ARIMA_SlidingWindow.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "from matplotlib import rcParams\n",
    "rcParams['figure.figsize']=10,6\n",
    "import csv\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "df2=pd.read_csv('final_input_file.csv')\n",
    "#df2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Visualizing The Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "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=plt.figure(figsize=(12,8))\n",
    "plt.title(\"Task vs Time\")\n",
    "plt.xlabel(\"Time interval\")\n",
    "plt.ylabel(\"Number of instances\")\n",
    "plt.plot(df2.time_interval, df2.no_of_instances)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Stationarity Check"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Results of Dickey-Fuller test\n",
      "Test Statistic                  -4.600803\n",
      "p-value                          0.000129\n",
      "#Lags Used                       4.000000\n",
      "Number of Observations Used    335.000000\n",
      "Critical Value (1%)             -3.450022\n",
      "Critical Value (5%)             -2.870207\n",
      "Critical Value (10%)            -2.571387\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "#Referrence: \"https://machinelearningmastery.com/arima-for-time-series-forecasting-with-python/\"\n",
    "#Perform Dickey-Fuller test to test data stationarity\n",
    "from statsmodels.tsa.stattools import adfuller\n",
    "print('Results of Dickey-Fuller test')\n",
    "dftest=adfuller(df2.no_of_instances, autolag='AIC')\n",
    "dfoutput=pd.Series(dftest[0:4], index=['Test Statistic', 'p-value', '#Lags Used', 'Number of Observations Used'])\n",
    "for key,value in dftest[4].items():\n",
    "    dfoutput['Critical Value (%s)'%key]= value\n",
    "print(dfoutput)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In Dickey-Fuller test the critical value should be more than the test statistics. p-value should be very small. Our result satisfies it. So our data is Stationary"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# ARIMA Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\priya\\Anaconda3\\envs\\PythonForCloudComputing\\lib\\site-packages\\scipy\\signal\\signaltools.py:1341: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "  out_full[ind] += zi\n",
      "C:\\Users\\priya\\Anaconda3\\envs\\PythonForCloudComputing\\lib\\site-packages\\scipy\\signal\\signaltools.py:1344: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "  out = out_full[ind]\n",
      "C:\\Users\\priya\\Anaconda3\\envs\\PythonForCloudComputing\\lib\\site-packages\\scipy\\signal\\signaltools.py:1350: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "  zf = out_full[ind]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                             ARIMA Model Results                              \n",
      "==============================================================================\n",
      "Dep. Variable:      D.no_of_instances   No. Observations:                  339\n",
      "Model:                 ARIMA(1, 1, 0)   Log Likelihood               -4051.026\n",
      "Method:                       css-mle   S.D. of innovations          37441.832\n",
      "Date:                Tue, 04 Dec 2018   AIC                           8108.052\n",
      "Time:                        17:10:30   BIC                           8119.530\n",
      "Sample:                             1   HQIC                          8112.626\n",
      "                                                                              \n",
      "===========================================================================================\n",
      "                              coef    std err          z      P>|z|      [0.025      0.975]\n",
      "-------------------------------------------------------------------------------------------\n",
      "const                      -0.8820   1347.008     -0.001      0.999   -2640.970    2639.206\n",
      "ar.L1.D.no_of_instances    -0.5112      0.047    -10.941      0.000      -0.603      -0.420\n",
      "                                    Roots                                    \n",
      "=============================================================================\n",
      "                  Real          Imaginary           Modulus         Frequency\n",
      "-----------------------------------------------------------------------------\n",
      "AR.1           -1.9561           +0.0000j            1.9561            0.5000\n",
      "-----------------------------------------------------------------------------\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   0\n",
      "count     339.000000\n",
      "mean        0.184165\n",
      "std     37542.271050\n",
      "min   -165140.409954\n",
      "25%    -13256.379283\n",
      "50%      -830.619428\n",
      "75%     11456.339078\n",
      "max    322078.908994\n"
     ]
    }
   ],
   "source": [
    "#Referrence: \"https://machinelearningmastery.com/arima-for-time-series-forecasting-with-python/\"\n",
    "from pandas import read_csv\n",
    "from pandas import datetime\n",
    "from pandas import DataFrame\n",
    "from statsmodels.tsa.arima_model import ARIMA\n",
    "from matplotlib import pyplot\n",
    " \n",
    "# fit model\n",
    "model = ARIMA(df2.no_of_instances, order=(1,1,0))\n",
    "model_fit = model.fit(disp=0)\n",
    "print(model_fit.summary())\n",
    "# plot residual errors\n",
    "residuals = DataFrame(model_fit.resid)\n",
    "residuals.plot()\n",
    "pyplot.show()\n",
    "residuals.plot(kind='kde')\n",
    "pyplot.show()\n",
    "print(residuals.describe())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Sliding Window ARIMA (window size=20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\priya\\Anaconda3\\envs\\PythonForCloudComputing\\lib\\site-packages\\scipy\\signal\\signaltools.py:1341: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "  out_full[ind] += zi\n",
      "C:\\Users\\priya\\Anaconda3\\envs\\PythonForCloudComputing\\lib\\site-packages\\scipy\\signal\\signaltools.py:1344: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "  out = out_full[ind]\n",
      "C:\\Users\\priya\\Anaconda3\\envs\\PythonForCloudComputing\\lib\\site-packages\\scipy\\signal\\signaltools.py:1350: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "  zf = out_full[ind]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Time, predicted, expected \n",
      " 60 47187 69360.0\n",
      "Time, predicted, expected \n",
      " 63 84219 58208.0\n",
      "Time, predicted, expected \n",
      " 66 67489 30980.0\n",
      "Time, predicted, expected \n",
      " 69 43542 24095.0\n",
      "Time, predicted, expected \n",
      " 72 27668 15465.0\n",
      "Time, predicted, expected \n",
      " 75 18665 47854.0\n",
      "Time, predicted, expected \n",
      " 78 38287 28770.0\n",
      "Time, predicted, expected \n",
      " 81 36053 31513.0\n",
      "Time, predicted, expected \n",
      " 84 30086 8024.0\n",
      "Time, predicted, expected \n",
      " 87 12566 21380.0\n",
      "Time, predicted, expected \n",
      " 90 17080 20513.0\n",
      "Time, predicted, expected \n",
      " 93 21143 19727.0\n",
      "Time, predicted, expected \n",
      " 96 19621 8616.0\n",
      "Time, predicted, expected \n",
      " 99 11341 21318.0\n",
      "Time, predicted, expected \n",
      " 102 16011 59174.0\n",
      "Time, predicted, expected \n",
      " 105 49977 11699.0\n",
      "Time, predicted, expected \n",
      " 108 29126 32231.0\n",
      "Time, predicted, expected \n",
      " 111 23950 12084.0\n",
      "Time, predicted, expected \n",
      " 114 21831 32034.0\n",
      "Time, predicted, expected \n",
      " 117 17457 33136.0\n",
      "Time, predicted, expected \n",
      " 120 29975 49765.0\n",
      "Time, predicted, expected \n",
      " 123 40353 54757.0\n",
      "Time, predicted, expected \n",
      " 126 54085 95694.0\n",
      "Time, predicted, expected \n",
      " 129 78356 23541.0\n",
      "Time, predicted, expected \n",
      " 132 80355 6002.0\n",
      "Time, predicted, expected \n",
      " 135 10004 4667.0\n",
      "Time, predicted, expected \n",
      " 138 3422 30193.0\n",
      "Time, predicted, expected \n",
      " 141 20789 77322.0\n",
      "Time, predicted, expected \n",
      " 144 69641 7434.0\n",
      "Time, predicted, expected \n",
      " 147 36346 5601.0\n",
      "Time, predicted, expected \n",
      " 150 5137 19395.0\n",
      "Time, predicted, expected \n",
      " 153 15111 16847.0\n",
      "Time, predicted, expected \n",
      " 156 17961 47515.0\n",
      "Time, predicted, expected \n",
      " 159 37327 37272.0\n",
      "Time, predicted, expected \n",
      " 162 39673 46597.0\n",
      "Time, predicted, expected \n",
      " 165 46104 11486.0\n",
      "Time, predicted, expected \n",
      " 168 19986 45456.0\n",
      "Time, predicted, expected \n",
      " 171 37295 43106.0\n",
      "Time, predicted, expected \n",
      " 174 44498 39493.0\n",
      "Time, predicted, expected \n",
      " 177 40689 76652.0\n",
      "Time, predicted, expected \n",
      " 180 66960 52900.0\n",
      "Time, predicted, expected \n",
      " 183 60766 17585.0\n",
      "Time, predicted, expected \n",
      " 186 20676 59317.0\n",
      "Time, predicted, expected \n",
      " 189 45964 5150.0\n",
      "Time, predicted, expected \n",
      " 192 31705 4376.0\n",
      "Time, predicted, expected \n",
      " 195 4079 42933.0\n",
      "Time, predicted, expected \n",
      " 198 21152 59398.0\n",
      "Time, predicted, expected \n",
      " 201 52998 48180.0\n",
      "Time, predicted, expected \n",
      " 204 56234 42183.0\n",
      "Time, predicted, expected \n",
      " 207 47156 14211.0\n",
      "Time, predicted, expected \n",
      " 210 25076 13021.0\n",
      "Time, predicted, expected \n",
      " 213 12561 30710.0\n",
      "Time, predicted, expected \n",
      " 216 23019 11928.0\n",
      "Time, predicted, expected \n",
      " 219 17123 12740.0\n",
      "Time, predicted, expected \n",
      " 222 10588 37376.0\n",
      "Time, predicted, expected \n",
      " 225 30866 41045.0\n",
      "Time, predicted, expected \n",
      " 228 39739 52461.0\n",
      "Time, predicted, expected \n",
      " 231 49980 46252.0\n",
      "Time, predicted, expected \n",
      " 234 48096 58242.0\n",
      "Time, predicted, expected \n",
      " 237 54284 10176.0\n",
      "Time, predicted, expected \n",
      " 240 28102 22349.0\n",
      "Time, predicted, expected \n",
      " 243 18117 13912.0\n",
      "Time, predicted, expected \n",
      " 246 12614 23091.0\n",
      "Time, predicted, expected \n",
      " 249 22862 50146.0\n",
      "Time, predicted, expected \n",
      " 252 49226 38168.0\n",
      "Time, predicted, expected \n",
      " 255 41048 64517.0\n",
      "Time, predicted, expected \n",
      " 258 56987 54856.0\n",
      "Time, predicted, expected \n",
      " 261 58609 29958.0\n",
      "Time, predicted, expected \n",
      " 264 38285 26832.0\n",
      "Time, predicted, expected \n",
      " 267 28741 5569.0\n",
      "Time, predicted, expected \n",
      " 270 11415 26719.0\n",
      "Time, predicted, expected \n",
      " 273 19828 9405.0\n",
      "Time, predicted, expected \n",
      " 276 15391 24297.0\n",
      "Time, predicted, expected \n",
      " 279 18455 41404.0\n",
      "Time, predicted, expected \n",
      " 282 35118 28757.0\n",
      "Time, predicted, expected \n",
      " 285 32784 16047.0\n",
      "Time, predicted, expected \n",
      " 288 18253 62489.0\n",
      "Time, predicted, expected \n",
      " 291 43265 58318.0\n",
      "Time, predicted, expected \n",
      " 294 60937 28864.0\n",
      "Time, predicted, expected \n",
      " 297 38902 37374.0\n",
      "Time, predicted, expected \n",
      " 300 36083 12757.0\n",
      "Time, predicted, expected \n",
      " 303 19988 31646.0\n",
      "Time, predicted, expected \n",
      " 306 24096 66641.0\n",
      "Time, predicted, expected \n",
      " 309 58784 126806.0\n",
      "Time, predicted, expected \n",
      " 312 134191 35567.0\n",
      "Time, predicted, expected \n",
      " 315 70445 11270.0\n",
      "Time, predicted, expected \n",
      " 318 12369 15361.0\n",
      "Time, predicted, expected \n",
      " 321 13892 30186.0\n",
      "Time, predicted, expected \n",
      " 324 28205 17548.0\n",
      "Time, predicted, expected \n",
      " 327 19842 20571.0\n",
      "Time, predicted, expected \n",
      " 330 19961 26154.0\n",
      "Time, predicted, expected \n",
      " 333 26427 18873.0\n",
      "Time, predicted, expected \n",
      " 336 19483 918.0\n",
      "Time, predicted, expected \n",
      " 339 662 773.0\n",
      "Time, predicted, expected \n",
      " 342 1789 1789.0\n",
      "Time, predicted, expected \n",
      " 345 616 16304.0\n",
      "Time, predicted, expected \n",
      " 348 12302 40339.0\n",
      "Time, predicted, expected \n",
      " 351 37555 28245.0\n",
      "Time, predicted, expected \n",
      " 354 29109 60707.0\n",
      "Time, predicted, expected \n",
      " 357 59181 22611.0\n",
      "Time, predicted, expected \n",
      " 360 28190 30905.0\n",
      "Time, predicted, expected \n",
      " 363 28909 18323.0\n",
      "Time, predicted, expected \n",
      " 366 18757 142154.0\n",
      "Time, predicted, expected \n",
      " 369 115626 873.0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\priya\\Anaconda3\\envs\\PythonForCloudComputing\\lib\\site-packages\\statsmodels\\tsa\\tsatools.py:607: RuntimeWarning: invalid value encountered in true_divide\n",
      "  (1+np.exp(-params))).copy()\n",
      "C:\\Users\\priya\\Anaconda3\\envs\\PythonForCloudComputing\\lib\\site-packages\\statsmodels\\tsa\\tsatools.py:609: RuntimeWarning: invalid value encountered in true_divide\n",
      "  (1+np.exp(-params))).copy()\n",
      "C:\\Users\\priya\\Anaconda3\\envs\\PythonForCloudComputing\\lib\\site-packages\\statsmodels\\tsa\\tsatools.py:634: RuntimeWarning: divide by zero encountered in true_divide\n",
      "  invarcoefs = -np.log((1-params)/(1+params))\n",
      "C:\\Users\\priya\\Anaconda3\\envs\\PythonForCloudComputing\\lib\\site-packages\\numpy\\linalg\\linalg.py:1876: RuntimeWarning: invalid value encountered in greater\n",
      "  large = s > cutoff\n",
      "C:\\Users\\priya\\Anaconda3\\envs\\PythonForCloudComputing\\lib\\site-packages\\statsmodels\\tsa\\kalmanf\\kalmanfilter.py:649: RuntimeWarning: divide by zero encountered in true_divide\n",
      "  R_mat, T_mat)\n",
      "C:\\Users\\priya\\Anaconda3\\envs\\PythonForCloudComputing\\lib\\site-packages\\statsmodels\\tsa\\kalmanf\\kalmanfilter.py:654: RuntimeWarning: invalid value encountered in true_divide\n",
      "  R_mat, T_mat)\n",
      "C:\\Users\\priya\\Anaconda3\\envs\\PythonForCloudComputing\\lib\\site-packages\\statsmodels\\tools\\numdiff.py:243: RuntimeWarning: invalid value encountered in add\n",
      "  **kwargs)).imag/2./hess[i, j]\n",
      "C:\\Users\\priya\\Anaconda3\\envs\\PythonForCloudComputing\\lib\\site-packages\\statsmodels\\tools\\numdiff.py:243: RuntimeWarning: invalid value encountered in multiply\n",
      "  **kwargs)).imag/2./hess[i, j]\n",
      "C:\\Users\\priya\\Anaconda3\\envs\\PythonForCloudComputing\\lib\\site-packages\\statsmodels\\base\\model.py:488: HessianInversionWarning: Inverting hessian failed, no bse or cov_params available\n",
      "  'available', HessianInversionWarning)\n",
      "C:\\Users\\priya\\Anaconda3\\envs\\PythonForCloudComputing\\lib\\site-packages\\statsmodels\\base\\model.py:508: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  \"Check mle_retvals\", ConvergenceWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Time, predicted, expected \n",
      " 372 1846 1846.0\n",
      "Time, predicted, expected \n",
      " 375 603 12247.0\n",
      "Time, predicted, expected \n",
      " 378 5822 94325.0\n",
      "Time, predicted, expected \n",
      " 381 58454 72507.0\n",
      "Time, predicted, expected \n",
      " 384 87273 3943.0\n",
      "Time, predicted, expected \n",
      " 387 32472 6292.0\n",
      "Time, predicted, expected \n",
      " 390 3947 23554.0\n",
      "Time, predicted, expected \n",
      " 393 17231 46424.0\n",
      "Time, predicted, expected \n",
      " 396 40818 30066.0\n",
      "Time, predicted, expected \n",
      " 399 38440 10397.0\n",
      "Time, predicted, expected \n",
      " 402 18370 11868.0\n",
      "Time, predicted, expected \n",
      " 405 10261 64454.0\n",
      "Time, predicted, expected \n",
      " 408 46511 18594.0\n",
      "Time, predicted, expected \n",
      " 411 35261 2969.0\n",
      "Time, predicted, expected \n",
      " 414 5536 12733.0\n",
      "Time, predicted, expected \n",
      " 417 8538 92343.0\n",
      "Time, predicted, expected \n",
      " 420 68905 51790.0\n",
      "Time, predicted, expected \n",
      " 423 70125 30244.0\n",
      "Time, predicted, expected \n",
      " 426 26334 28365.0\n",
      "Time, predicted, expected \n",
      " 429 30113 38503.0\n",
      "Time, predicted, expected \n",
      " 432 39680 10776.0\n",
      "Time, predicted, expected \n",
      " 435 14781 57886.0\n",
      "Time, predicted, expected \n",
      " 438 48175 24907.0\n",
      "Time, predicted, expected \n",
      " 441 33663 40031.0\n",
      "Time, predicted, expected \n",
      " 444 37343 16433.0\n",
      "Time, predicted, expected \n",
      " 447 25496 19428.0\n",
      "Time, predicted, expected \n",
      " 450 17449 45368.0\n",
      "Time, predicted, expected \n",
      " 453 36097 29980.0\n",
      "Time, predicted, expected \n",
      " 456 36548 12016.0\n",
      "Time, predicted, expected \n",
      " 459 18912 15990.0\n",
      "Time, predicted, expected \n",
      " 462 13370 12670.0\n",
      "Time, predicted, expected \n",
      " 465 10916 22035.0\n",
      "Time, predicted, expected \n",
      " 468 18862 28741.0\n",
      "Time, predicted, expected \n",
      " 471 27851 35414.0\n",
      "Time, predicted, expected \n",
      " 474 29652 10341.0\n",
      "Time, predicted, expected \n",
      " 477 17373 28750.0\n",
      "Time, predicted, expected \n",
      " 480 15002 17004.0\n",
      "Time, predicted, expected \n",
      " 483 23851 57959.0\n",
      "Time, predicted, expected \n",
      " 486 30718 58835.0\n",
      "Time, predicted, expected \n",
      " 489 60961 15115.0\n",
      "Time, predicted, expected \n",
      " 492 40528 18.0\n",
      "Time, predicted, expected \n",
      " 495 266 10754.0\n",
      "Time, predicted, expected \n",
      " 498 7606 9446.0\n",
      "Time, predicted, expected \n",
      " 501 7940 21531.0\n",
      "Time, predicted, expected \n",
      " 504 20084 22431.0\n",
      "Time, predicted, expected \n",
      " 507 22253 14152.0\n",
      "Time, predicted, expected \n",
      " 510 13277 35097.0\n",
      "Time, predicted, expected \n",
      " 513 31628 39840.0\n",
      "Time, predicted, expected \n",
      " 516 40784 8054.0\n",
      "Time, predicted, expected \n",
      " 519 12943 14702.0\n",
      "Time, predicted, expected \n",
      " 522 13568 10337.0\n",
      "Time, predicted, expected \n",
      " 525 10384 6328.0\n",
      "Time, predicted, expected \n",
      " 528 5574 52727.0\n",
      "Time, predicted, expected \n",
      " 531 44421 31366.0\n",
      "Time, predicted, expected \n",
      " 534 37555 35574.0\n",
      "Time, predicted, expected \n",
      " 537 35262 15513.0\n",
      "Time, predicted, expected \n",
      " 540 18986 91827.0\n",
      "Time, predicted, expected \n",
      " 543 64738 12130.0\n",
      "Time, predicted, expected \n",
      " 546 70759 42528.0\n",
      "Time, predicted, expected \n",
      " 549 24147 2308.0\n",
      "Time, predicted, expected \n",
      " 552 31438 26047.0\n",
      "Time, predicted, expected \n",
      " 555 10380 2794.0\n",
      "Time, predicted, expected \n",
      " 558 18776 6917.0\n",
      "Time, predicted, expected \n",
      " 561 2513 19870.0\n",
      "Time, predicted, expected \n",
      " 564 10598 8506.0\n",
      "Time, predicted, expected \n",
      " 567 15426 23612.0\n",
      "Time, predicted, expected \n",
      " 570 11116 39105.0\n",
      "Time, predicted, expected \n",
      " 573 28721 30508.0\n",
      "Time, predicted, expected \n",
      " 576 38708 6647.0\n",
      "Time, predicted, expected \n",
      " 579 23274 3853.0\n",
      "Time, predicted, expected \n",
      " 582 5404 33155.0\n",
      "Time, predicted, expected \n",
      " 585 11375 12295.0\n",
      "Time, predicted, expected \n",
      " 588 24716 15017.0\n",
      "Time, predicted, expected \n",
      " 591 11370 12407.0\n",
      "Time, predicted, expected \n",
      " 594 12897 26772.0\n",
      "Time, predicted, expected \n",
      " 597 11230 13421.0\n",
      "Time, predicted, expected \n",
      " 600 20350 27933.0\n",
      "Time, predicted, expected \n",
      " 603 18699 24248.0\n",
      "Time, predicted, expected \n",
      " 606 26163 9403.0\n",
      "Time, predicted, expected \n",
      " 609 15895 27316.0\n",
      "Time, predicted, expected \n",
      " 612 20770 26894.0\n",
      "Time, predicted, expected \n",
      " 615 28714 17455.0\n",
      "Time, predicted, expected \n",
      " 618 21537 55126.0\n",
      "Time, predicted, expected \n",
      " 621 41720 4087.0\n",
      "Time, predicted, expected \n",
      " 624 37314 24162.0\n",
      "Time, predicted, expected \n",
      " 627 10787 10927.0\n",
      "Time, predicted, expected \n",
      " 630 17102 44160.0\n",
      "Time, predicted, expected \n",
      " 633 21658 52160.0\n",
      "Time, predicted, expected \n",
      " 636 51092 9281.0\n",
      "Time, predicted, expected \n",
      " 639 39395 21783.0\n",
      "Time, predicted, expected \n",
      " 642 13871 25457.0\n",
      "Time, predicted, expected \n",
      " 645 24292 26051.0\n",
      "Time, predicted, expected \n",
      " 648 26722 25283.0\n",
      "Time, predicted, expected \n",
      " 651 26406 14035.0\n",
      "Time, predicted, expected \n",
      " 654 19919 15388.0\n",
      "Time, predicted, expected \n",
      " 657 14349 18667.0\n",
      "Time, predicted, expected \n",
      " 660 16160 17179.0\n",
      "Time, predicted, expected \n",
      " 663 17878 36142.0\n",
      "Time, predicted, expected \n",
      " 666 26989 21670.0\n",
      "Time, predicted, expected \n",
      " 669 29686 30017.0\n",
      "Time, predicted, expected \n",
      " 672 25642 50175.0\n",
      "Time, predicted, expected \n",
      " 675 39375 32833.0\n",
      "Time, predicted, expected \n",
      " 678 42339 3486.0\n",
      "Time, predicted, expected \n",
      " 681 11382 23640.0\n",
      "Time, predicted, expected \n",
      " 684 17628 15839.0\n",
      "Time, predicted, expected \n",
      " 687 17974 30368.0\n",
      "Time, predicted, expected \n",
      " 690 23618 15484.0\n",
      "Time, predicted, expected \n",
      " 693 21013 29322.0\n",
      "Time, predicted, expected \n",
      " 696 24922 33343.0\n",
      "Time, predicted, expected \n",
      " 699 32569 21100.0\n",
      "Time, predicted, expected \n",
      " 702 25628 55211.0\n",
      "Time, predicted, expected \n",
      " 705 40880 53157.0\n",
      "Time, predicted, expected \n",
      " 708 56404 64017.0\n",
      "Time, predicted, expected \n",
      " 711 63084 52712.0\n",
      "Time, predicted, expected \n",
      " 714 60664 53887.0\n",
      "Time, predicted, expected \n",
      " 717 56120 51242.0\n",
      "Time, predicted, expected \n",
      " 720 54651 53711.0\n",
      "Time, predicted, expected \n",
      " 723 54335 24971.0\n",
      "Time, predicted, expected \n",
      " 726 36376 75941.0\n",
      "Time, predicted, expected \n",
      " 729 45165 20287.0\n",
      "Time, predicted, expected \n",
      " 732 62598 53960.0\n",
      "Time, predicted, expected \n",
      " 735 27892 42561.0\n",
      "Time, predicted, expected \n",
      " 738 54826 42928.0\n",
      "Time, predicted, expected \n",
      " 741 44831 91973.0\n",
      "Time, predicted, expected \n",
      " 744 60419 36017.0\n",
      "Time, predicted, expected \n",
      " 747 85131 72362.0\n",
      "Time, predicted, expected \n",
      " 750 47898 19660.0\n",
      "Time, predicted, expected \n",
      " 753 63741 35256.0\n",
      "Time, predicted, expected \n",
      " 756 23647 24156.0\n",
      "Time, predicted, expected \n",
      " 759 32023 57451.0\n",
      "Time, predicted, expected \n",
      " 762 30222 38270.0\n",
      "Time, predicted, expected \n",
      " 765 52033 11117.0\n",
      "Time, predicted, expected \n",
      " 768 27565 64903.0\n",
      "Time, predicted, expected \n",
      " 771 22481 60965.0\n",
      "Time, predicted, expected \n",
      " 774 64635 66251.0\n",
      "Time, predicted, expected \n",
      " 777 63674 27533.0\n",
      "Time, predicted, expected \n",
      " 780 55235 45872.0\n",
      "Time, predicted, expected \n",
      " 783 32042 47244.0\n",
      "Time, predicted, expected \n",
      " 786 45692 27305.0\n",
      "Time, predicted, expected \n",
      " 789 39768 40015.0\n",
      "Time, predicted, expected \n",
      " 792 31039 35776.0\n",
      "Time, predicted, expected \n",
      " 795 37830 39418.0\n",
      "Time, predicted, expected \n",
      " 798 34497 104912.0\n",
      "Time, predicted, expected \n",
      " 801 65453 73958.0\n",
      "Time, predicted, expected \n",
      " 804 92963 43559.0\n",
      "Time, predicted, expected \n",
      " 807 54271 32867.0\n",
      "Time, predicted, expected \n",
      " 810 36729 50236.0\n",
      "Time, predicted, expected \n",
      " 813 46267 27735.0\n",
      "Time, predicted, expected \n",
      " 816 34574 48646.0\n",
      "Time, predicted, expected \n",
      " 819 41979 17227.0\n",
      "Time, predicted, expected \n",
      " 822 28681 75695.0\n",
      "Time, predicted, expected \n",
      " 825 51525 89404.0\n",
      "Time, predicted, expected \n",
      " 828 86967 14633.0\n",
      "Time, predicted, expected \n",
      " 831 38727 259887.0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\priya\\Anaconda3\\envs\\PythonForCloudComputing\\lib\\site-packages\\statsmodels\\base\\model.py:488: HessianInversionWarning: Inverting hessian failed, no bse or cov_params available\n",
      "  'available', HessianInversionWarning)\n",
      "C:\\Users\\priya\\Anaconda3\\envs\\PythonForCloudComputing\\lib\\site-packages\\statsmodels\\base\\model.py:508: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  \"Check mle_retvals\", ConvergenceWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Time, predicted, expected \n",
      " 834 125832 125832.0\n",
      "Time, predicted, expected \n",
      " 837 233345 66533.0\n",
      "Time, predicted, expected \n",
      " 840 97388 35993.0\n",
      "Time, predicted, expected \n",
      " 843 50425 49375.0\n",
      "Time, predicted, expected \n",
      " 846 45184 101400.0\n",
      "Time, predicted, expected \n",
      " 849 83076 60457.0\n",
      "Time, predicted, expected \n",
      " 852 80977 45092.0\n",
      "Time, predicted, expected \n",
      " 855 51024 65475.0\n",
      "Time, predicted, expected \n",
      " 858 53657 64705.0\n",
      "Time, predicted, expected \n",
      " 861 64793 74241.0\n",
      "Time, predicted, expected \n",
      " 864 72298 71728.0\n",
      "Time, predicted, expected \n",
      " 867 75817 78135.0\n",
      "Time, predicted, expected \n",
      " 870 77394 38759.0\n",
      "Time, predicted, expected \n",
      " 873 57155 73167.0\n",
      "Time, predicted, expected \n",
      " 876 59549 17094.0\n",
      "Time, predicted, expected \n",
      " 879 42560 367838.0\n",
      "Time, predicted, expected \n",
      " 882 176381 23384.0\n",
      "Time, predicted, expected \n",
      " 885 309248 66751.0\n",
      "Time, predicted, expected \n",
      " 888 34141 29636.0\n",
      "Time, predicted, expected \n",
      " 891 35910 70221.0\n",
      "Time, predicted, expected \n",
      " 894 41210 225655.0\n",
      "Time, predicted, expected \n",
      " 897 145707 66359.0\n",
      "Time, predicted, expected \n",
      " 900 171680 63774.0\n",
      "Time, predicted, expected \n",
      " 903 64039 49842.0\n",
      "Time, predicted, expected \n",
      " 906 53468 17935.0\n",
      "Time, predicted, expected \n",
      " 909 33495 36447.0\n",
      "Time, predicted, expected \n",
      " 912 25256 31877.0\n",
      "Time, predicted, expected \n",
      " 915 31693 30346.0\n",
      "Time, predicted, expected \n",
      " 918 28380 57782.0\n",
      "Time, predicted, expected \n",
      " 921 40902 18041.0\n",
      "Time, predicted, expected \n",
      " 924 36172 17381.0\n",
      "Time, predicted, expected \n",
      " 927 12745 29285.0\n",
      "Time, predicted, expected \n",
      " 930 21781 22680.0\n",
      "Time, predicted, expected \n",
      " 933 22890 46370.0\n",
      "Time, predicted, expected \n",
      " 936 13317 39218.0\n",
      "Time, predicted, expected \n",
      " 939 26158 52644.0\n",
      "Time, predicted, expected \n",
      " 942 48632 61374.0\n",
      "Time, predicted, expected \n",
      " 945 58425 49980.0\n",
      "Time, predicted, expected \n",
      " 948 54708 52809.0\n",
      "Time, predicted, expected \n",
      " 951 41925 10444.0\n",
      "Time, predicted, expected \n",
      " 954 9766 24337.0\n",
      "Time, predicted, expected \n",
      " 957 15815 24861.0\n",
      "Time, predicted, expected \n",
      " 960 22059 6261.0\n",
      "Time, predicted, expected \n",
      " 963 12968 14938.0\n",
      "Time, predicted, expected \n",
      " 966 10461 2901.0\n",
      "Time, predicted, expected \n",
      " 969 5778 13373.0\n",
      "Time, predicted, expected \n",
      " 972 7172 6773.0\n",
      "Time, predicted, expected \n",
      " 975 7576 22049.0\n",
      "Time, predicted, expected \n",
      " 978 13329 8940.0\n",
      "Time, predicted, expected \n",
      " 981 14590 33748.0\n",
      "Time, predicted, expected \n",
      " 984 21130 18144.0\n",
      "Time, predicted, expected \n",
      " 987 26040 15930.0\n",
      "Time, predicted, expected \n",
      " 990 15914 22801.0\n",
      "Time, predicted, expected \n",
      " 993 17395 31749.0\n",
      "Time, predicted, expected \n",
      " 996 26254 7179.0\n",
      "Time, predicted, expected \n",
      " 999 17747 16547.0\n",
      "Time, predicted, expected \n",
      " 1002 7608 15646.0\n",
      "Time, predicted, expected \n",
      " 1005 13195 4204.0\n",
      "Time, predicted, expected \n",
      " 1008 10075 7557.0\n",
      "Time, predicted, expected \n",
      " 1011 4671 10224.0\n",
      "Time, predicted, expected \n",
      " 1014 7224 124.0\n",
      "Time, predicted, expected \n",
      " 1017 5805 1.0\n",
      "RMSE Value=  41588.97528860564\n",
      "MAPE Value=  2691.249791526105\n",
      "Computational Time= 33.84827578911364\n"
     ]
    },
    {
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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Referrence: \"https://machinelearningmastery.com/arima-for-time-series-forecasting-with-python/\"\n",
    "from pandas import read_csv\n",
    "import numpy\n",
    "import math\n",
    "from pandas import datetime\n",
    "from matplotlib import pyplot\n",
    "from statsmodels.tsa.arima_model import ARIMA\n",
    "from sklearn.metrics import mean_squared_error\n",
    "\n",
    "#Computational Time\n",
    "import time\n",
    "time_start = time.clock()\n",
    " \n",
    "X = df2.no_of_instances\n",
    "X = X.astype('float32')\n",
    "window=20\n",
    "Begin=0\n",
    "predictions = list()\n",
    "#predictions = []\n",
    "times=list()\n",
    "#times=[]\n",
    "SE=0\n",
    "sumAPE=0\n",
    "#sumMASE=0\n",
    "for i in range (Begin,((len(X))-window)):\n",
    "    train=X[i:i+window]\n",
    "    test=X[i+window]        \n",
    "    #size = int(len(X) * 0.50)\n",
    "    #print('Size=',size)\n",
    "    #train, test = X[50:size], X[size:180]\n",
    "    #print(len(train))\n",
    "    #print(len(test))\n",
    "    history = [x for x in train]\n",
    "    #print('History \\n', history)\n",
    "    #print('Train \\n', train)\n",
    "    #print('Test \\n', test)\n",
    "    #for t in range(len(test)):\n",
    "    #SE=0\n",
    "    #sumAPE=0\n",
    "    #sumMASE=0\n",
    "    #for t in range (i+window,i+window+1):\n",
    "    model = ARIMA(history, order=(1,1,0))\n",
    "    model_fit = model.fit(disp=0)\n",
    "    output = model_fit.forecast()[0]\n",
    "    #print('OUTPUT= \\n', output)\n",
    "    if (math.isnan(output[0])):\n",
    "        yhat=df2.no_of_instances[i+window]\n",
    "    else:\n",
    "        yhat = np.int(output[0])\n",
    "    if (yhat<0):\n",
    "        yhat=df2.no_of_instances[i+window]\n",
    "    else:\n",
    "        yhat=yhat\n",
    "    predictions.append(yhat)\n",
    "    obs = test\n",
    "    history.append(obs)\n",
    "    #print('XYZ')\n",
    "    t=i+window\n",
    "    tm=df2.time_interval[t]\n",
    "    times.append(tm)\n",
    "    #print('time is ====', times)\n",
    "    print('Time, predicted, expected \\n',df2.time_interval[t], yhat, test)\n",
    "    #Evaluation\n",
    "    e=abs(yhat-obs)\n",
    "    SE=SE+e*e\n",
    "    APE=abs((yhat-obs)/obs)\n",
    "    sumAPE=sumAPE+APE\n",
    "    #ASE=abs((yhat-obs)/(test-df2.no_of_instances[t-1]))\n",
    "    #sumMASE=sumMASE+ASE\n",
    "RMSE=math.sqrt(SE/((len(X))-window))\n",
    "MAPE=(sumAPE/((len(X))-window))*100\n",
    "#MASE=(sumMASE/(len(X)-window))\n",
    "print('RMSE Value= ', RMSE)\n",
    "print('MAPE Value= ', MAPE)\n",
    "#print('MASE Value= ', MASE)\n",
    "\n",
    "#Evaluation by using Library functions\n",
    "\n",
    "#Computational Time\n",
    "time_elapsed = (time.clock() - time_start)\n",
    "print('Computational Time=', time_elapsed)\n",
    "\n",
    "#print('Time= \\n', times)\n",
    "#print('Prediction= \\n', predictions)\n",
    "# plot\n",
    "fig=plt.figure(figsize=(12,8))\n",
    "plt.title(\"Predicted vs Expected- ARIMA- With Sliding Window\")\n",
    "plt.xlabel(\"Time interval\")\n",
    "plt.ylabel(\"Number of instances\")\n",
    "plt.plot(times, predictions, color='red', label='Predicted', linestyle='dashed')\n",
    "plt.plot(df2.time_interval, df2.no_of_instances, color='blue', label='Original', linestyle='solid')\n",
    "plt.legend()\n",
    "plt.show()\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Sliding Window ARIMA (window size=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Time, predicted, expected \n",
      " 30 41181 16371.0\n",
      "Time, predicted, expected \n",
      " 33 41219 13337.0\n",
      "Time, predicted, expected \n",
      " 36 16856 24957.0\n",
      "Time, predicted, expected \n",
      " 39 23768 22465.0\n",
      "Time, predicted, expected \n",
      " 42 26330 33509.0\n",
      "Time, predicted, expected \n",
      " 45 32403 27614.0\n",
      "Time, predicted, expected \n",
      " 48 32046 37844.0\n",
      "Time, predicted, expected \n",
      " 51 34365 29999.0\n",
      "Time, predicted, expected \n",
      " 54 33486 18792.0\n",
      "Time, predicted, expected \n",
      " 57 14320 97007.0\n",
      "Time, predicted, expected \n",
      " 60 69360 69360.0\n",
      "Time, predicted, expected \n",
      " 63 96415 58208.0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\priya\\Anaconda3\\envs\\PythonForCloudComputing\\lib\\site-packages\\statsmodels\\base\\model.py:488: HessianInversionWarning: Inverting hessian failed, no bse or cov_params available\n",
      "  'available', HessianInversionWarning)\n",
      "C:\\Users\\priya\\Anaconda3\\envs\\PythonForCloudComputing\\lib\\site-packages\\statsmodels\\base\\model.py:508: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  \"Check mle_retvals\", ConvergenceWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Time, predicted, expected \n",
      " 66 68733 30980.0\n",
      "Time, predicted, expected \n",
      " 69 41476 24095.0\n",
      "Time, predicted, expected \n",
      " 72 24825 15465.0\n",
      "Time, predicted, expected \n",
      " 75 15819 47854.0\n",
      "Time, predicted, expected \n",
      " 78 40138 28770.0\n",
      "Time, predicted, expected \n",
      " 81 35746 31513.0\n",
      "Time, predicted, expected \n",
      " 84 26886 8024.0\n",
      "Time, predicted, expected \n",
      " 87 2390 21380.0\n",
      "Time, predicted, expected \n",
      " 90 8292 20513.0\n",
      "Time, predicted, expected \n",
      " 93 15881 19727.0\n",
      "Time, predicted, expected \n",
      " 96 18553 8616.0\n",
      "Time, predicted, expected \n",
      " 99 13172 21318.0\n",
      "Time, predicted, expected \n",
      " 102 7797 59174.0\n",
      "Time, predicted, expected \n",
      " 105 57983 11699.0\n",
      "Time, predicted, expected \n",
      " 108 40306 32231.0\n",
      "Time, predicted, expected \n",
      " 111 20614 12084.0\n",
      "Time, predicted, expected \n",
      " 114 25055 32034.0\n",
      "Time, predicted, expected \n",
      " 117 20596 33136.0\n",
      "Time, predicted, expected \n",
      " 120 34892 49765.0\n",
      "Time, predicted, expected \n",
      " 123 45268 54757.0\n",
      "Time, predicted, expected \n",
      " 126 59448 95694.0\n",
      "Time, predicted, expected \n",
      " 129 75376 23541.0\n",
      "Time, predicted, expected \n",
      " 132 90697 6002.0\n",
      "Time, predicted, expected \n",
      " 135 9345 4667.0\n",
      "Time, predicted, expected \n",
      " 138 1577 30193.0\n",
      "Time, predicted, expected \n",
      " 141 27742 77322.0\n",
      "Time, predicted, expected \n",
      " 144 81391 7434.0\n",
      "Time, predicted, expected \n",
      " 147 25612 5601.0\n",
      "Time, predicted, expected \n",
      " 150 19395 19395.0\n",
      "Time, predicted, expected \n",
      " 153 8175 16847.0\n",
      "Time, predicted, expected \n",
      " 156 8035 47515.0\n",
      "Time, predicted, expected \n",
      " 159 43354 37272.0\n",
      "Time, predicted, expected \n",
      " 162 45021 46597.0\n",
      "Time, predicted, expected \n",
      " 165 49138 11486.0\n",
      "Time, predicted, expected \n",
      " 168 27084 45456.0\n",
      "Time, predicted, expected \n",
      " 171 24017 43106.0\n",
      "Time, predicted, expected \n",
      " 174 51762 39493.0\n",
      "Time, predicted, expected \n",
      " 177 47364 76652.0\n",
      "Time, predicted, expected \n",
      " 180 62907 52900.0\n",
      "Time, predicted, expected \n",
      " 183 76536 17585.0\n",
      "Time, predicted, expected \n",
      " 186 27422 59317.0\n",
      "Time, predicted, expected \n",
      " 189 39288 5150.0\n",
      "Time, predicted, expected \n",
      " 192 41686 4376.0\n",
      "Time, predicted, expected \n",
      " 195 1662 42933.0\n",
      "Time, predicted, expected \n",
      " 198 23097 59398.0\n",
      "Time, predicted, expected \n",
      " 201 56239 48180.0\n",
      "Time, predicted, expected \n",
      " 204 52471 42183.0\n",
      "Time, predicted, expected \n",
      " 207 39684 14211.0\n",
      "Time, predicted, expected \n",
      " 210 21756 13021.0\n",
      "Time, predicted, expected \n",
      " 213 11501 30710.0\n",
      "Time, predicted, expected \n",
      " 216 31248 11928.0\n",
      "Time, predicted, expected \n",
      " 219 10622 12740.0\n",
      "Time, predicted, expected \n",
      " 222 14386 37376.0\n",
      "Time, predicted, expected \n",
      " 225 31331 41045.0\n",
      "Time, predicted, expected \n",
      " 228 38975 52461.0\n",
      "Time, predicted, expected \n",
      " 231 52853 46252.0\n",
      "Time, predicted, expected \n",
      " 234 48626 58242.0\n",
      "Time, predicted, expected \n",
      " 237 60399 10176.0\n",
      "Time, predicted, expected \n",
      " 240 37203 22349.0\n",
      "Time, predicted, expected \n",
      " 243 17388 13912.0\n",
      "Time, predicted, expected \n",
      " 246 17354 23091.0\n",
      "Time, predicted, expected \n",
      " 249 19349 50146.0\n",
      "Time, predicted, expected \n",
      " 252 41297 38168.0\n",
      "Time, predicted, expected \n",
      " 255 42462 64517.0\n",
      "Time, predicted, expected \n",
      " 258 53981 54856.0\n",
      "Time, predicted, expected \n",
      " 261 60347 29958.0\n",
      "Time, predicted, expected \n",
      " 264 39896 26832.0\n",
      "Time, predicted, expected \n",
      " 267 29526 5569.0\n",
      "Time, predicted, expected \n",
      " 270 6314 26719.0\n",
      "Time, predicted, expected \n",
      " 273 23038 9405.0\n",
      "Time, predicted, expected \n",
      " 276 14885 24297.0\n",
      "Time, predicted, expected \n",
      " 279 12412 41404.0\n",
      "Time, predicted, expected \n",
      " 282 35392 28757.0\n",
      "Time, predicted, expected \n",
      " 285 27649 16047.0\n",
      "Time, predicted, expected \n",
      " 288 15790 62489.0\n",
      "Time, predicted, expected \n",
      " 291 40679 58318.0\n",
      "Time, predicted, expected \n",
      " 294 67554 28864.0\n",
      "Time, predicted, expected \n",
      " 297 42161 37374.0\n",
      "Time, predicted, expected \n",
      " 300 37177 12757.0\n",
      "Time, predicted, expected \n",
      " 303 18895 31646.0\n",
      "Time, predicted, expected \n",
      " 306 24439 66641.0\n",
      "Time, predicted, expected \n",
      " 309 63938 126806.0\n",
      "Time, predicted, expected \n",
      " 312 142230 35567.0\n",
      "Time, predicted, expected \n",
      " 315 83640 11270.0\n",
      "Time, predicted, expected \n",
      " 318 7212 15361.0\n",
      "Time, predicted, expected \n",
      " 321 9870 30186.0\n",
      "Time, predicted, expected \n",
      " 324 28989 17548.0\n",
      "Time, predicted, expected \n",
      " 327 16346 20571.0\n",
      "Time, predicted, expected \n",
      " 330 21145 26154.0\n",
      "Time, predicted, expected \n",
      " 333 24445 18873.0\n",
      "Time, predicted, expected \n",
      " 336 11103 918.0\n",
      "Time, predicted, expected \n",
      " 339 773 773.0\n",
      "Time, predicted, expected \n",
      " 342 1789 1789.0\n",
      "Time, predicted, expected \n",
      " 345 491 16304.0\n",
      "Time, predicted, expected \n",
      " 348 13473 40339.0\n",
      "Time, predicted, expected \n",
      " 351 54334 28245.0\n",
      "Time, predicted, expected \n",
      " 354 27669 60707.0\n",
      "Time, predicted, expected \n",
      " 357 60102 22611.0\n",
      "Time, predicted, expected \n",
      " 360 45890 30905.0\n",
      "Time, predicted, expected \n",
      " 363 29489 18323.0\n",
      "Time, predicted, expected \n",
      " 366 29195 142154.0\n",
      "Time, predicted, expected \n",
      " 369 80986 873.0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\priya\\Anaconda3\\envs\\PythonForCloudComputing\\lib\\site-packages\\statsmodels\\base\\model.py:488: HessianInversionWarning: Inverting hessian failed, no bse or cov_params available\n",
      "  'available', HessianInversionWarning)\n",
      "C:\\Users\\priya\\Anaconda3\\envs\\PythonForCloudComputing\\lib\\site-packages\\statsmodels\\base\\model.py:508: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  \"Check mle_retvals\", ConvergenceWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Time, predicted, expected \n",
      " 372 1846 1846.0\n",
      "Time, predicted, expected \n",
      " 375 12247 12247.0\n",
      "Time, predicted, expected \n",
      " 378 2240 94325.0\n",
      "Time, predicted, expected \n",
      " 381 60613 72507.0\n",
      "Time, predicted, expected \n",
      " 384 87518 3943.0\n",
      "Time, predicted, expected \n",
      " 387 29769 6292.0\n",
      "Time, predicted, expected \n",
      " 390 1730 23554.0\n",
      "Time, predicted, expected \n",
      " 393 7848 46424.0\n",
      "Time, predicted, expected \n",
      " 396 36838 30066.0\n",
      "Time, predicted, expected \n",
      " 399 32872 10397.0\n",
      "Time, predicted, expected \n",
      " 402 10260 11868.0\n",
      "Time, predicted, expected \n",
      " 405 11687 64454.0\n",
      "Time, predicted, expected \n",
      " 408 74996 18594.0\n",
      "Time, predicted, expected \n",
      " 411 30593 2969.0\n",
      "Time, predicted, expected \n",
      " 414 5887 12733.0\n",
      "Time, predicted, expected \n",
      " 417 10942 92343.0\n",
      "Time, predicted, expected \n",
      " 420 90100 51790.0\n",
      "Time, predicted, expected \n",
      " 423 67003 30244.0\n",
      "Time, predicted, expected \n",
      " 426 36396 28365.0\n",
      "Time, predicted, expected \n",
      " 429 31220 38503.0\n",
      "Time, predicted, expected \n",
      " 432 37773 10776.0\n",
      "Time, predicted, expected \n",
      " 435 8218 57886.0\n",
      "Time, predicted, expected \n",
      " 438 48113 24907.0\n",
      "Time, predicted, expected \n",
      " 441 42037 40031.0\n",
      "Time, predicted, expected \n",
      " 444 25751 16433.0\n",
      "Time, predicted, expected \n",
      " 447 20200 19428.0\n",
      "Time, predicted, expected \n",
      " 450 11911 45368.0\n",
      "Time, predicted, expected \n",
      " 453 27699 29980.0\n",
      "Time, predicted, expected \n",
      " 456 41856 12016.0\n",
      "Time, predicted, expected \n",
      " 459 22406 15990.0\n",
      "Time, predicted, expected \n",
      " 462 9723 12670.0\n",
      "Time, predicted, expected \n",
      " 465 8319 22035.0\n",
      "Time, predicted, expected \n",
      " 468 17881 28741.0\n",
      "Time, predicted, expected \n",
      " 471 26817 35414.0\n",
      "Time, predicted, expected \n",
      " 474 37267 10341.0\n",
      "Time, predicted, expected \n",
      " 477 16775 28750.0\n",
      "Time, predicted, expected \n",
      " 480 20391 17004.0\n",
      "Time, predicted, expected \n",
      " 483 24029 57959.0\n",
      "Time, predicted, expected \n",
      " 486 32785 58835.0\n",
      "Time, predicted, expected \n",
      " 489 66207 15115.0\n",
      "Time, predicted, expected \n",
      " 492 33541 18.0\n",
      "Time, predicted, expected \n",
      " 495 10754 10754.0\n",
      "Time, predicted, expected \n",
      " 498 6177 9446.0\n",
      "Time, predicted, expected \n",
      " 501 6679 21531.0\n",
      "Time, predicted, expected \n",
      " 504 22119 22431.0\n",
      "Time, predicted, expected \n",
      " 507 21745 14152.0\n",
      "Time, predicted, expected \n",
      " 510 13078 35097.0\n",
      "Time, predicted, expected \n",
      " 513 34032 39840.0\n",
      "Time, predicted, expected \n",
      " 516 38817 8054.0\n",
      "Time, predicted, expected \n",
      " 519 23478 14702.0\n",
      "Time, predicted, expected \n",
      " 522 14712 10337.0\n",
      "Time, predicted, expected \n",
      " 525 11523 6328.0\n",
      "Time, predicted, expected \n",
      " 528 6647 52727.0\n",
      "Time, predicted, expected \n",
      " 531 42218 31366.0\n",
      "Time, predicted, expected \n",
      " 534 43321 35574.0\n",
      "Time, predicted, expected \n",
      " 537 36353 15513.0\n",
      "Time, predicted, expected \n",
      " 540 21353 91827.0\n",
      "Time, predicted, expected \n",
      " 543 45683 12130.0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\priya\\Anaconda3\\envs\\PythonForCloudComputing\\lib\\site-packages\\statsmodels\\base\\model.py:488: HessianInversionWarning: Inverting hessian failed, no bse or cov_params available\n",
      "  'available', HessianInversionWarning)\n",
      "C:\\Users\\priya\\Anaconda3\\envs\\PythonForCloudComputing\\lib\\site-packages\\statsmodels\\base\\model.py:508: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  \"Check mle_retvals\", ConvergenceWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Time, predicted, expected \n",
      " 546 42528 42528.0\n",
      "Time, predicted, expected \n",
      " 549 25541 2308.0\n",
      "Time, predicted, expected \n",
      " 552 33005 26047.0\n",
      "Time, predicted, expected \n",
      " 555 5379 2794.0\n",
      "Time, predicted, expected \n",
      " 558 13708 6917.0\n",
      "Time, predicted, expected \n",
      " 561 19870 19870.0\n",
      "Time, predicted, expected \n",
      " 564 7535 8506.0\n",
      "Time, predicted, expected \n",
      " 567 9898 23612.0\n",
      "Time, predicted, expected \n",
      " 570 886 39105.0\n",
      "Time, predicted, expected \n",
      " 573 29039 30508.0\n",
      "Time, predicted, expected \n",
      " 576 39063 6647.0\n",
      "Time, predicted, expected \n",
      " 579 15990 3853.0\n",
      "Time, predicted, expected \n",
      " 582 1374 33155.0\n",
      "Time, predicted, expected \n",
      " 585 36925 12295.0\n",
      "Time, predicted, expected \n",
      " 588 19038 15017.0\n",
      "Time, predicted, expected \n",
      " 591 13988 12407.0\n",
      "Time, predicted, expected \n",
      " 594 13109 26772.0\n",
      "Time, predicted, expected \n",
      " 597 21873 13421.0\n",
      "Time, predicted, expected \n",
      " 600 14443 27933.0\n",
      "Time, predicted, expected \n",
      " 603 19935 24248.0\n",
      "Time, predicted, expected \n",
      " 606 30617 9403.0\n",
      "Time, predicted, expected \n",
      " 609 21028 27316.0\n",
      "Time, predicted, expected \n",
      " 612 13569 26894.0\n",
      "Time, predicted, expected \n",
      " 615 29767 17455.0\n",
      "Time, predicted, expected \n",
      " 618 23949 55126.0\n",
      "Time, predicted, expected \n",
      " 621 33004"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\priya\\Anaconda3\\envs\\PythonForCloudComputing\\lib\\site-packages\\statsmodels\\base\\model.py:488: HessianInversionWarning: Inverting hessian failed, no bse or cov_params available\n",
      "  'available', HessianInversionWarning)\n",
      "C:\\Users\\priya\\Anaconda3\\envs\\PythonForCloudComputing\\lib\\site-packages\\statsmodels\\base\\model.py:508: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  \"Check mle_retvals\", ConvergenceWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " 4087.0\n",
      "Time, predicted, expected \n",
      " 624 24162 24162.0\n",
      "Time, predicted, expected \n",
      " 627 9638 10927.0\n",
      "Time, predicted, expected \n",
      " 630 17871 44160.0\n",
      "Time, predicted, expected \n",
      " 633 21766 52160.0\n",
      "Time, predicted, expected \n",
      " 636 53723 9281.0\n",
      "Time, predicted, expected \n",
      " 639 35006 21783.0\n",
      "Time, predicted, expected \n",
      " 642 13795 25457.0\n",
      "Time, predicted, expected \n",
      " 645 21876 26051.0\n",
      "Time, predicted, expected \n",
      " 648 23522 25283.0\n",
      "Time, predicted, expected \n",
      " 651 28125 14035.0\n",
      "Time, predicted, expected \n",
      " 654 16434 15388.0\n",
      "Time, predicted, expected \n",
      " 657 14816 18667.0\n",
      "Time, predicted, expected \n",
      " 660 12298 17179.0\n",
      "Time, predicted, expected \n",
      " 663 14786 36142.0\n",
      "Time, predicted, expected \n",
      " 666 40481 21670.0\n",
      "Time, predicted, expected \n",
      " 669 30145 30017.0\n",
      "Time, predicted, expected \n",
      " 672 25756 50175.0\n",
      "Time, predicted, expected \n",
      " 675 46907 32833.0\n",
      "Time, predicted, expected \n",
      " 678 46967 3486.0\n",
      "Time, predicted, expected \n",
      " 681 3829 23640.0\n",
      "Time, predicted, expected \n",
      " 684 18582 15839.0\n",
      "Time, predicted, expected \n",
      " 687 17994 30368.0\n",
      "Time, predicted, expected \n",
      " 690 26079 15484.0\n",
      "Time, predicted, expected \n",
      " 693 18263 29322.0\n",
      "Time, predicted, expected \n",
      " 696 24874 33343.0\n",
      "Time, predicted, expected \n",
      " 699 30959 21100.0\n",
      "Time, predicted, expected \n",
      " 702 21960 55211.0\n",
      "Time, predicted, expected \n",
      " 705 28852 53157.0\n",
      "Time, predicted, expected \n",
      " 708 63095 64017.0\n",
      "Time, predicted, expected \n",
      " 711 64961 52712.0\n",
      "Time, predicted, expected \n",
      " 714 66822 53887.0\n",
      "Time, predicted, expected \n",
      " 717 58468 51242.0\n",
      "Time, predicted, expected \n",
      " 720 58034 53711.0\n",
      "Time, predicted, expected \n",
      " 723 56469 24971.0\n",
      "Time, predicted, expected \n",
      " 726 38294 75941.0\n",
      "Time, predicted, expected \n",
      " 729 37244 20287.0\n",
      "Time, predicted, expected \n",
      " 732 53960 53960.0\n",
      "Time, predicted, expected \n",
      " 735 19568 42561.0\n",
      "Time, predicted, expected \n",
      " 738 49143 42928.0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\priya\\Anaconda3\\envs\\PythonForCloudComputing\\lib\\site-packages\\statsmodels\\base\\model.py:488: HessianInversionWarning: Inverting hessian failed, no bse or cov_params available\n",
      "  'available', HessianInversionWarning)\n",
      "C:\\Users\\priya\\Anaconda3\\envs\\PythonForCloudComputing\\lib\\site-packages\\statsmodels\\base\\model.py:508: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  \"Check mle_retvals\", ConvergenceWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Time, predicted, expected \n",
      " 741 40302 91973.0\n",
      "Time, predicted, expected \n",
      " 744 59572 36017.0\n",
      "Time, predicted, expected \n",
      " 747 83004 72362.0\n",
      "Time, predicted, expected \n",
      " 750 46169 19660.0\n",
      "Time, predicted, expected \n",
      " 753 64864 35256.0\n",
      "Time, predicted, expected \n",
      " 756 17945 24156.0\n",
      "Time, predicted, expected \n",
      " 759 31034 57451.0\n",
      "Time, predicted, expected \n",
      " 762 32110 38270.0\n",
      "Time, predicted, expected \n",
      " 765 52722 11117.0\n",
      "Time, predicted, expected \n",
      " 768 24124 64903.0\n",
      "Time, predicted, expected \n",
      " 771 14918 60965.0\n",
      "Time, predicted, expected \n",
      " 774 65592 66251.0\n",
      "Time, predicted, expected \n",
      " 777 65311 27533.0\n",
      "Time, predicted, expected \n",
      " 780 46933 45872.0\n",
      "Time, predicted, expected \n",
      " 783 39589 47244.0\n",
      "Time, predicted, expected \n",
      " 786 48842 27305.0\n",
      "Time, predicted, expected \n",
      " 789 30766 40015.0\n",
      "Time, predicted, expected \n",
      " 792 34946 35776.0\n",
      "Time, predicted, expected \n",
      " 795 39300 39418.0\n",
      "Time, predicted, expected \n",
      " 798 32932 104912.0\n",
      "Time, predicted, expected \n",
      " 801 90649 73958.0\n",
      "Time, predicted, expected \n",
      " 804 96483 43559.0\n",
      "Time, predicted, expected \n",
      " 807 51801 32867.0\n",
      "Time, predicted, expected \n",
      " 810 32529 50236.0\n",
      "Time, predicted, expected \n",
      " 813 48224 27735.0\n",
      "Time, predicted, expected \n",
      " 816 31489 48646.0\n",
      "Time, predicted, expected \n",
      " 819 45257 17227.0\n",
      "Time, predicted, expected \n",
      " 822 23494 75695.0\n",
      "Time, predicted, expected \n",
      " 825 28402 89404.0\n",
      "Time, predicted, expected \n",
      " 828 84912 14633.0\n",
      "Time, predicted, expected \n",
      " 831 42736 259887.0\n",
      "Time, predicted, expected \n",
      " 834 125832 125832.0\n",
      "Time, predicted, expected \n",
      " 837 258210 66533.0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\priya\\Anaconda3\\envs\\PythonForCloudComputing\\lib\\site-packages\\statsmodels\\base\\model.py:488: HessianInversionWarning: Inverting hessian failed, no bse or cov_params available\n",
      "  'available', HessianInversionWarning)\n",
      "C:\\Users\\priya\\Anaconda3\\envs\\PythonForCloudComputing\\lib\\site-packages\\statsmodels\\base\\model.py:508: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  \"Check mle_retvals\", ConvergenceWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Time, predicted, expected \n",
      " 840 104713 35993.0\n",
      "Time, predicted, expected \n",
      " 843 50582 49375.0\n",
      "Time, predicted, expected \n",
      " 846 43706 101400.0\n",
      "Time, predicted, expected \n",
      " 849 87518 60457.0\n",
      "Time, predicted, expected \n",
      " 852 76826 45092.0\n",
      "Time, predicted, expected \n",
      " 855 49179 65475.0\n",
      "Time, predicted, expected \n",
      " 858 40344 64705.0\n",
      "Time, predicted, expected \n",
      " 861 49456 74241.0\n",
      "Time, predicted, expected \n",
      " 864 68845 71728.0\n",
      "Time, predicted, expected \n",
      " 867 74296 78135.0\n",
      "Time, predicted, expected \n",
      " 870 82115 38759.0\n",
      "Time, predicted, expected \n",
      " 873 61125 73167.0\n",
      "Time, predicted, expected \n",
      " 876 51501 17094.0\n",
      "Time, predicted, expected \n",
      " 879 71257 367838.0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\priya\\Anaconda3\\envs\\PythonForCloudComputing\\lib\\site-packages\\statsmodels\\base\\model.py:488: HessianInversionWarning: Inverting hessian failed, no bse or cov_params available\n",
      "  'available', HessianInversionWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Time, predicted, expected \n",
      " 882 138882 23384.0\n",
      "Time, predicted, expected \n",
      " 885 66751 66751.0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\priya\\Anaconda3\\envs\\PythonForCloudComputing\\lib\\site-packages\\statsmodels\\base\\model.py:488: HessianInversionWarning: Inverting hessian failed, no bse or cov_params available\n",
      "  'available', HessianInversionWarning)\n",
      "C:\\Users\\priya\\Anaconda3\\envs\\PythonForCloudComputing\\lib\\site-packages\\statsmodels\\base\\model.py:508: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  \"Check mle_retvals\", ConvergenceWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Time, predicted, expected \n",
      " 888 38209 29636.0\n",
      "Time, predicted, expected \n",
      " 891 43522 70221.0\n",
      "Time, predicted, expected \n",
      " 894 46121 225655.0\n",
      "Time, predicted, expected \n",
      " 897 158281 66359.0\n",
      "Time, predicted, expected \n",
      " 900 173230 63774.0\n",
      "Time, predicted, expected \n",
      " 903 69565 49842.0\n",
      "Time, predicted, expected \n",
      " 906 35995 17935.0\n",
      "Time, predicted, expected \n",
      " 909 36447 36447.0\n",
      "Time, predicted, expected \n",
      " 912 29167 31877.0\n",
      "Time, predicted, expected \n",
      " 915 29873 30346.0\n",
      "Time, predicted, expected \n",
      " 918 29393 57782.0\n",
      "Time, predicted, expected \n",
      " 921 14321 18041.0\n",
      "Time, predicted, expected \n",
      " 924 4889 17381.0\n",
      "Time, predicted, expected \n",
      " 927 9719 29285.0\n",
      "Time, predicted, expected \n",
      " 930 19394 22680.0\n",
      "Time, predicted, expected \n",
      " 933 23873 46370.0\n",
      "Time, predicted, expected \n",
      " 936 37660 39218.0\n",
      "Time, predicted, expected \n",
      " 939 43522 52644.0\n",
      "Time, predicted, expected \n",
      " 942 49283 61374.0\n",
      "Time, predicted, expected \n",
      " 945 60474 49980.0\n",
      "Time, predicted, expected \n",
      " 948 55097 52809.0\n",
      "Time, predicted, expected \n",
      " 951 57663 10444.0\n",
      "Time, predicted, expected \n",
      " 954 30179 24337.0\n",
      "Time, predicted, expected \n",
      " 957 18087 24861.0\n",
      "Time, predicted, expected \n",
      " 960 23992 6261.0\n",
      "Time, predicted, expected \n",
      " 963 7333 14938.0\n",
      "Time, predicted, expected \n",
      " 966 6413 2901.0\n",
      "Time, predicted, expected \n",
      " 969 492 13373.0\n",
      "Time, predicted, expected \n",
      " 972 6773 6773.0\n",
      "Time, predicted, expected \n",
      " 975 2523 22049.0\n",
      "Time, predicted, expected \n",
      " 978 5351 8940.0\n",
      "Time, predicted, expected \n",
      " 981 17635 33748.0\n",
      "Time, predicted, expected \n",
      " 984 8126 18144.0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\priya\\Anaconda3\\envs\\PythonForCloudComputing\\lib\\site-packages\\statsmodels\\base\\model.py:488: HessianInversionWarning: Inverting hessian failed, no bse or cov_params available\n",
      "  'available', HessianInversionWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Time, predicted, expected \n",
      " 987 34746 15930.0\n",
      "Time, predicted, expected \n",
      " 990 19076 22801.0\n",
      "Time, predicted, expected \n",
      " 993 19711 31749.0\n",
      "Time, predicted, expected \n",
      " 996 29830 7179.0\n",
      "Time, predicted, expected \n",
      " 999 26352 16547.0\n",
      "Time, predicted, expected \n",
      " 1002 10531 15646.0\n",
      "Time, predicted, expected \n",
      " 1005 15925 4204.0\n",
      "Time, predicted, expected \n",
      " 1008 9285 7557.0\n",
      "Time, predicted, expected \n",
      " 1011 2270 10224.0\n",
      "Time, predicted, expected \n",
      " 1014 7758 124.0\n",
      "Time, predicted, expected \n",
      " 1017 2212 1.0\n",
      "RMSE Value=  38669.06582602657\n",
      "MAPE Value=  1391.7706478654586\n",
      "Computational Time= 36.19423188472233\n"
     ]
    },
    {
     "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": [
    "#Referrence: \"https://machinelearningmastery.com/arima-for-time-series-forecasting-with-python/\"\n",
    "from pandas import read_csv\n",
    "import numpy\n",
    "import math\n",
    "from pandas import datetime\n",
    "from matplotlib import pyplot\n",
    "from statsmodels.tsa.arima_model import ARIMA\n",
    "from sklearn.metrics import mean_squared_error\n",
    "\n",
    "#Computational Time\n",
    "import time\n",
    "time_start = time.clock()\n",
    " \n",
    "X = df2.no_of_instances\n",
    "X = X.astype('float32')\n",
    "window=10\n",
    "Begin=0\n",
    "predictions = list()\n",
    "times=list()\n",
    "SE=0\n",
    "sumAPE=0\n",
    "#sumMASE=0\n",
    "for i in range (Begin,((len(X))-window)):\n",
    "    train=X[i:i+window]\n",
    "    test=X[i+window]        \n",
    "    #size = int(len(X) * 0.50)\n",
    "    #print('Size=',size)\n",
    "    #train, test = X[50:size], X[size:180]\n",
    "    #print(len(train))\n",
    "    #print(len(test))\n",
    "    history = [x for x in train]\n",
    "    #print('History \\n', history)\n",
    "    #print('Train \\n', train)\n",
    "    #print('Test \\n', test)\n",
    "    #for t in range(len(test)):\n",
    "    #SE=0\n",
    "    #sumAPE=0\n",
    "    #sumMASE=0\n",
    "    #for t in range (i+window,i+window+1):\n",
    "    model = ARIMA(history, order=(1,1,0))\n",
    "    model_fit = model.fit(disp=0)\n",
    "    output = model_fit.forecast()[0]\n",
    "    #print('OUTPUT= \\n', output)\n",
    "    if (math.isnan(output[0])):\n",
    "        yhat=df2.no_of_instances[i+window]\n",
    "    else:\n",
    "        yhat = np.int(output[0])\n",
    "    if (yhat<0):\n",
    "        yhat=df2.no_of_instances[i+window]\n",
    "    else:\n",
    "        yhat=yhat\n",
    "    predictions.append(yhat)\n",
    "    obs = test\n",
    "    history.append(obs)\n",
    "    #print('XYZ')\n",
    "    t=i+window\n",
    "    tm=df2.time_interval[t]\n",
    "    times.append(tm)\n",
    "    #print('time is ====', times)\n",
    "    print('Time, predicted, expected \\n',df2.time_interval[t], yhat, test)\n",
    "    #Evaluation\n",
    "    e=abs(yhat-obs)\n",
    "    SE=SE+e*e\n",
    "    APE=abs((yhat-obs)/obs)\n",
    "    sumAPE=sumAPE+APE\n",
    "    #ASE=abs((yhat-obs)/(test-df2.no_of_instances[t-1]))\n",
    "    #sumMASE=sumMASE+ASE\n",
    "RMSE=math.sqrt(SE/((len(X))-window))\n",
    "MAPE=(sumAPE/((len(X))-window))*100\n",
    "#MASE=(sumMASE/(len(X)-window))\n",
    "print('RMSE Value= ', RMSE)\n",
    "print('MAPE Value= ', MAPE)\n",
    "#print('MASE Value= ', MASE)\n",
    "\n",
    "#Computational Time\n",
    "time_elapsed = (time.clock() - time_start)\n",
    "print('Computational Time=', time_elapsed)\n",
    "\n",
    "#print('Time= \\n', times)\n",
    "#print('Prediction= \\n', predictions)\n",
    "# plot\n",
    "fig=plt.figure(figsize=(12,8))\n",
    "plt.title(\"Predicted vs Expected- ARIMA- With Sliding Window\")\n",
    "plt.xlabel(\"Time interval\")\n",
    "plt.ylabel(\"Number of instances\")\n",
    "plt.plot(times, predictions, color='red', label='Predicted', linestyle='dashed')\n",
    "plt.plot(df2.time_interval, df2.no_of_instances, color='blue', label='Original', linestyle='solid')\n",
    "plt.legend()\n",
    "plt.show()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
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  "language_info": {
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    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
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