Time Series Prediction.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Get the dataset and prepare it for analysis and model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Set the index to date"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Price</th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Change %</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</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>2017-08-10</th>\n",
       "      <td>64.165</td>\n",
       "      <td>63.898</td>\n",
       "      <td>64.175</td>\n",
       "      <td>63.855</td>\n",
       "      <td>0.48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-08-09</th>\n",
       "      <td>63.860</td>\n",
       "      <td>63.780</td>\n",
       "      <td>63.860</td>\n",
       "      <td>63.710</td>\n",
       "      <td>0.26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-08-08</th>\n",
       "      <td>63.692</td>\n",
       "      <td>63.750</td>\n",
       "      <td>63.785</td>\n",
       "      <td>63.615</td>\n",
       "      <td>-0.23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-08-07</th>\n",
       "      <td>63.840</td>\n",
       "      <td>63.710</td>\n",
       "      <td>63.865</td>\n",
       "      <td>63.648</td>\n",
       "      <td>0.26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-08-04</th>\n",
       "      <td>63.675</td>\n",
       "      <td>63.670</td>\n",
       "      <td>63.786</td>\n",
       "      <td>63.572</td>\n",
       "      <td>-0.05</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Price    Open    High     Low  Change %\n",
       "Date                                                \n",
       "2017-08-10  64.165  63.898  64.175  63.855      0.48\n",
       "2017-08-09  63.860  63.780  63.860  63.710      0.26\n",
       "2017-08-08  63.692  63.750  63.785  63.615     -0.23\n",
       "2017-08-07  63.840  63.710  63.865  63.648      0.26\n",
       "2017-08-04  63.675  63.670  63.786  63.572     -0.05"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('USD_INR.csv')\n",
    "df['Date'] = pd.to_datetime(df[\"Date\"])\n",
    "df_idx = df.set_index([\"Date\"], drop=True)\n",
    "df_idx.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Flip the dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df_idx = df_idx.sort_index(axis=1, ascending=True)\n",
    "df_idx = df_idx.iloc[::-1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Plot the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x11737ee50>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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Lk8xTkhL45I6R4QzJGFOHgFq5iEiiiCwFdgAzVXURkK2q29xDtgPVTgkjIpNE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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x116991a10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data = df_idx[['Price']]\n",
    "data.plot(y='Price')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total data: 37 years\n",
      "80 percent data = 1980 to 2009\n"
     ]
    }
   ],
   "source": [
    "diff = data.index.values[-1] - data.index.values[0]\n",
    "days = diff.astype('timedelta64[D]')\n",
    "days = days / np.timedelta64(1, 'D')\n",
    "years = int(days/365)\n",
    "print(\"Total data: %d years\"%years)\n",
    "print(\"80 percent data = 1980 to %d\"%(1980 + int(0.8*years)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Create training and testing data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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iTAEAgOTEapla+3ZwP9lgtfK12NcqOiVfUwEgTAEAgORYMVqmnD78S3LPWvxE7GsTvpXc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\nxZSIiIhIFP4fstD31No8O5gAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1175dc850>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "split_date = pd.Timestamp('01-01-2009')\n",
    "\n",
    "train = data.loc[:split_date]\n",
    "test = data.loc[split_date:]\n",
    "\n",
    "ax = train.plot(figsize=(10,12))\n",
    "test.plot(ax=ax)\n",
    "plt.legend(['train', 'test'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Normalize the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import MinMaxScaler\n",
    "sc = MinMaxScaler()\n",
    "train_sc = sc.fit_transform(train)\n",
    "test_sc = sc.transform(test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "train_sc_df = pd.DataFrame(train_sc, columns=['Y'], index=train.index)\n",
    "test_sc_df = pd.DataFrame(test_sc, columns=['Y'], index=test.index)\n",
    "\n",
    "for s in range(1,2):\n",
    "    train_sc_df['X_{}'.format(s)] = train_sc_df['Y'].shift(s)\n",
    "    test_sc_df['X_{}'.format(s)] = test_sc_df['Y'].shift(s)\n",
    "\n",
    "X_train = train_sc_df.dropna().drop('Y', axis=1)\n",
    "y_train = train_sc_df.dropna().drop('X_1', axis=1)\n",
    "\n",
    "X_test = test_sc_df.dropna().drop('Y', axis=1)\n",
    "y_test = test_sc_df.dropna().drop('X_1', axis=1)\n",
    "\n",
    "X_train = X_train.as_matrix()\n",
    "y_train = y_train.as_matrix()\n",
    "\n",
    "X_test = X_test.as_matrix()\n",
    "y_test = y_test.as_matrix()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train size: (7451 x 1)\n",
      "Test size: (2245 x 1)\n"
     ]
    }
   ],
   "source": [
    "print('Train size: (%d x %d)'%(X_train.shape[0], X_train.shape[1]))\n",
    "print('Test size: (%d x %d)'%(X_test.shape[0], X_test.shape[1]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Setup baseline model of SVM Regressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.svm import SVR\n",
    "regressor = SVR(kernel='rbf')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/neelabhpant/anaconda/lib/python2.7/site-packages/sklearn/utils/validation.py:526: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    }
   ],
   "source": [
    "regressor.fit(X_train, y_train)\n",
    "y_pred = regressor.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x119948810>]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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g+ULPWdv0io7rgXL66O70y06lvLrGq9g3eF/H5VY+8tgYJROlUUKDhbtSqr+yvm2l1Ggg\nEdjX0OMKQjRQ49R+Au/uj1cE7Ltjf7nfvi4WbS1snAEGodKKNE2MiyEpIRatvX32famorqF7u2Sm\njWp8Fz4hMoTjCjkTmAsMVEptV0pdrpSarpSabnU5A1iulFoMPAWco31VAEFopTi1Dugi6IrOtBdP\nvuNDb29iuzA9/elf/Mw2jYnL7z45IdadvbGiuganU/PTur1+YyksqyZvfzmpCRL3GC2E/Ka01ueF\n2P5P4J8RG5EgRBEuzd2XwrIqslITWJZX5G5bvG0/ByodbCssY8rjP3L1UX299vl61W5OGNql0ccM\nHnt6VmoiYAqOlFY6+G/udh6YtYo7Tx4ScL9vV+cHn1QTWhQSoSoIDaBGBxbuFdVOtwvhNZP6uduH\n3v0lUx7/EYDn5nhX5rn69YWNOFIPz/6wgWlP/QxAh7QEOqQaj5/dxRWszy8F4P5PjQePb1Hrh84Y\n3iRjFBqOCHdBaADOIJp7SUU11VYisS6ZSQH7BOLW95eF7tRAZtgeKtmpiWRZk8FnPDOXd3K3efV9\n6IwRHNm/g9k+ugcTBogjRLQgwl0QGkCN1sQGsLmf+uTP7oyR8bExvHnFYUGP8dfjD3IvR7KIw/yN\n+6zCGXvJsyZz/5u7zW2SAchKS/AqXO1LRlI85x5qIk5PHNY0JiMhMohwF4QG4HRCTBCt/G/vGS3c\n4dS0SwleM/ToQY2TPXH2KpPo6/zn53PkQ98CcNP/lnr1SU2IpVN6kls79yU9KY5TRnbjyz8dxeQh\nnRtlnELjIMJdEBqAw+l0a+4zrzycs8f0cG9z+a+vyCvyKgitFBzcqx0APbOSGdotkxX3nsAhvdub\nY0YoqCk1sXZ/iWcuGO124XTZ3X3p1s5o9QO7pEdkTELTIX5NglBPtNYUllXTLtUI7nH9OlDj1Lyb\nu92r343HHUR6kke4b3pwKtOeNKXq+mSnAUYQHzekMwu3FFJV4yQuRLEHX35ct4cFmwq48fiB7jbf\nqNNr31oEwNEDO/LyZWO9ttmLWT985giGdssgv6SyTmMQWhaiuQtCPdldXEmVw+nOHwPQtV2SX79O\nGUkkxXv/qz3wO+N18uhZI91trgySM3/dFrTiUTAuevFXnvh2vVeU6ZJtRV59Plu6E4B9B/yrAN07\nbah7+ewxPRnaLZOjpdhGVCPCXRDqyR/eNK6L9gnKfh3TAvb1Ddkf1j2TzQ9N9ap+lGAJ9/s/XcnZ\nViHlurKn1KNtf7Zsp9/2CQOyeer80X7tSfGx3D9tKNcd079e5xVaHiLcBaEeFB6o4ret+wE4zyd/\neUaSx9r5zlWHh31MV2EPgB1WRaPX5m7mjg9rd4+0a+v3fmz802fM2RCw7+uXH0bPrJSA2y4al8Nf\nbGYdIboR4S4I9eBdmz94u2RvT5gj+pm8eT/cNInD+nq8ULpmJvkFBdnpk+2/7a6PVvDGvNrdI71y\nwigTNfuPWav9+q2874RajyO0LmRCVRDqgd3k4euV8u9zRvHngjJ6d/AW1r/ccoxf5SY7HVI9tvth\n3TPCHotduC/YVOCV8mB490zumDqYYd0zSZG8MG0K+bYFIUzOeOYXdhVVcNcpQ1i63SNAXbZyF8kJ\nsQFdB5VS1JYtt3eHFG487iA+WpxHvI+3jNY6aKpdV7AUQH5JJde+uci9ft+0oRzcq32t1yW0TsQs\nIwhhsnBLIXn7y71ywNRmZqkrSimuP3YAB3VOp7TC4ZWZcZ2V8yUQrvS9I3sa3/k8W2phEextFxHu\nglAPBnY2mvmXfzoq4sdOS4yjtNLB3gMez5fa6qy6zDK/PzLHq33OTUdHfGxC9CDCXRDqQbd2SYzo\nkVnnYKNwSE2MY2dRBec8N8/d9vN6T/2bL5bvYvg9X1JwoIr84gru+sjkiU+Mi+WScb1tx5FC1W0Z\nsbkLQj1wBMkGGQle+WUzAJv2HnC3vfTzJu46xeRYf/aHDZRUOBh9/9de+yXGxXDvtGEM6prB47PX\neqU8ENoeorkLQj2ocQbOBhkJrp7YN2D7B7+ZtAaJcYH/bV0Tq+eN7cX82yY3yluFED3Ity8IYVDj\nU3auqLy60TT3Ed3buZdz75hM/04m6vXP7ywBAvvDJ8fHctwQSckreBDhLghhsHpXsdf6ih3FfgI/\nUkwaaApi3DdtKNlpibw3/QgATh/dHYCSSge9bFGmb11xGKvun9JoDxshOhGbuyCEwdrdJX5tG/YE\nd09sCKmJcWx+aKp7PTMlnuy0RJKszI2lFQ7apcSztcBsH9WrXaDDCG0c0dwFIQwe+Mw/nN/RSJp7\nIPaWVvLWfJOG4EClg7TEOObcdDSr7psikadCQES4C0IY7C31z23eWGaZQLg8X2av3E3ulkL2lFTS\nq0MKyQni7igERoS7INSBaaO6uZebUrife2hPEuNiuOK1XKD2iFVBgDCEu1LqJaVUvlJqeZDtFyil\nliqllimlflFKjQzUTxCiGZd74n/OPZj/Th8HUGsSsEiTGBfjlSDsrSuDF9wWBAhPc38FmFLL9k3A\nRK31cOB+YEYExiUILQqtPaXoencwniopTRgBmmgrg3f9sQPcaYUFIRghZ2K01nOUUjm1bP/FtjoP\n6BGsryBEKzPmbHQvZ6cmctmROX5FOhoTe+DScYM7N9l5hegl0tPslwOfR/iYgtCiiIlR3H3K0NAd\nI4hdcx/eI7NJzy1EJxET7kqpozHCfXwtfa4CrgLo1avptB5BaCjpSXGUVDia7fwFpf5FrQWhNiIi\n3JVSI4AXgBO11vuC9dNaz8CyyY8ZM6YJp6MEoWHkdEj1Kmbd1BRXVDfbuYXopMGukEqpXsD7wEVa\n67UNH5IgtCxen7uZZXlFlFfVhOzbWBzWJ6vZzi1EJyE1d6XUTGASkK2U2g7cDcQDaK2fBe4COgBP\nW2XAHFrrMY01YEFoamb8aCZTSyubzyxz/NAunHtoT47sL14yQniE4y1zXojtVwBXRGxEgtDCqHYY\nC2JzCneAh84Y0aznF6ILiVAVhBDsKq4AvItnCEJLR4S7IIQgRfK3CFGICHdBCMFkK2iokQovCUKj\nIMJdEEIQF2ukekq8aPBC9CDCXRDC5P1rjmzuIQhC2IhwF4QQOGo0fbJTGdglvbmHIghhI8JdaLH8\nZ/Y6Dv/HN809DGqcWuqTClGH1OcSWiz/nm0CngsPVNE+NaHZxlFcUS0eM0LUIZq70GJpn2JKy81Z\nt6dZx7GzqIJumcnNOgZBqCsi3IUWy1grn0re/vJmG0Olo4b1+aVNWphDECKBCHehxeIqUepswlql\nvryzYBsA7y/Ka7YxCEJ9EOEutFi0VaS0xhmiYyOyv8yk2rUXxhaEaECEu9BiqXG6hHvzSfficiPc\nHz5TknYJ0YUId6HF8fGSHSzdvp8yK396jda8Pnczl7z0a5OPZVdxBX2zU0mME5u7EF2IK6TQoiiv\nquH6mb8B0LdjKgAOp+bOj1YAxv4e04Q+5wcqHaQmyr+JEH2I5i60KC5+ab57Ob+4EoB3rUlNgEVb\nCwFYuKWAE//zY6NXR9pWWE5inPybCNGH/GqFFsWCzYXuZVdxjMIyT/3QM5+dy/NzNnLGM3NZtbOY\n1buKAx5Ha83P6/e6J2Xrw4FKB+vzS8ndUhi6syC0MES4C1HHA7NWuZedQYT3xS/9ygUvzOfTpTvr\nfZ7leUX13lcQmhsR7kJUs70wcIDTj+v2ArC1oKzex3ZVYOqZJdGpQvQhwl2Iam54e3Gt27tmJtX7\n2MUVxiz07tXj6n0MQWguRLgLrQ57RKsr4dfKHcXc+8mKOtngSyqMrb9dcvMlLROE+iLCXWhRdM5I\ndC/375QWtN97fxhHQhAvlke/XuNernQ4eX/Rdk564kde/nkze0urwh5LSYWDuBhFUrz8mwjRh/xq\nhRZF+5QE+ln+7Qmx3j/PFy8ZQ4fUBL776yQO6Z3FFeP7EB/r7/P+1Hcb3Mt7S6u48d0l7vXiCo/n\nTY1Tc9N/lzB75W4A9pRUerlW7i+rIjM5HiXFU4UoJKRwV0q9pJTKV0otD7J9kFJqrlKqUin118gP\nUWhLVDqcDO6awfSJ/Xj6gtHu9u//OoljB3dm4Z3H0SfbEv5xMVTX6FoTi93/6Uqv9Qtf8PjRb9xT\nyn8XbufOj8xP+9AHZnP2c3MByC+pYOav25o1j7wgNIRwNPdXgCm1bC8Argf+FYkBCW2biuoakuNj\nueXEQeRYQhwgOz3Rr2+8pdlX1yH3zM6iCvfyAUtL31lU4X5ALLPcHxdv3Q9ABxHuQpQSUrhrredg\nBHiw7fla6wVAdbA+ghAu5dU1JMX753FJDtDmMttU13g09wdtPvDBcCUkK7W8YQDmbdrnXv5ta6Hb\nU0YShgnRitjchRaD1pryqpqAE5iBapi6JlSrHB7N/bk5GwG45cRBQc/jEuoXvugx0Zz/vGf5nBnz\nWGhFpbZLEc1diE6aVLgrpa5SSuUqpXL37Gne0mlCy2P1rhIqHU66tQsvaMhlllm5wz8FQW35YEoq\nq3nl501Bt1c5nMz8dSsxCtIlaZgQpTSpcNdaz9Baj9Faj+nYsWNTnlqIAvJLTKKwYd0z3W1dMoIH\nIbk8ZewauIvutTwgKh1O7vlkZdDtLpyaJs1AKQiRRMwyQouhotpMcNrt61/deBTzbzs2YH9fU83c\nDR67+XFDOrPxHydx8oiuPHn+wax74ES3982xj/4Q8HhXT+zboPELQksi5DunUmomMAnIVkptB+4G\n4gG01s8qpboAuUAG4FRK/QkYorUOnK5PEILw9PfGP91uUslIiicjKT5gf7utHeDtBVvdy0oplIIn\nz/e4UwaalAX48+SD6N8pjakjuvLcDxvd7ScN71L3ixCEFkJI4a61Pi/E9l1Aj4iNSGizLNlm3A/D\nDRoaPyAbgDG927O/rIqPFu+otX8gE0uH1AT+MKmfe3J2yV3HM/rvX1Pj1LSXyVQhihGzjBBRHDVO\nHvlyNQUHwg/z96W2tAN2erRPoU92KrlbCql0hPZ1H9I1w2v9lhMHsfDO47zSGGSmxHPmaKOr/O7g\n7nUYtSC0LES4CxFl0db9PPXdBm7675LQnW3sL6vfw2DT3gMALNjsCcWYeeXhAft2TE/knlOGuNcH\ndkkP2O/OU4bw5PkHc0jv9vUakyC0BES4CxHFZfnYXVLhLokXDrd/GDC7Rdi4fNevntiXcf06BO03\nsmc793JaEDfHtMQ4Th7RTXLKCFGNCHchojis6M/lecWc/vQvbNl3IKz9wu3ny0GdjQmnusaYZQ7r\nk1Vrf3ux69QE8WEXWi8i3IWI4uvBsi9M23uZleclp0NKnc53x1RjZlm9qwSApLjAHjEu+nf02PO7\nNKCQhyC0dES4CxHFd2LTnr+lNvZbRbDtAUzh4JoMfXO+cYNMSqhduMfEKMb3N142WZIUTGjFyHup\nEFEqHTVe61+t3MVRB4WORq60Apg6Bsj+WBu+AjqU5g7w8mWHus04gtBaEeEuRJTKam+hqQhvUrLK\nErY3nTCwTufr7JOewGWDr4342Bh3XhpBaK2IcBcigtaa7YXlfmaZovLQmaC/WL7LnbY3pY6TnPYM\nkn07phInQlsQALG5CxHijXlbmPDwd+Ta/M17d0ihMAz/9cdnr633ee2l+O6cOqSWnoLQthDhLkSE\nJdtNBaPfrBQCndIT6ZWV4i56URujrWChL/40oc7ntfuip4SYTBWEtoQIdyEiuAKCXBGjc289lsS4\nGKrDSAtQUFpFelIcg7pkhOwbCJd8T5Xc64LgptUI9z0llWzYU9rcw2izZCZ7MjfGxShiYxTxsTE4\nwqhv+sWKXZRX1YTsFwxtVdlLFs1dENy0GlVn3IPf4HBqNv7jJCmw0AykJnoEa4ylSsfHxnjVNw1G\nSkIsQ7vVT2v3PY4gCIZWo7m7wt5n/LgxRE+hMbC7QLoqJH2+fCeb9h7w8333JT42xi9jY32QoCRB\n8NBqhLuLT5fWntNbaByqbEFBLhONS2vPL64Mut+BSgdF5dU4Qyv4QXn50kM585AeJIYRwCQIbYVW\nJ9yX50kBqObAnlMmxZrYPHZQJwB2F1cE3e/4f88BYOavW4P2CcXRgzrxr7NG1nt/QWiNtBrh3skW\ntq51A9SL1UD/AAAgAElEQVRAoV7Yg5dctu9bTxoMeDxo7ByodPDoV2vI218OwPAedcspIwhC7bSa\nCdUkW33MZXlFjOjRrpbeQqSxC3dX4FKP9skA5Jf4m2Wmv7GQH9ftda/PuGhMI49QENoWrUZzd9hs\nvqc++TMv/bSpGUfT9jhQ6QlWGmk9WBPjYlDKkxTMzk/rPYI9PlbVOWGYIAi102qEe5VPlr8XxGum\nSSkqr2ZQl3RunjKQR8829m+lFIlxMVQECGTKshWffvjMEU02TkFoK7Qa4V5eVUNXW/EFsbo3LXtL\nK+mamcQ1k/p7ea0kxcdSEUBzt8ciHNxTapUKQqRpFcJda01ZdQ2TBnryhu8sCu6hIUSenUUVdG2X\n7NeeGBcTULjb2+zzJYIgRIaQwl0p9ZJSKl8pFbCCsTI8oZRar5RaqpQaHflh1k6lw4nWkGELgRea\njiqHk4IDVXRO9y9bZzR3f7NMd9uDIC5WIooFIdKEo7m/AkypZfuJwADr7yrgmYYPq2648pJkJHkL\n9+oaJzuLypt6OG2OUmsyNTPZ3/kqKc7fLLNk236qa5wM6JTGHyb1o4NElgpCxAkp3LXWc4CCWrpM\nA17ThnlAO6VU10gNMBzKLOFh19wHdUlnwO2fM+7Bb/l4iUStNiYlFaYgR3qS/5uTRrM8r8i9vmZX\nCdOe+pkNew7QJTOJv00Z5JW2VxCEyBAJm3t3YJttfbvV1mSUVxnNMSPJozmu3lXiXp6/cV9TDqfN\nUWLlbE9P8tfc1+4uZUdRBTVWfoFymxZv93MXBCGyNOmEqlLqKqVUrlIqd8+ePRE77utztwCmRNvm\nh6b6bW9I3hIhNI99bSoppQXIpz6qp/F5d2nv9sLUt544qAlGJwhtk0gI9zygp229h9Xmh9Z6htZ6\njNZ6TMeOHQN1qRevuoV7YK8LSUfQOFRU1zBr2U6WWlWY+nfyL059yRG9AZizdo97HxfHDu7cBKMU\nhLZJJIT7x8DFltfM4UCR1npnBI5bZ1zFGmZd712uzZW/RIgsL/60iWveXITD6aRjeiKdMvy9ZSZb\nAtz13diLcvTukNI0AxWENkg4rpAzgbnAQKXUdqXU5Uqp6Uqp6VaXWcBGYD3wPHBNo402BC7NfUi3\nDK/c3j+u28s9H69ormG1Or5fk091jZMt+0xCsP1l1V6Fqu24Apo+WWqe9y77/E0nDCQ+yD6CIDSc\nkInDtNbnhdiugWsjNqIGkBLvuZwrJ/Tln1+sdq+/8stm7jl1aHMMq1WRu7mAS19ewNEDO9KjvUfz\nTogLLKhdhTuWbNvPhIe/ZerwbgCcN7ZX4w9WENowUa862e3pSQmey5k+sS+r7/d2z6+RmdUG4wpI\n+m7NHl6ft8XdHh8kEMnu5ritoJxnf9gABPasEQQhckS9cLfX6ExN8AgMpZRfWHtZlYNQ5O0vD1kW\nri0TTChvKwg+rzF1uH/Yg5hkBKFxidr/sJ/W7WXSI9+5A2iuO6Y/qQFc8W46YaB72ekfBe9FjVNz\n5EPf8se3fovoWFsTziCeR+UB8se4GD8gu7GGIwhCEKJSuBeVV3Phi/PZvK/MHQgTLB/4FRP60Cc7\nFYAaH8H02dKdLN2+372+w/Kq+Xrl7sYYdqugPqatpPio/JkJQlQTlYbP85+f517+0zuLAZN9MBCJ\ncbFcdmQOd320wkvrLCqv5tq3FgGw+aGpLN62n9Oe+rkRR906cAQR7g+fETwne1yM+W6mDu/KQZ3T\nObSPpPgVhMYmKoX7ih3+RbBri1OKsSb1nDbB9NhXa7z6XPvmIq/1z5ft5MQAtuK2jusevnPV4RzW\ntwM5t3wGwBmH9Ai6j2uytbrGyQ2TBzT+IBsDpxNenAy7lsGIs0HFwvYFgILi7fDHXHjnQijdDeld\noaIY0KCdULwDuo6E6jJw1oCuMcfTTijZCZ2GgNMBNVWgFCS3N9uyDzL7VJZAZk8YdDL0PAzyV8KS\nmRAbDwlpUFkMhVugcDMkZUJKFtRUm/M6q825YmIhJs7sExNn1qsOQGIGZPWBLXNNG0BsAsQlQVwC\nFO+EpAwzbhUDeblw6JXQaZDp1220Oef2X6FkF8SnQPYASOsM6V0gMb0Zv7QWSPl+yF8FPQ6F2MYV\nv1En3INFmwayt7uItQpDuMwy936ywh3VClBwoIoOaQlewU5/eHNRwFQGbR2X5u5K0zuyRyZLthe5\n73EgXC6Tw7pHaRFspxNeOh7yFpr1397w7/Mv20MrJRva50BMDOxdZ4RvwUbo0N8IxNh4IygBKvZD\n2T7I6Aqx7YxQL9sHO36Djd9DWheIT4aVH8HcJ4MMUJnjdRkOjgrzAKo6YM6XkGqEudNh+6sxwn/X\ncqiphI3fGWHe92jzcHFUmgdNZSkcyDfjTUw3Dy6ABc+Hd986DIDpP0G8Lbht6buw6DXoORYm3mIe\nINHM5p/h3YutB3APKNjg2dZxEOxZDQNOMPeweAfsXmHu+aFXwtR/NerQok647y+r9msb3DWDk0cE\n17JdcseluL/882av7e8v2i5pZ8PEZXOPtUwtb115eK2TqWCE+mfXj2dQl4xGH1+jsOAFS0sHznkT\n0PDVnXDiw9B1BDzqmbTn1jxItKVh0Br2rDGabl2ocRhN2uVKuvF7eG2aWR7/Zzj4IsjoZjTB9C6m\nva7ZNcsKYP8W82DI6AGpHULvs2cN/PwEFOdBv2PMm4HW0HmYGYeugZ/+Das+gX3r4IEu8Jc1sHct\nzDwXqkrNcTb/CAtfgSkPwYDjIbkdVJXBvvXmntaGo8o8BFVM8z0cyvfDcxNg/1ZPm1LmgbpvvVnf\nZwn6Lb+Yh263g6HvRPOAHdf4oUFRJ9w37i11L4erWQcyy9j5+2erOG6ICZMfm5PFr5tNhmOttaSj\n9cGtuVtPzNTEuFrfmlwM7VYPrX3tV/D5zXD2q8as0Vys+MB89jsGBp9slgefErhvok9+HaXqLtjB\n/5W97yT480pISDFmGxfx/tWvwiYly/zVhY4D4bSnau9zzhtG63+wO6Dh0YO8t5/xInx5O5Tugvev\nNG2JGeYhUWVlc71+sTEXudDavDnMPNe81diJTYCLPjQPuwUvwNG3m/tUVmD2m/ukMXF1G2XeHH76\nN/SfDOu/Nm8Xg06C7blQXQ5XfGPeuALhqDIP2bK95gG3f6t5qE2+BwYc5+lXUWwePK7fgutNKd4/\nPUdjEnXCvbg8tK+6L26zjFMH9fZweci8fsVYBt7xBQD/mLWKm6cMEp9sGy7X00AZICNCjQNWfQSp\nHWHlh1C4CebPgIk3wZrP4bDpHg21sqTxbbrrv4Gtvxg7+kUf1N43uY6Csq5kNmkm7YaRmAaTboXv\nH/S0Df0dHHc/tOsJw86AD/8Aa2ZBRZExXdl5YhT8dZ0xD2X2gK/uCG6WqqmCV07yrAc1X9lY/7X5\n3LcOfv6Pp33Hb5DWEdZ8AaPO8/y+qivgAZ9Ed73Gwe+/8D92ks8bakysZz6jCYk64X4gjEAkX9ya\nu9bufCjBSIyL5cnzD+aPb/3G8z9uon+nNM45VELlXRSXG+HeaCUN5zwCPzzk3bb4DfMH8MUtRoiW\nW/VjfjcDRp7jf5zdK+GZccbm22V4/cZSdQDeON0sX/Z56P4DTwrdpy0x4a8w8lyjQXccZLRpF0rB\n7541y1vmwq/PQVY/OPZOeHa8mTdwzWMMPhVWf+rZ9+o50L6PmbwFWPwmfHJ98HF0GACjL4av74R2\nvc3E78AT4Zg7oLzQzIsoZR42P//bKBFOB3x+U+Dj9TsWjroJeoyp/71pAqJOuJ88ohtjemfVScjH\nxHiE+8Y93sJ90Z3HMfp+8xR3paw9rI/H9ljpCBH51MZwldRrNM19wzeh+5TbCoN9cBUMOdXbPLH5\nZ48mN/dpM4G1/D3jWXL1HOjQz9N3e67x9sgeAKX5xgvGZXte9Yn57DvJ20QQjJMfC92nLREbZyaW\n2+fU3q/3OPPn4uKP4WHb/V71sfk87j448gb//Q+5xJhdUrONzfvNs8xb3XkzzXfr0pqPDPIA6DnW\nmG9yX/J858G4e3/d5zaaiagT7gBdMutmu4pVLrMM7C6u8NrWPsWjgV59VF/AOyDKlcVQMJRW1pAQ\nGxM0UViDibdpdwOONxNxhZvNelKmeYXvPMxs27nEPAxKdkKW+e4o2OT9ir7kLc9yVSn832i4bafR\nIrfOg5dOMNsGnezRDi+dZezaH1xt7LnnvV37mGMTjGkgLnAgnVBHUrLgniIoyoOFL5u3OTDug8Gw\nPxwu/F/dz6kUnDsT/tXfrP9tszHFzH3S/L4GTTV2+igR7BClwr2uuLxlPlmyg2qnE6XgqAEdufTI\nHK8J0362YhMfXXsk0576mYIDVU093BaLo8bpTvzVKJQVwKYfYMQ5cNozZlLqiYPNtps2GM3Mzrqv\njXB//2q44mujfT0xKvR5vvib8Vz49M+eNvtrv/3hcOLDoSctb1wNDqkZEHEyuxvTyaTbjCtpdv/G\nPV9aR/NdJmVaE9fACQ807jkbkTYh3DMt+/CT3613t736+7F+/QbYhPvInu3o1zGVnUWR/6fduKeU\nYx79gc9vmMDgrtHjHrizqCJ0J4C1X0L3MeG51tl5xDKX9D7C8yp93tuw7it/wQ7GOwJMAI3TCffZ\nvEj+vBK2zoX3Lje2395HwJaf4cdHjZ/1otf8j2d3Y3Mx+pLQ467rdQp1Iyam8QW7i4zWE7jYJoT7\nCKuOZyjSk7wnCbNSExpFc//K8sz58Lc8L+FeXlWDUvhls2wpVITwZweMC9xbZ0NGdzNpdexdRhMK\nxdb5xncZjCeFi06DgrsSdhriWV4y07OcPdBofcPPNH8u+kyEFR96Ak16jDUTbaPON28JSpnxL34T\nZt8Lpz8X3C1OEFo4beKXG2ryb8KAbC4e19uvPSk+lkqHk5KKar5YvjNiqYBdcwDbCsu82ofe/QWT\nH/shIudoDFyTy/Z5Cj9cUYzFecbneNbN4R18zSzPcrjujUqZSTaAj6wCYGOvhmvnB+4fGwd/XOBZ\nP+EfMPoi72ChxDQ47Gq4fUdwX3ZBiALahObuy7MXjvZaf/3ywwL2S4yLoeCAk+tm/sb3a/bQJzuV\n7/46qcHnd/ndz1q2i+/X5DNpYCe2FZTh1LC9sBxHjZO42Bh+WreXGq2ZeFDkiok3BJfm/p9zD/bf\n6KyB+c/5a9mlu0IfeP82+Plxszzt6boN6vBr4Ou7POuHXFL7pFdMLNywxEQN9qxlgk4Qopw2obn7\nMqRreNGSCXExVDqcLN1eBMCmvR43yhU7inj7163Bdq0Vu+y59OUFVFTXMOHh79xt7/+WR0mFSWt8\nyUu/1uscjYFLcw+YgXP9N/DlrfD67zxtWf2Mv3ko7EEnB19Qt0HF2t4iDrkMOodRSrF9jjHFCEIr\npvUI928fgHsyYefSkF27tw8vZDshNoYqh9PL7r6twJhSpj7xE7e8v4z1+SXubS/8uJGjbEI6GFsL\nvM0xe0oqvdZ37q9g+D1fudd/snLWNzcus1RioDkB7WOyuuoH6H+sSTzlDGHOyrceAHfuq9/AOlpv\nCxJEJAhuWo9wn/Ow+XxugskB4cOzFx4CwF+OO6jWDIZ2UhLjKKmopns7z8NgwsPfcd4MTz75yY/N\nwVFjNNq/f7aKrQVlQTNXuvAV5r9s8Bbe/5691mvdlbO+uXHVTw1YfKO80Hu9y3ATeQgmgCgQVWWw\n7H+waY5Zr28K1PGWS2Nz5p8RhBZG6xHu3Wx24A+u9mRks5gyrAubH5rKdceGn0+8e7tkCsuqvVIB\nA8zd6K1h/v2zVV7roaJafbf/7b1ltfbfW1pZ6/amwq25xwXQ3MsKvNdjYuFcK2XA9gX+/VfPgn90\nNa6KYPKV15eR58JdhZDeOXRfQWgjtB7hHmeLWl3xPjzZ8MmycM0hr/yy2Wv9wVmrWGbZ6QMRTPjP\nvnEio2xumxlWMequdYzIbSzKq2rR3As3eZbH/dF8ts+BzsPNhKkvvjnRr1vYsMGJy6IgeBHWf4RS\naopSao1Sar1S6pYA29srpT5QSi1VSv2qlBoW+aGGoHS3qQrjwtcGXA/sE59HD/T3WPn2LxMBOHlE\nVz5ZssPd/urcLZzy5E9Bj1sVxKWyf6c0umQYQX5Evw4suft4wAQP1ad2aaRxZYT0jQcAYNt86HOU\nCRu3R/WldzHh277Y3R1Pf0FC9wUhwoQU7kqpWOAp4ERgCHCeUmqIT7fbgMVa6xHAxcB/aGpK95gS\nZHZm32smWVfPCrxPCE4d2c29fNmR/omj+nZMo1N6IvnFlVw38ze/7cGodDjJSIrjG+vhAB73yB7W\nZG+7lHiUUpw+2qR5/XVTgf+BmpBnvt/Ag5+vJi5GkZrgY5bR2mTW6xKgyEJcIuxc7KmDWOOAx0fA\nUitfS6ehcNDxjTt4QWiDhKO5jwXWa603aq2rgLeBaT59hgDfAmitVwM5SqmmM4DmvmSS/Kd18nhO\nAPxkZen7/G/1Ouw5h3rswGlJgSf70pPi2FJQexphX6ocTg7NyaJfR0+6g9MPNkLclahs1jLjH377\nSYMBWL3Lv25sU/LPL1YDMKJHpncBk+IdJpOeo8LkPPelwjJPLXjBfJbuNtV/XFzzS3gRrIIg1Ilw\nhHt3wG403W612VkCnA6glBoL9AaCV0yONK4EUGmdTHTiNJ9KMUVbTRrYOqKUok92KgAjumdy/zSP\nD/W7V5ssdBnJ8ewu9p/wjI1RQb1mKh1OEn3s1g+ebnKOO619jrICl9qnJBCjCJgGQWvNwi2FVPnY\n8A9UOsi55TP+t3B7WNcZCnvaga6ZyXBgn3Fv3DoPHhsMH1s29v7H+u98mhWU5IpAtQc1hSp+IQhC\nvYlUhOpDwH+UUouBZcBvgJ9hWSl1FXAVQK9ejVAAw2XHPfhCkyrWXmHllZNMGk97ibIw+ObGiShl\nBP1F43K4aFyO13Zf+/P3f53E8z9u5M35W3nll80BzTlVDicJPtWd4qz1W08aTEpCLLdaGntMjKJ9\nSgL7Agj3uRv3cf7z8znrkB48cpbHDfBHayL4H7NWccbo7g0uFfj9mj3u5ZS4GnjESq/rSsFaUQQo\n77cmF+2s73nDt8ZENsgqU5czwZStEwShUQhHc88D7H5qPaw2N1rrYq31ZVrrURibe0dgo++BtNYz\ntNZjtNZjOnZsYEh9dbnxka6xFcxO7eRZPvp2uOJb70IBxZ5Jz6Asnuml5cfEqFqFo91lfv0DJ5KT\nner2eLn3k5VuH3h3n/xStheWud0JffPeZKUmcO+0YV7Jw3q0T2bNrhJ8Of95k0PFlYjMxfQ3jOdJ\nwYEqbni74T7yruMBjOpsK0js5eKog4f9p9ksdK7UuqfPaPC4BEEITjjCfQEwQCnVRymVAJwLfGzv\noJRqZ20DuAKYo7VuHCNxTbWZvHugi/GRnnmeae9xqHfC/rhE6HGIKZzrwuGTsvbDa02xXDCBT0ve\ngQ+ne+fzDoFdOLu079NHeyxS/W//nOIKzwNo8mM/4NS4i0rPuflofr6ldg12TE4Wy/OK/B4ULu2/\nXUo8r8/djNOp2V/mreF/vGQHy/OCu2WGg0tm33nyEM4b3aXuBzjnDf+2QPZ5QRAiRkjhrrV2AH8E\nvgRWAe9qrVcopaYrpaZb3QYDy5VSazBeNQFqYUWIlR/Bk7baha5Ct8GCYLL6wIXvm+Uaq6pSab7R\n+he/AbPvMRVXFjxvSra5+PL2sIaTnWZc+M48xCPQfSNg37Ns33bh3CXT7JeVmuAVARuIvh1TqXQ4\n2VvqEdxVDidV1vG27Cvjzo9WcMM7ixl139d++5/8fz9RXRM8sOqL5buYtzFw6P/yvCK3o8vl4/sQ\n4wySAnmin4esB/t3M/leU/c0iiraCEI0EpbNXWs9C5jl0/asbXkucFBkhxaEhLTA7bUlnHIll3Ja\nGvS/fKJUXz/N5Pq2M/dJU6k9RHCMq3rTEf28Czasvn8Kczfu47KXF7gnQ6tsArZzRviBSVkp5qWo\nsKzKXWJwxhz/ikh2X/sLDuvFm/M9ic3ezd1Gcnys11sFmFw5LrPL5oem+h1zw55SAG450bKn11jC\n/fTnYcTZsPJj45U04S/BLyCtsylqfdx9JsWuIAiNTvSl/K2wmRjOecN4beSMD1ypx0WMdZl2+7yd\nrXPNH8CQaebtAOCja01ATkpW0ENfMLYXXTKSmDy4k1d7UnwsRw80bSt3GAtVtcPjPVMX4d4+1RLu\ntknVvP21V0W6f9ow4mIUr841boe3f7AcgBOHdSXZ5qduz0Z5oNJB3v5y9pdVM7aPueZdVvWlCw+3\n8t1XWrZ/V63TIaeav9qIiYG/baq9jyAIESX6Yrb7HOVZHnQyDD2tdsEOEOPS3B0m2Kk2zn4Nfm9l\nZFzyFsy6qfZDxyiOG9K51knXb1bnA1BZ43Eg6hFmZkrwPAjW7vZMqva33hgO6xP4wRMTo7jrFP/0\nt79t8yT48i0+ct7z8zj+33M4+7m5bjfOXcUVpCXGeeYWiq259IxuCILQcok+4Z7RFe4qMBXsw7Xb\nurINbl8Aebne2+4p8gTRDLFis3rZIl231N0/PhCllQ7KKu3CPSXsfXM6pJAQG+OVwKyovBqlCFiD\n1ZW2IDbGE+Hq4ub/eVIir9jhPee91JYPp7jczE/sKqpwm4IAmPeM+UyTJF2C0JKJPuEOJuNgQvjC\n0S28f/gnzDzXLJ/1Klz5rVl2mXoOv8azz51W0rDUhrls/ufcUQAMu/tLJv3rewD+NL6z8fle+t+w\njqGUomN6oteEalFZFemJcVwxoQ8XHNbLLdDBUxAc4NGzRnLdMf157w/Gk2h7Ybnbe+f0p38BvCeD\nXYy87yvW55eys6jCne8GrWHzj2a5gfdFEITGJTqFe13J6uvfNvQ06H6Id5t9PTbeVPYpzjN5xz+9\nEfasqfOp+2b7TwBP6W5FtL5/BThs0a1f3g4vTfGsa+3OyZK3v5wPfsvj3QXb+GbVbnYXV5KVmkCP\n9ik88LvhZCbH07djqt+5lFL85fiBHNI7i9G9jP/9DTN/49o3F7n7DO6awRPnHcyATmm8eYXnrWXy\nYz+wu9imuVfZ0izE2fzdBUFocUTfhGokGOKTGuemDcYeH+uT7TA12xSh+O4ByH3R/N1jafllBbDp\nBxhqKyvn8hm0mYuSfZNsAVkptmfq3zuZY37yJ1j4smlb/h4MOwMeGwKVxXCbJ2bs5vc8ZpWBnb0L\nSc+6foI7fUEg/u/80Rz50Ld8t8Z73uHSI3KIjVGcOrKbX+54L83dNZk64a9BzyEIQsugbWjuvpzw\noPd6arZJTetLSjZopymm7OLdS0y90DdOh/9eCi/aMhrmvgj3tvMqXBEoF3tWwRLvhpLdHsEO8L/f\nG7NNyQ6oKoVZN9E+xT/N7prd3lGrSfGxpCQEf153C5IX3u6X3yHVXyN3a+4u4d5pcNBzCILQMmg7\nwv26RTD2KjjrFcj0zXsWhBTLd32Hx4TByg+NYN9hpfjdNt8IY4B5luu/rXBFqk96gfPG9iKuxCeh\n16MhQgR+ncFrvz/Mr/mYQZ0CdA5OII+eT68b79fnuYsO4S/HecaU4bLhV1oTsIn+k7iCILQs2o5w\n79APTnrE24wSiqrS8Potf88U5t63zqw/fwy8eop78+wbJ3LbSYPY9OBJJvtjVZAUwVMf886J3s+T\nZXF4lyQuHmd8zQd1MeaYET3qnyr3L8cdxK+3Hcuw7v7HOGFoF69yhO63D1edVEnRKwgtnrYj3OtD\nJ1tNkrFX+W932d87DTWFue1smuO2wffvlMZVR/XzaM4u4e4bsn/o5TDqfM/6Re97TEiL3+TeU4cy\n6/oJXDnBTBC7Uh/UhaX3HM9rvx/LdccOoFOIQKpFdx7H0xeM5tCcLPj2AXjzTLNBapUKQotHhHtt\n2P3dB5zgpUm786UMPhX2rA68f3Fe4PaqA9B5GBx9q6fNVXfU18Vw3DUm22XeQpRSDOmWwemju/P8\nxWM4f2wv2PBd4GyXH18Hqz71a85Iinfnig9FVmoCJw23EnzNedizoV3vsPYXBKH5aJveMnXhL2tM\nINOAydD7CNi7xiQg62KViU3v4l2vdeqjkL/KVB56dgLcvNE72KrGAeX7IcHHbXGSJegPOgHa94Ez\nX/Rsy+rj5YaplImKZduvJi9OZk/483JP/6/vgkWvmb/z3zXHbAhrv/Qsq1hJ+iUIUYBo7qFI72Lc\nEsEETnU7GHoeCvFW+oCOAz19b9sBh14Bx9xh1ssLjPdM/ipPnydGwZafPIVFLnwPRpwDiZY/fGI6\n3LDY2+c+Zzxsz4UDe73H9s195rNoG8x5xHjplBV4Fyl562zIfZkG8Y4t2delnzXsWIIgNAki3BtK\nhi2606WNJ7eHOJs9e/5znuUiq2JhVj/z2X9y6MIVfSYCGnbbtPPyQk+0KMC3f4eH+5g/Xz79k4mG\n3eefSRKnE54/Fl4/PfC5C7dAjeX7fuc+75z5giC0WES4N5Q+E0wys+k+OWju2A235kFcMix61X+/\nDv3CP4erVF2BLbNi3sLAfV1c/LF3ScH3r4D/G+0v4N843eTb2fAN7F1vio3Pn+EJyHK9dUx72pOj\nRxCEFo8I94aSkAqXfOKxwdtJTANHuQmEqjpgSgO66OyfsTEoGd3Nm4A96VlVmfkMVoe0z1GmZqwv\nX93hWa6pho2elL88eYgpNv75TZ43jL2WrX9Q+NWpBEFofkS4NzZHXG8+Hx1sSgO6yBkfuH8g4hKg\n6yjYOMfTVrLTfLpSAQy0Cd+/bfFMel4zz7u27JpZkG9593z4h+DnfHw4rPkc9qw1+9exsLggCM2L\n0rXkImlMxowZo3Nzc0N3jHYqiuEhnxKAF30QXOMOxj0BAodi4uGOfCPIlTK+6POegVu3eXu0uL7j\n7x80mTF9OfIGGHQKfHQNTPybqU3rIiXbpBu41N+tUhCEpkcptVBrPSZUP9HcG5vEdP+2pHZ1P06g\nzOL2GxAAAAVfSURBVJbOalPlyCXIj7kdbtvu76roEv5H32bK3fly7D3GA+iPC2D4md7byvZCdtNU\nUBQEIXKIcG9slDLuky46D6+bvd2FqzpUQykv8F4/8WH/OrGXfAIjbZGyZYGLZwuC0HIR94em4LLP\njXdLryNCFtwOSlpHk/zsg+mmGPgnN0DfSXU/zrkz4e3z4IL3oMchgW3pfY4yf11HwBe3wMSb6zdm\nQRCaDbG5RyNOp7Gfj74Y2vUM3V8QhFZDuDZ30dyjkZgYY18XBEEIQlg2AqXUFKXUGqXUeqXULQG2\nZyqlPlFKLVFKrVBKXRb5oQqCIAjhElK4K6VigaeAE4EhwHlKqSE+3a4FVmqtRwKTgEeVUlJkUxAE\noZkIR3MfC6zXWm/UWlcBbwM+RUjRQLoyCcvTgALAEdGRCoIgCGETjnDvDmyzrW+32uw8CQwGdgDL\ngBu01k7fAymlrlJK5Sqlcvfs2eO7WRAEQYgQkfJzPwFYDHQDRgFPKqX8Cm1qrWdorcdorcd07Bhe\nwQhBEASh7oQj3PMAu79dD6vNzmXA+9qwHtgEDIrMEAVBEIS6Eo5wXwAMUEr1sSZJzwU+9umzFTgW\nQCnVGRgIbIzkQAVBEITwCennrrV2KKX+CHwJxAIvaa1XKKWmW9ufBe4HXlFKLQMU8Det9d6gBxUE\nQRAalWaLUFVK7QG21HP3bEAeHh7kfngj98MbuR/eRPv96K21Djlp2WzCvSEopXLDCb9tK8j98Ebu\nhzdyP7xpK/dDskIKgiC0QkS4C4IgtEKiVbjPaO4BtDDkfngj98MbuR/etIn7EZU2d0EQBKF2olVz\nFwRBEGoh6oR7qPTDrRGl1Gal1DKl1GKlVK7VlqWU+loptc76bG/rf6t1f9YopU5ovpFHBqXUS0qp\nfKXUcltbna9fKXWIdR/XK6WesBLdRR1B7sc9Sqk86zeyWCl1km1ba78fPZVS3ymlVlopx2+w2tvs\nbwQArXXU/GGCqDYAfYEEYAkwpLnH1QTXvRnI9ml7GLjFWr4F+Ke1PMS6L4lAH+t+xTb3NTTw+o8C\nRgPLG3L9wK/A4ZhAu8+BE5v72iJ4P+4B/hqgb1u4H12B0dZyOrDWuu42+xvRWked5h5O+uG2wjTg\nVWv5VeA0W/vbWutKrfUmYD3mvkUtWus5mDTSdup0/UqprkCG1nqeNv/Fr9n2iSqC3I9gtIX7sVNr\nvchaLgFWYTLXttnfCESfWSac9MOtEQ3MVkotVEpdZbV11lrvtJZ3AZ2t5bZyj+p6/d2tZd/21sR1\nSqmlltnGZYJoU/dDKZUDHAzMp43/RqJNuLdVxmutR2GqYV2rlDrKvtHSMtqs21Nbv36LZzDmylHA\nTuDR5h1O06OUSgPeA/6ktS62b2uLv5FoE+7hpB9udWit86zPfOADjJllt/UaifWZb3VvK/eortef\nZy37trcKtNa7tdY12hTJeR6PKa5N3A+lVDxGsL+ptX7fam7Tv5FoE+7hpB9uVSilUpVS6a5l4Hhg\nOea6L7G6XQJ8ZC1/DJyrlEpUSvUBBmAmiVobdbp+6/W8WCl1uOUBcbFtn6jHJcQsfof5jUAbuB/W\n+F8EVmmtH7Ntatu/keae0a3rH3ASZjZ8A3B7c4+nCa63L2ZmfwmwwnXNQAfgG2AdMBvIsu1zu3V/\n1hDFs/2265mJMTVUY+ygl9fn+oExGKG3AVMaUjX3tUXwfryOKXG5FCO8urah+zEeY3JZiqkIt9iS\nE232N6K1lghVQRCE1ki0mWUEQRCEMBDhLgiC0AoR4S4IgtAKEeEuCILQChHhLgiC0AoR4S4IgtAK\nEeEuCILQChHhLgiC0Ar5fwQRB3mM8HJwAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x119948890>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(y_test)\n",
    "plt.plot(y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R-squared is: -0.963381\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import r2_score\n",
    "\n",
    "def adj_r2_score(r2, n, k):\n",
    "    return 1-((1-r2)*((n-1)/(n-k-1)))\n",
    "\n",
    "r2_test = r2_score(y_test, y_pred)\n",
    "print(\"R-squared is: %f\"%r2_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Build a Neural Network"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "from keras.models import Sequential\n",
    "from keras.layers import Dense\n",
    "from keras.optimizers import Adam\n",
    "import keras.backend as K"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1 hidden layer with 1 neuron"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/20\n",
      "7451/7451 [==============================] - 0s - loss: 0.0025     \n",
      "Epoch 2/20\n",
      "7451/7451 [==============================] - 0s - loss: 0.0024     \n",
      "Epoch 3/20\n",
      "7451/7451 [==============================] - 0s - loss: 0.0024     \n",
      "Epoch 4/20\n",
      "7451/7451 [==============================] - 0s - loss: 0.0024     \n",
      "Epoch 5/20\n",
      "7451/7451 [==============================] - 0s - loss: 0.0024     \n",
      "Epoch 6/20\n",
      "7451/7451 [==============================] - 0s - loss: 0.0024     \n",
      "Epoch 7/20\n",
      "7451/7451 [==============================] - 0s - loss: 0.0024     \n",
      "Epoch 8/20\n",
      "7451/7451 [==============================] - 0s - loss: 0.0024     \n",
      "Epoch 9/20\n",
      "7451/7451 [==============================] - 0s - loss: 0.0024     \n",
      "Epoch 10/20\n",
      "7451/7451 [==============================] - 0s - loss: 0.0024     \n",
      "Epoch 11/20\n",
      "7451/7451 [==============================] - 0s - loss: 0.0024     \n",
      "Epoch 12/20\n",
      "7451/7451 [==============================] - 0s - loss: 0.0024     \n",
      "Epoch 13/20\n",
      "7451/7451 [==============================] - 0s - loss: 0.0024     \n",
      "Epoch 14/20\n",
      "7451/7451 [==============================] - 0s - loss: 0.0024     \n",
      "Epoch 15/20\n",
      "7451/7451 [==============================] - 0s - loss: 0.0024     \n",
      "Epoch 16/20\n",
      "7451/7451 [==============================] - 0s - loss: 0.0024     \n",
      "Epoch 17/20\n",
      "7451/7451 [==============================] - 0s - loss: 0.0024     \n",
      "Epoch 18/20\n",
      "7451/7451 [==============================] - 0s - loss: 0.0024     \n",
      "Epoch 19/20\n",
      "7451/7451 [==============================] - 0s - loss: 0.0024     \n",
      "Epoch 20/20\n",
      "7451/7451 [==============================] - 0s - loss: 0.0024     \n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x124527290>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "K.clear_session()\n",
    "model = Sequential()\n",
    "model.add(Dense(1, input_shape=(X_test.shape[1],), activation='tanh', kernel_initializer='lecun_uniform'))\n",
    "model.compile(optimizer=Adam(lr=0.001), loss='mean_squared_error')\n",
    "model.fit(X_train, y_train, batch_size=16, epochs=20, verbose=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R-Squared: -13.593283\n"
     ]
    },
    {
     "data": {
      "image/png": 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uHc3B3aJ1VD2HG4BDOkQzqH0jp9Zoo/AQ0nML+Xn3aWKn/8h/t7vMqlBhlq6N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\ncBdCCD8kwV0IIfyQBHchhPBD/w+wp4rVV5I3lwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10e09d0d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "y_pred = model.predict(X_test)\n",
    "plt.plot(y_test)\n",
    "plt.plot(y_pred)\n",
    "print('R-Squared: %f'%(r2_score(y_test, y_pred)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 2 Hidden Layers with 50 neurons each and ReLU activation function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/20\n",
      "7451/7451 [==============================] - 1s - loss: 0.0118     \n",
      "Epoch 2/20\n",
      "7451/7451 [==============================] - 0s - loss: 1.4968e-05     \n",
      "Epoch 3/20\n",
      "7451/7451 [==============================] - 0s - loss: 9.7235e-06     \n",
      "Epoch 4/20\n",
      "7451/7451 [==============================] - 1s - loss: 9.0429e-06     \n",
      "Epoch 5/20\n",
      "7451/7451 [==============================] - 1s - loss: 1.0248e-05     \n",
      "Epoch 6/20\n",
      "7451/7451 [==============================] - 1s - loss: 1.0652e-05     \n",
      "Epoch 7/20\n",
      "7451/7451 [==============================] - 0s - loss: 1.0775e-05     \n",
      "Epoch 8/20\n",
      "7451/7451 [==============================] - 0s - loss: 1.1454e-05     \n",
      "Epoch 9/20\n",
      "7451/7451 [==============================] - 0s - loss: 1.1335e-05     \n",
      "Epoch 10/20\n",
      "7451/7451 [==============================] - 1s - loss: 1.1369e-05     \n",
      "Epoch 11/20\n",
      "7451/7451 [==============================] - 0s - loss: 1.0755e-05     \n",
      "Epoch 12/20\n",
      "7451/7451 [==============================] - 1s - loss: 1.1995e-05     \n",
      "Epoch 13/20\n",
      "7451/7451 [==============================] - 1s - loss: 1.4330e-05     \n",
      "Epoch 14/20\n",
      "7451/7451 [==============================] - 1s - loss: 1.4510e-05     \n",
      "Epoch 15/20\n",
      "7451/7451 [==============================] - 1s - loss: 1.6119e-05     \n",
      "Epoch 16/20\n",
      "7451/7451 [==============================] - 1s - loss: 1.6437e-05     \n",
      "Epoch 17/20\n",
      "7451/7451 [==============================] - 0s - loss: 2.2215e-05     \n",
      "Epoch 18/20\n",
      "7451/7451 [==============================] - 0s - loss: 1.5089e-05     \n",
      "Epoch 19/20\n",
      "7451/7451 [==============================] - 1s - loss: 1.7636e-05     \n",
      "Epoch 20/20\n",
      "7451/7451 [==============================] - 0s - loss: 1.7340e-05     \n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x124c3d4d0>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "K.clear_session()\n",
    "model = Sequential()\n",
    "model.add(Dense(50, input_shape=(X_test.shape[1],), activation='relu', kernel_initializer='lecun_uniform'))\n",
    "model.add(Dense(50, input_shape=(X_test.shape[1],), activation='relu'))\n",
    "model.add(Dense(1))\n",
    "model.compile(optimizer=Adam(lr=0.001), loss='mean_squared_error')\n",
    "model.fit(X_train, y_train, batch_size=16, epochs=20, verbose=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R-Squared: 0.998666\n"
     ]
    },
    {
     "data": {
      "image/png": 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aQtPCNEwi2rEWgHGHn576ewvRDR2m/BUi1Zru2os8owmX/VCzwAiw9pGz2Nu7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\n3rBmHkM2vQGAZ+EsilUz2Jw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t2NHEzM+quPnUvSO6Y2JZvGkXz87bwN0/2r/DukKI1FBK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\n/9IAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x124d61d90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "y_pred = model.predict(X_test)\n",
    "plt.plot(y_test)\n",
    "plt.plot(y_pred)\n",
    "print('R-Squared: %f'%(r2_score(y_test, y_pred)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "## Try to predict the difference between consecutive days\n",
    "### This is also known as introducing stationarity in the dataset.\n",
    "### A better test of the model is to predict stationarized data where the mean, standard deviation, autocorrelation are constant over time.\n",
    "### Because stock price data, currency exchange etc. are generated using a completely stochastic random walk process. Being able to predict future outcomes of a stochastic process is by definition not possible, and if someone claims to do this, one should be a bit skeptical [Vegard Flovik, PhD]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}