   6.822039 -120.954027  134.014443\n",
       "4    2014-01-07   3.193853 -124.013189  133.834311\n",
       "...         ...        ...         ...         ...\n",
       "1597 2018-12-26  23.419262 -113.010204  154.161701\n",
       "1598 2018-12-27  22.944420 -101.665918  149.398608\n",
       "1599 2018-12-28  22.847432 -115.125951  162.484750\n",
       "1600 2018-12-29  23.516178 -116.529456  149.120306\n",
       "1601 2018-12-30  21.734648 -108.707729  156.326805\n",
       "\n",
       "[1602 rows x 4 columns]"
      ]
     },
     "execution_count": 223,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "forecast = fb.predict(future_df)\n",
    "forecast[['ds', 'yhat', 'yhat_lower', 'yhat_upper']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 224,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fb.plot(forecast,uncertainty=True);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 225,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x648 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fb.plot_components(forecast,uncertainty=True);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# LSTM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 686,
   "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>y</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ds</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2014-01-03</th>\n",
       "      <td>5.551200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-04</th>\n",
       "      <td>-21.996700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-05</th>\n",
       "      <td>4.884000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-06</th>\n",
       "      <td>150.894711</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-07</th>\n",
       "      <td>-35.981050</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     y\n",
       "ds                    \n",
       "2014-01-03    5.551200\n",
       "2014-01-04  -21.996700\n",
       "2014-01-05    4.884000\n",
       "2014-01-06  150.894711\n",
       "2014-01-07  -35.981050"
      ]
     },
     "execution_count": 686,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 1237 rows × 1 columns\n",
    "LSTM_df = copy.deepcopy(index_df)\n",
    "LSTM_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 687,
   "metadata": {},
   "outputs": [],
   "source": [
    "LSTM_df['y-1'] = LSTM_df['y'].shift(1)\n",
    "LSTM_df['y-2'] = LSTM_df['y'].shift(2)\n",
    "LSTM_df['y-3'] = LSTM_df['y'].shift(3)\n",
    "LSTM_df['y-4'] = LSTM_df['y'].shift(4)\n",
    "LSTM_df['y-5'] = LSTM_df['y'].shift(5)\n",
    "LSTM_df['y-6'] = LSTM_df['y'].shift(6)\n",
    "LSTM_df['y-7'] = LSTM_df['y'].shift(7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 688,
   "metadata": {},
   "outputs": [],
   "source": [
    "LSTM_df.dropna(inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 689,
   "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>y</th>\n",
       "      <th>y-1</th>\n",
       "      <th>y-2</th>\n",
       "      <th>y-3</th>\n",
       "      <th>y-4</th>\n",
       "      <th>y-5</th>\n",
       "      <th>y-6</th>\n",
       "      <th>y-7</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ds</th>\n",
       "      <th></th>\n",
       "      <th></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>2014-01-11</th>\n",
       "      <td>3.081400</td>\n",
       "      <td>11.326850</td>\n",
       "      <td>5.459000</td>\n",
       "      <td>-35.981050</td>\n",
       "      <td>150.894711</td>\n",
       "      <td>4.884000</td>\n",
       "      <td>-21.996700</td>\n",
       "      <td>5.551200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-13</th>\n",
       "      <td>61.239873</td>\n",
       "      <td>3.081400</td>\n",
       "      <td>11.326850</td>\n",
       "      <td>5.459000</td>\n",
       "      <td>-35.981050</td>\n",
       "      <td>150.894711</td>\n",
       "      <td>4.884000</td>\n",
       "      <td>-21.996700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-14</th>\n",
       "      <td>-53.285600</td>\n",
       "      <td>61.239873</td>\n",
       "      <td>3.081400</td>\n",
       "      <td>11.326850</td>\n",
       "      <td>5.459000</td>\n",
       "      <td>-35.981050</td>\n",
       "      <td>150.894711</td>\n",
       "      <td>4.884000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-15</th>\n",
       "      <td>65.978000</td>\n",
       "      <td>-53.285600</td>\n",
       "      <td>61.239873</td>\n",
       "      <td>3.081400</td>\n",
       "      <td>11.326850</td>\n",
       "      <td>5.459000</td>\n",
       "      <td>-35.981050</td>\n",
       "      <td>150.894711</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-16</th>\n",
       "      <td>-1.483375</td>\n",
       "      <td>65.978000</td>\n",
       "      <td>-53.285600</td>\n",
       "      <td>61.239873</td>\n",
       "      <td>3.081400</td>\n",
       "      <td>11.326850</td>\n",
       "      <td>5.459000</td>\n",
       "      <td>-35.981050</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    y        y-1        y-2        y-3         y-4  \\\n",
       "ds                                                                   \n",
       "2014-01-11   3.081400  11.326850   5.459000 -35.981050  150.894711   \n",
       "2014-01-13  61.239873   3.081400  11.326850   5.459000  -35.981050   \n",
       "2014-01-14 -53.285600  61.239873   3.081400  11.326850    5.459000   \n",
       "2014-01-15  65.978000 -53.285600  61.239873   3.081400   11.326850   \n",
       "2014-01-16  -1.483375  65.978000 -53.285600  61.239873    3.081400   \n",
       "\n",
       "                   y-5         y-6         y-7  \n",
       "ds                                              \n",
       "2014-01-11    4.884000  -21.996700    5.551200  \n",
       "2014-01-13  150.894711    4.884000  -21.996700  \n",
       "2014-01-14  -35.981050  150.894711    4.884000  \n",
       "2014-01-15    5.459000  -35.981050  150.894711  \n",
       "2014-01-16   11.326850    5.459000  -35.981050  "
      ]
     },
     "execution_count": 689,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 1230 rows × 8 columns\n",
    "LSTM_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 690,
   "metadata": {},
   "outputs": [],
   "source": [
    "scaler = MinMaxScaler(feature_range=(0, 1))\n",
    "dataset = scaler.fit_transform(LSTM_df)\n",
    "invert_dataset = scaler.inverse_transform(LSTM_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 678,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "865 365\n"
     ]
    }
   ],
   "source": [
    "train_size = int(len(X) * 0.70)\n",
    "test_size = len(X) - train_size\n",
    "train, test = dataset[0:train_size,:], dataset[train_size:len(dataset),:]\n",
    "print(len(train), len(test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 679,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train = train[:,1:]\n",
    "y_train = train[:,0]\n",
    "X_test = test[:,1:]\n",
    "y_test = test[:,0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 680,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(865, 7)"
      ]
     },
     "execution_count": 680,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 681,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(365, 7)"
      ]
     },
     "execution_count": 681,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 682,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train = np.reshape(X_train, (X_train.shape[0], X_train.shape[1], 1))\n",
    "X_test = np.reshape(X_test, (X_test.shape[0], X_test.shape[1], 1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 683,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(865, 7, 1)"
      ]
     },
     "execution_count": 683,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 684,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(365, 7, 1)"
      ]
     },
     "execution_count": 684,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 685,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.5996678 , 0.60231846, 0.60043212, ..., 0.60024728, 0.59160594,\n",
       "        0.60046176],\n",
       "       [0.61836399, 0.5996678 , 0.60231846, ..., 0.64718531, 0.60024728,\n",
       "        0.59160594],\n",
       "       [0.58154751, 0.61836399, 0.5996678 , ..., 0.5871104 , 0.64718531,\n",
       "        0.60024728],\n",
       "       ...,\n",
       "       [0.60295985, 0.59353785, 0.60358929, ..., 0.60476581, 0.610073  ,\n",
       "        0.61535745],\n",
       "       [0.61594105, 0.60295985, 0.59353785, ..., 0.63018608, 0.60476581,\n",
       "        0.610073  ],\n",
       "       [0.60334021, 0.61594105, 0.60295985, ..., 0.60369774, 0.63018608,\n",
       "        0.60476581]])"
      ]
     },
     "execution_count": 685,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### modeling"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src=\"LSTM_input.png\">"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src=\"LSTM_param.png\">"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 628,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(865, 7, 1)"
      ]
     },
     "execution_count": 628,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 629,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/100\n",
      "28/28 [==============================] - 0s 12ms/step - loss: 0.1110\n",
      "Epoch 2/100\n",
      "28/28 [==============================] - 0s 11ms/step - loss: 0.0074\n",
      "Epoch 3/100\n",
      "28/28 [==============================] - 0s 11ms/step - loss: 0.0049\n",
      "Epoch 4/100\n",
      "28/28 [==============================] - 0s 10ms/step - loss: 0.0041\n",
      "Epoch 5/100\n",
      "28/28 [==============================] - 0s 10ms/step - loss: 0.0041\n",
      "Epoch 6/100\n",
      "28/28 [==============================] - 0s 10ms/step - loss: 0.0039\n",
      "Epoch 7/100\n",
      "28/28 [==============================] - 0s 10ms/step - loss: 0.0039\n",
      "Epoch 8/100\n",
      "28/28 [==============================] - 0s 10ms/step - loss: 0.0036\n",
      "Epoch 9/100\n",
      "28/28 [==============================] - 0s 10ms/step - loss: 0.0038\n",
      "Epoch 10/100\n",
      "28/28 [==============================] - 0s 10ms/step - loss: 0.0034\n",
      "Epoch 11/100\n",
      "28/28 [==============================] - 0s 11ms/step - loss: 0.0032\n",
      "Epoch 12/100\n",
      "28/28 [==============================] - 0s 11ms/step - loss: 0.0033\n",
      "Epoch 13/100\n",
      "28/28 [==============================] - 0s 10ms/step - loss: 0.0030\n",
      "Epoch 14/100\n",
      "28/28 [==============================] - 0s 10ms/step - loss: 0.0032\n",
      "Epoch 15/100\n",
      "28/28 [==============================] - 0s 10ms/step - loss: 0.0033\n",
      "Epoch 16/100\n",
      "28/28 [==============================] - 0s 10ms/step - loss: 0.0030\n",
      "Epoch 17/100\n",
      "28/28 [==============================] - 0s 10ms/step - loss: 0.0030\n",
      "Epoch 18/100\n",
      "28/28 [==============================] - 0s 10ms/step - loss: 0.0032\n",
      "Epoch 19/100\n",
      "28/28 [==============================] - 0s 10ms/step - loss: 0.0033\n",
      "Epoch 20/100\n",
      "28/28 [==============================] - 0s 10ms/step - loss: 0.0031\n",
      "Epoch 21/100\n",
      "28/28 [==============================] - 0s 10ms/step - loss: 0.0030\n",
      "Epoch 22/100\n",
      "28/28 [==============================] - 0s 10ms/step - loss: 0.0031\n",
      "Epoch 23/100\n",
      "28/28 [==============================] - 0s 10ms/step - loss: 0.0030\n",
      "Epoch 24/100\n",
      "28/28 [==============================] - 0s 10ms/step - loss: 0.0031\n",
      "Epoch 25/100\n",
      "28/28 [==============================] - 0s 10ms/step - loss: 0.0029\n",
      "Epoch 26/100\n",
      "28/28 [==============================] - 0s 10ms/step - loss: 0.0031\n",
      "Epoch 27/100\n",
      "28/28 [==============================] - 0s 10ms/step - loss: 0.0033\n",
      "Epoch 28/100\n",
      "28/28 [==============================] - 0s 10ms/step - loss: 0.0028\n",
      "Epoch 29/100\n",
      "28/28 [==============================] - 0s 10ms/step - loss: 0.0032\n",
      "Epoch 30/100\n",
      "28/28 [==============================] - 0s 10ms/step - loss: 0.0030\n",
      "Epoch 31/100\n",
      "28/28 [==============================] - 0s 10ms/step - loss: 0.0028\n",
      "Epoch 32/100\n",
      "28/28 [==============================] - 0s 10ms/step - loss: 0.0033\n",
      "Epoch 33/100\n",
      "28/28 [==============================] - 0s 10ms/step - loss: 0.0027\n",
      "Epoch 34/100\n",
      "28/28 [==============================] - 0s 10ms/step - loss: 0.0030\n",
      "Epoch 35/100\n",
      "28/28 [==============================] - 0s 10ms/step - loss: 0.0030\n",
      "Epoch 36/100\n",
      "28/28 [==============================] - 0s 10ms/step - loss: 0.0026\n",
      "Epoch 37/100\n",
      "28/28 [==============================] - 0s 10ms/step - loss: 0.0030\n",
      "Epoch 38/100\n",
      "28/28 [==============================] - 0s 10ms/step - loss: 0.0028\n",
      "Epoch 39/100\n",
      "28/28 [==============================] - 0s 10ms/step - loss: 0.0026\n",
      "Epoch 40/100\n",
      "28/28 [==============================] - 0s 10ms/step - loss: 0.0028\n",
      "Epoch 41/100\n",
      "28/28 [==============================] - 0s 10ms/step - loss: 0.0027\n",
      "Epoch 42/100\n",
      "28/28 [==============================] - 0s 10ms/step - loss: 0.0028\n",
      "Epoch 43/100\n",
      "28/28 [==============================] - 0s 10ms/step - loss: 0.0026\n",
      "Epoch 44/100\n",
      "28/28 [==============================] - 0s 10ms/step - loss: 0.0028\n",
      "Epoch 45/100\n",
      "28/28 [==============================] - 0s 10ms/step - loss: 0.0027\n",
      "Epoch 46/100\n",
      "28/28 [==============================] - 0s 10ms/step - loss: 0.0027\n",
      "Epoch 47/100\n",
      "28/28 [==============================] - 0s 10ms/step - loss: 0.0027\n",
      "Epoch 48/100\n",
      "28/28 [==============================] - 0s 10ms/step - loss: 0.0025\n",
      "Epoch 49/100\n",
      "28/28 [==============================] - 0s 10ms/step - loss: 0.0028\n",
      "Epoch 50/100\n",
      "28/28 [==============================] - 0s 10ms/step - loss: 0.0025\n",
      "Epoch 51/100\n",
      "28/28 [==============================] - 0s 10ms/step - loss: 0.0025\n",
      "Epoch 52/100\n",
      "28/28 [==============================] - 0s 10ms/step - loss: 0.0026\n",
      "Epoch 53/100\n",
      "28/28 [==============================] - 0s 10ms/step - loss: 0.0027\n",
      "Epoch 54/100\n",
      "28/28 [==============================] - 0s 10ms/step - loss: 0.0023\n",
      "Epoch 55/100\n",
      "28/28 [==============================] - 0s 10ms/step - loss: 0.0025\n",
      "Epoch 56/100\n",
      "28/28 [==============================] - 0s 10ms/step - loss: 0.0024\n",
      "Epoch 57/100\n",
      "28/28 [==============================] - 0s 10ms/step - loss: 0.0025\n",
      "Epoch 58/100\n",
      "28/28 [==============================] - 0s 10ms/step - loss: 0.0026\n",
      "Epoch 59/100\n",
      "28/28 [==============================] - 0s 10ms/step - loss: 0.0024\n",
      "Epoch 60/100\n",
      "28/28 [==============================] - 0s 10ms/step - loss: 0.0023\n",
      "Epoch 61/100\n",
      "28/28 [==============================] - 0s 10ms/step - loss: 0.0026\n",
      "Epoch 62/100\n",
      "28/28 [==============================] - 0s 10ms/step - loss: 0.0025\n",
      "Epoch 63/100\n",
      "28/28 [==============================] - 0s 11ms/step - loss: 0.0023\n",
      "Epoch 64/100\n",
      "28/28 [==============================] - 0s 11ms/step - loss: 0.0026\n",
      "Epoch 65/100\n",
      "28/28 [==============================] - 0s 11ms/step - loss: 0.0027\n",
      "Epoch 66/100\n",
      "28/28 [==============================] - 0s 10ms/step - loss: 0.0022\n",
      "Epoch 67/100\n",
      "28/28 [==============================] - 0s 11ms/step - loss: 0.0024\n",
      "Epoch 68/100\n",
      "28/28 [==============================] - 0s 10ms/step - loss: 0.0023\n",
      "Epoch 69/100\n",
      "28/28 [==============================] - 0s 11ms/step - loss: 0.0025\n",
      "Epoch 70/100\n",
      "28/28 [==============================] - 0s 11ms/step - loss: 0.0024\n",
      "Epoch 71/100\n",
      "28/28 [==============================] - 0s 10ms/step - loss: 0.0026\n",
      "Epoch 72/100\n",
      "28/28 [==============================] - 0s 10ms/step - loss: 0.0023\n",
      "Epoch 73/100\n",
      "28/28 [==============================] - 0s 10ms/step - loss: 0.0024\n",
      "Epoch 74/100\n",
      "28/28 [==============================] - 0s 10ms/step - loss: 0.0021\n",
      "Epoch 75/100\n",
      "28/28 [==============================] - 0s 10ms/step - loss: 0.0023\n",
      "Epoch 76/100\n",
      "28/28 [==============================] - 0s 11ms/step - loss: 0.0021\n",
      "Epoch 77/100\n",
      "28/28 [==============================] - 0s 10ms/step - loss: 0.0022\n",
      "Epoch 78/100\n",
      "28/28 [==============================] - 0s 10ms/step - loss: 0.0021\n",
      "Epoch 79/100\n",
      "28/28 [==============================] - 0s 10ms/step - loss: 0.0020\n",
      "Epoch 80/100\n",
      "28/28 [==============================] - 0s 10ms/step - loss: 0.0022\n",
      "Epoch 81/100\n",
      "28/28 [==============================] - 0s 10ms/step - loss: 0.0021\n",
      "Epoch 82/100\n",
      "28/28 [==============================] - 0s 10ms/step - loss: 0.0021\n",
      "Epoch 83/100\n",
      "28/28 [==============================] - 0s 10ms/step - loss: 0.0022\n",
      "Epoch 84/100\n",
      "28/28 [==============================] - 0s 10ms/step - loss: 0.0021\n",
      "Epoch 85/100\n",
      "28/28 [==============================] - 0s 10ms/step - loss: 0.0020\n",
      "Epoch 86/100\n",
      "28/28 [==============================] - 0s 11ms/step - loss: 0.0021\n",
      "Epoch 87/100\n",
      "28/28 [==============================] - 0s 10ms/step - loss: 0.0021\n",
      "Epoch 88/100\n",
      "28/28 [==============================] - 0s 10ms/step - loss: 0.0019\n",
      "Epoch 89/100\n",
      "28/28 [==============================] - 0s 10ms/step - loss: 0.0021\n",
      "Epoch 90/100\n",
      "28/28 [==============================] - 0s 10ms/step - loss: 0.0020\n",
      "Epoch 91/100\n",
      "28/28 [==============================] - 0s 10ms/step - loss: 0.0019\n",
      "Epoch 92/100\n",
      "28/28 [==============================] - 0s 10ms/step - loss: 0.0019\n",
      "Epoch 93/100\n",
      "28/28 [==============================] - 0s 10ms/step - loss: 0.0025\n",
      "Epoch 94/100\n",
      "28/28 [==============================] - 0s 10ms/step - loss: 0.0020\n",
      "Epoch 95/100\n",
      "28/28 [==============================] - 0s 10ms/step - loss: 0.0021\n",
      "Epoch 96/100\n",
      "28/28 [==============================] - 0s 10ms/step - loss: 0.0020\n",
      "Epoch 97/100\n",
      "28/28 [==============================] - 0s 10ms/step - loss: 0.0018\n",
      "Epoch 98/100\n",
      "28/28 [==============================] - 0s 10ms/step - loss: 0.0020\n",
      "Epoch 99/100\n",
      "28/28 [==============================] - 0s 10ms/step - loss: 0.0019\n",
      "Epoch 100/100\n",
      "28/28 [==============================] - 0s 10ms/step - loss: 0.0019\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<tensorflow.python.keras.callbacks.History at 0x7f9a80141220>"
      ]
     },
     "execution_count": 629,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = Sequential()\n",
    "\n",
    "model.add(LSTM(units = 50, return_sequences = True, input_shape = (X_train.shape[1], 1)))\n",
    "model.add(Dropout(0.2))\n",
    "\n",
    "model.add(LSTM(units = 50, return_sequences = True))\n",
    "model.add(Dropout(0.2))\n",
    "\n",
    "model.add(LSTM(units = 50, return_sequences = True))\n",
    "model.add(Dropout(0.2))\n",
    "\n",
    "model.add(LSTM(units = 50))\n",
    "model.add(Dropout(0.2))\n",
    "\n",
    "model.add(Dense(units = 1))\n",
    "\n",
    "model.compile(optimizer = 'adam', loss = 'mean_squared_error')\n",
    "\n",
    "model.fit(X_train, y_train, epochs = 100, batch_size = 32)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### predicting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 630,
   "metadata": {},
   "outputs": [],
   "source": [
    "pred = model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 631,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(365, 1)"
      ]
     },
     "execution_count": 631,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pred.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 632,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "365"
      ]
     },
     "execution_count": 632,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 634,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test Score: 0.03 RMSE\n"
     ]
    }
   ],
   "source": [
    "testScore = np.sqrt(mean_squared_error(y_test, pred))\n",
    "print('Test Score: %.2f RMSE' % (testScore))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### plotting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 556,
   "metadata": {},
   "outputs": [],
   "source": [
    "# preds = np.empty_like(dataset)\n",
    "# preds[:, :] = np.nan\n",
    "# preds[len(trainPredict)+(look_back*2)+1:len(dataset)-1, :] = testPredict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 693,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(365, 1)"
      ]
     },
     "execution_count": 693,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pred.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 695,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(365,)"
      ]
     },
     "execution_count": 695,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 698,
   "metadata": {},
   "outputs": [],
   "source": [
    "#pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 697,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "#y_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 696,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test MSE: 20475.329\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f9a6e84ccd0>]"
      ]
     },
     "execution_count": 696,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "print('Test MSE: %.3f' % error)\n",
    "zoom_start = 100\n",
    "zoom_end = 200\n",
    "fig, ax = plt.subplots(2,figsize=(12,8))\n",
    "ax[0].plot(y_test[zoom_start:zoom_end])\n",
    "ax[1].plot(pred[zoom_start:zoom_end], color='red')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 699,
   "metadata": {},
   "outputs": [],
   "source": [
    "# trainPredictPlot = np.empty_like(dataset)\n",
    "# trainPredictPlot[:, :] = np.nan\n",
    "# trainPredictPlot[look_back:len(trainPredict)+look_back, :] = trainPredict\n",
    "# # shift test predictions for plotting\n",
    "# testPredictPlot = np.empty_like(dataset)\n",
    "# testPredictPlot[:, :] = np.nan\n",
    "# testPredictPlot[len(trainPredict)+(look_back*2)+1:len(dataset)-1, :] = testPredict\n",
    "# # plot baseline and predictions\n",
    "# plt.plot(scaler.inverse_transform(dataset))\n",
    "# #plt.plot(trainPredictPlot)\n",
    "# plt.plot(testPredictPlot)\n",
    "# plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}