ARIMA_v2.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pyspark\n",
    "from pyspark import SparkContext,SQLContext\n",
    "sc = SparkContext.getOrCreate()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "bitCoinPrices = sc.textFile('bitcoinprices.txt')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "bitCoinPrices.map(lambda x: x.split(',')).take(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "bitCoinPrices=spark.read.csv('bitcoinprices.txt', mode=\"DROPMALFORMED\",inferSchema=True, header = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "bitCoinPrices = bitCoinPrices.withColumnRenamed('_c0','date').withColumnRenamed('_c1','price')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "bitCoinPrices.sort('date').head(657)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "tweetSents=spark.read.csv('sentiment6.txt', mode=\"DROPMALFORMED\",inferSchema=True, header = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "tweetSents = tweetSents.withColumnRenamed('_c0','date').withColumnRenamed('_c1','SentimentProb')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "tweetSents.sort('date').head(643)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df1 = flintContext.read.pandas(pd.read_csv(bitcoinprices.txt))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'flintContext' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-2-0572c1bfc892>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mdf1\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mflintContext\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mread\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpandas\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mpd\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mread_csv\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mbitcoinprices\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtxt\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m: name 'flintContext' is not defined"
     ]
    }
   ],
   "source": [
    "df1 = flintContext.read.pandas(pd.read_csv(bitcoinprices.txt))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "ename": "ImportError",
     "evalue": "No module named 'ts'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mImportError\u001b[0m                               Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-4-db01aeac061b>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[1;32mimport\u001b[0m \u001b[0mts\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mflint\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      2\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[0mts\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mflint\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mFlintContext\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mImportError\u001b[0m: No module named 'ts'"
     ]
    }
   ],
   "source": [
    "import ts.flint\n",
    "from ts.flint import FlintContext"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                             ARIMA Model Results                              \n",
      "==============================================================================\n",
      "Dep. Variable:                D.427.1   No. Observations:                  655\n",
      "Model:                ARIMA(10, 1, 0)   Log Likelihood               -5765.509\n",
      "Method:                       css-mle   S.D. of innovations           1605.919\n",
      "Date:                Wed, 15 Nov 2017   AIC                          11555.018\n",
      "Time:                        21:44:04   BIC                          11608.833\n",
      "Sample:                    06-13-2016   HQIC                         11575.884\n",
      "                         - 05-24-2017                                         \n",
      "==================================================================================\n",
      "                     coef    std err          z      P>|z|      [0.025      0.975]\n",
      "----------------------------------------------------------------------------------\n",
      "const              0.2322      7.744      0.030      0.976     -14.946      15.410\n",
      "ar.L1.D.427.1     -0.9627      0.039    -24.934      0.000      -1.038      -0.887\n",
      "ar.L2.D.427.1     -0.9628      0.052    -18.344      0.000      -1.066      -0.860\n",
      "ar.L3.D.427.1     -0.9123      0.062    -14.804      0.000      -1.033      -0.791\n",
      "ar.L4.D.427.1     -0.9390      0.067    -14.099      0.000      -1.070      -0.808\n",
      "ar.L5.D.427.1     -0.8788      0.069    -12.784      0.000      -1.014      -0.744\n",
      "ar.L6.D.427.1     -0.8319      0.069    -12.123      0.000      -0.966      -0.697\n",
      "ar.L7.D.427.1     -0.6530      0.066     -9.831      0.000      -0.783      -0.523\n",
      "ar.L8.D.427.1     -0.5174      0.061     -8.412      0.000      -0.638      -0.397\n",
      "ar.L9.D.427.1     -0.3347      0.053     -6.375      0.000      -0.438      -0.232\n",
      "ar.L10.D.427.1    -0.1598      0.039     -4.133      0.000      -0.236      -0.084\n",
      "                                    Roots                                     \n",
      "==============================================================================\n",
      "                  Real           Imaginary           Modulus         Frequency\n",
      "------------------------------------------------------------------------------\n",
      "AR.1             0.8465           -0.6878j            1.0907           -0.1086\n",
      "AR.2             0.8465           +0.6878j            1.0907            0.1086\n",
      "AR.3             0.4148           -1.1292j            1.2030           -0.1940\n",
      "AR.4             0.4148           +1.1292j            1.2030            0.1940\n",
      "AR.5            -0.2562           -1.1801j            1.2076           -0.2840\n",
      "AR.6            -0.2562           +1.1801j            1.2076            0.2840\n",
      "AR.7            -1.1750           -0.4087j            1.2441           -0.4467\n",
      "AR.8            -1.1750           +0.4087j            1.2441            0.4467\n",
      "AR.9            -0.8773           -0.9170j            1.2690           -0.3715\n",
      "AR.10           -0.8773           +0.9170j            1.2690            0.3715\n",
      "------------------------------------------------------------------------------\n"
     ]
    },
    {
     "data": {
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zraqe15dujjp+9j3vc8ED8xg58yXWbffeALWtqp4n5q1L+l27ahpZs62av7yz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\nTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1d9007099e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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OtjH+LPGYlLe32zeUOrkSzxk1JfQNjrCj0wYYGOPPEo9JeXu6nZmg55QVJDiS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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1d9017188d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                 0\n",
      "count   655.000000\n",
      "mean      4.884351\n",
      "std    1608.617675\n",
      "min   -2960.046887\n",
      "25%    -998.792861\n",
      "50%    -468.573260\n",
      "75%     482.996348\n",
      "max    6497.914552\n"
     ]
    }
   ],
   "source": [
    "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",
    "def parser(x):\n",
    "    return datetime.strptime(x, '%Y%m%d')\n",
    "\n",
    "series = read_csv('bitcoinprices.txt', header=0, parse_dates=[0], index_col=0, squeeze=True, date_parser=parser)\n",
    "# fit model\n",
    "model = ARIMA(series, order=(10,1,0))\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": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "predicted=1131.924869, expected=1160.800000\n",
      "predicted=1170.547150, expected=1185.400000\n",
      "predicted=1176.787441, expected=1189.500000\n",
      "predicted=1205.632655, expected=1186.200000\n",
      "predicted=1181.409992, expected=1209.500000\n",
      "predicted=1207.497883, expected=1203.600000\n",
      "predicted=1198.542001, expected=1228.400000\n",
      "predicted=1234.204510, expected=1211.700000\n",
      "predicted=1214.034524, expected=1178.900000\n",
      "predicted=1179.338993, expected=1180.600000\n",
      "predicted=1186.100887, expected=1188.700000\n",
      "predicted=1188.088996, expected=1171.800000\n",
      "predicted=1184.020209, expected=1193.000000\n",
      "predicted=1190.549582, expected=1196.400000\n",
      "predicted=1191.032111, expected=1219.000000\n",
      "predicted=1218.158646, expected=1248.900000\n",
      "predicted=1250.968988, expected=1249.900000\n",
      "predicted=1247.991102, expected=1250.000000\n",
      "predicted=1255.063327, expected=1254.200000\n",
      "predicted=1251.173412, expected=1267.000000\n",
      "predicted=1273.833156, expected=1290.300000\n",
      "predicted=1294.878080, expected=1318.100000\n",
      "predicted=1317.511181, expected=1358.600000\n",
      "predicted=1355.473493, expected=1350.100000\n",
      "predicted=1344.784704, expected=1349.800000\n",
      "predicted=1350.949127, expected=1374.700000\n",
      "predicted=1377.227513, expected=1460.000000\n",
      "predicted=1464.127557, expected=1469.000000\n",
      "predicted=1475.129079, expected=1560.200000\n",
      "predicted=1555.679824, expected=1620.000000\n",
      "predicted=1613.594885, expected=1586.900000\n",
      "predicted=1583.958339, expected=1599.400000\n",
      "predicted=1620.590858, expected=1620.700000\n",
      "predicted=1617.525455, expected=1706.000000\n",
      "predicted=1732.566885, expected=1727.900000\n",
      "predicted=1737.132850, expected=1838.500000\n",
      "predicted=1838.684060, expected=1787.900000\n",
      "predicted=1784.040613, expected=1699.900000\n",
      "predicted=1698.041310, expected=1799.800000\n",
      "predicted=1824.508692, expected=1774.900000\n",
      "predicted=1784.460201, expected=1744.100000\n",
      "predicted=1789.977027, expected=1829.000000\n",
      "predicted=1810.583733, expected=1804.000000\n",
      "predicted=1776.289599, expected=1924.900000\n",
      "predicted=1938.665334, expected=1993.000000\n",
      "predicted=1982.087626, expected=2073.400000\n",
      "predicted=2077.787080, expected=2173.300000\n",
      "predicted=2173.144998, expected=2166.900000\n",
      "predicted=2177.340053, expected=2312.100000\n",
      "predicted=2353.478398, expected=2675.000000\n",
      "predicted=2729.538515, expected=2434.300000\n",
      "predicted=2521.227597, expected=2126.700000\n",
      "predicted=2192.437465, expected=2219.900000\n",
      "predicted=2267.553479, expected=2226.900000\n",
      "predicted=2210.937489, expected=2287.000000\n",
      "predicted=2382.663109, expected=2188.000000\n",
      "predicted=2076.024796, expected=2377.900000\n",
      "predicted=2427.001155, expected=2434.900000\n",
      "predicted=2285.718401, expected=2458.200000\n",
      "predicted=2465.057423, expected=2518.400000\n",
      "predicted=2545.356883, expected=2584.900000\n",
      "predicted=2531.707461, expected=2845.800000\n",
      "predicted=2854.724686, expected=2818.700000\n",
      "predicted=2784.662659, expected=2712.000000\n",
      "predicted=2760.327596, expected=2727.700000\n",
      "predicted=2753.909838, expected=2814.100000\n",
      "predicted=2810.864977, expected=2909.500000\n",
      "predicted=2898.695116, expected=2978.900000\n",
      "predicted=2972.391247, expected=2749.900000\n",
      "predicted=2749.528822, expected=2800.000000\n",
      "predicted=2845.162376, expected=2362.900000\n",
      "predicted=2355.875274, expected=2455.700000\n",
      "predicted=2516.245923, expected=2466.700000\n",
      "predicted=2482.689243, expected=2660.000000\n",
      "predicted=2602.144612, expected=2574.000000\n",
      "predicted=2560.706251, expected=2624.400000\n",
      "predicted=2530.643286, expected=2696.800000\n",
      "predicted=2764.077180, expected=2560.000000\n",
      "predicted=2560.586733, expected=2712.100000\n",
      "predicted=2745.801813, expected=2706.900000\n",
      "predicted=2784.942755, expected=2535.800000\n",
      "predicted=2540.127330, expected=2475.100000\n",
      "predicted=2479.719725, expected=2400.900000\n",
      "predicted=2435.177319, expected=2487.200000\n",
      "predicted=2481.743783, expected=2564.800000\n",
      "predicted=2554.993948, expected=2515.700000\n",
      "predicted=2528.072223, expected=2491.900000\n",
      "predicted=2477.426457, expected=2415.200000\n",
      "predicted=2427.048630, expected=2499.900000\n",
      "predicted=2528.917717, expected=2627.000000\n",
      "predicted=2639.786744, expected=2562.700000\n",
      "predicted=2569.524658, expected=2618.200000\n",
      "predicted=2597.334077, expected=2577.000000\n",
      "predicted=2576.844470, expected=2498.600000\n",
      "predicted=2518.992635, expected=2555.500000\n",
      "predicted=2569.756363, expected=2513.800000\n",
      "predicted=2542.963449, expected=2343.400000\n",
      "predicted=2350.918711, expected=2312.500000\n",
      "predicted=2313.503663, expected=2384.000000\n",
      "predicted=2404.339863, expected=2337.500000\n",
      "predicted=2337.357382, expected=2106.100000\n",
      "predicted=2128.183443, expected=1984.800000\n",
      "predicted=2024.570623, expected=2028.400000\n",
      "predicted=2044.497974, expected=2238.400000\n",
      "predicted=2237.037219, expected=2348.300000\n",
      "predicted=2335.056754, expected=2319.100000\n",
      "predicted=2292.173954, expected=2759.700000\n",
      "predicted=2757.917788, expected=2752.100000\n",
      "predicted=2745.357688, expected=2788.000000\n",
      "predicted=2805.702360, expected=2741.000000\n",
      "predicted=2770.701086, expected=2763.600000\n",
      "predicted=2775.207446, expected=2451.000000\n",
      "predicted=2400.437535, expected=2560.000000\n",
      "predicted=2516.170788, expected=2682.100000\n",
      "predicted=2671.490654, expected=2740.900000\n",
      "predicted=2669.225041, expected=2741.000000\n",
      "predicted=2746.130293, expected=2722.100000\n",
      "predicted=2728.390130, expected=2863.000000\n",
      "predicted=2861.590586, expected=2732.000000\n",
      "predicted=2740.457117, expected=2711.600000\n",
      "predicted=2738.536177, expected=2779.500000\n",
      "predicted=2785.808733, expected=3112.000000\n",
      "predicted=3065.507445, expected=3207.400000\n",
      "predicted=3167.859172, expected=3222.500000\n",
      "predicted=3203.455583, expected=3402.900000\n",
      "predicted=3403.932412, expected=3402.500000\n",
      "predicted=3372.562346, expected=3362.000000\n",
      "predicted=3335.927739, expected=3473.200000\n",
      "predicted=3497.394115, expected=3761.800000\n",
      "predicted=3755.427507, expected=4094.500000\n",
      "predicted=4072.590915, expected=4074.900000\n",
      "predicted=4061.443886, expected=4220.000000\n",
      "predicted=4249.376390, expected=4061.300000\n",
      "predicted=4055.315128, expected=4319.000000\n",
      "predicted=4318.599109, expected=4269.800000\n",
      "predicted=4334.075520, expected=4045.400000\n",
      "predicted=4064.022979, expected=4098.000000\n",
      "predicted=4112.813583, expected=4002.100000\n",
      "predicted=4007.746395, expected=3847.400000\n",
      "predicted=3799.196501, expected=4090.200000\n",
      "predicted=4095.203236, expected=4164.000000\n",
      "predicted=4179.896762, expected=4320.000000\n",
      "predicted=4285.863197, expected=4320.500000\n",
      "predicted=4324.302924, expected=4356.000000\n",
      "predicted=4381.049635, expected=4265.500000\n",
      "predicted=4256.278756, expected=4371.200000\n",
      "predicted=4380.932643, expected=4604.000000\n",
      "predicted=4634.811493, expected=4610.200000\n",
      "predicted=4607.534484, expected=4713.300000\n",
      "predicted=4711.653426, expected=4750.000000\n",
      "predicted=4755.003398, expected=4660.000000\n",
      "predicted=4652.487901, expected=4566.700000\n",
      "predicted=4575.943206, expected=4337.300000\n",
      "predicted=4365.519103, expected=4510.500000\n",
      "predicted=4521.438243, expected=4538.700000\n",
      "predicted=4537.225584, expected=4621.700000\n",
      "predicted=4626.139651, expected=4436.600000\n",
      "predicted=4440.605955, expected=4280.100000\n",
      "predicted=4272.733130, expected=4184.100000\n",
      "predicted=4179.631284, expected=4347.700000\n",
      "predicted=4377.896937, expected=4020.800000\n",
      "predicted=4061.150513, expected=3830.600000\n",
      "predicted=3832.917484, expected=3167.300000\n",
      "predicted=3148.843922, expected=3789.900000\n",
      "predicted=3721.181918, expected=3643.200000\n",
      "predicted=3745.897799, expected=3900.000000\n",
      "predicted=3708.258332, expected=3969.200000\n",
      "predicted=4052.890232, expected=3944.900000\n",
      "predicted=3899.139036, expected=3871.900000\n",
      "predicted=3898.634597, expected=3753.200000\n",
      "predicted=3763.655407, expected=3712.900000\n",
      "predicted=3799.038340, expected=3659.900000\n",
      "predicted=3595.991489, expected=3773.000000\n",
      "predicted=3810.407828, expected=3886.700000\n",
      "predicted=3883.985961, expected=3933.600000\n",
      "predicted=3934.820535, expected=4197.900000\n",
      "predicted=4177.994727, expected=4051.800000\n",
      "predicted=4076.279921, expected=4244.900000\n",
      "predicted=4186.023139, expected=4315.000000\n",
      "predicted=4348.559501, expected=4409.000000\n",
      "predicted=4396.137765, expected=4292.000000\n",
      "predicted=4302.816002, expected=4332.300000\n",
      "predicted=4339.602687, expected=4157.600000\n",
      "predicted=4200.457495, expected=4394.700000\n",
      "predicted=4350.026625, expected=4345.000000\n",
      "predicted=4423.837030, expected=4463.600000\n",
      "predicted=4414.030733, expected=4561.000000\n",
      "predicted=4590.717464, expected=4827.000000\n",
      "predicted=4804.070276, expected=4764.100000\n",
      "predicted=4791.744934, expected=4896.900000\n",
      "predicted=4848.183540, expected=5511.700000\n",
      "predicted=5526.703045, expected=5585.700000\n",
      "predicted=5611.475654, expected=5739.900000\n",
      "predicted=5726.468306, expected=5661.000000\n",
      "predicted=5739.215874, expected=5637.600000\n",
      "predicted=5638.735624, expected=5372.200000\n",
      "predicted=5390.497509, expected=5572.300000\n",
      "predicted=5584.648964, expected=5691.400000\n",
      "predicted=5746.262773, expected=6080.000000\n",
      "predicted=6061.883044, expected=5861.700000\n",
      "predicted=5940.101643, expected=5996.000000\n",
      "predicted=5918.916990, expected=5820.500000\n",
      "predicted=5887.679333, expected=5470.000000\n",
      "predicted=5441.629503, expected=5735.000000\n",
      "predicted=5755.150264, expected=5895.000000\n",
      "predicted=5972.697175, expected=5768.100000\n",
      "predicted=5771.727576, expected=5795.000000\n",
      "predicted=5778.850580, expected=6125.000000\n",
      "predicted=6108.006964, expected=6135.300000\n",
      "predicted=6096.833865, expected=6406.500000\n",
      "predicted=6404.500817, expected=6657.300000\n",
      "predicted=6695.266464, expected=7210.100000\n",
      "predicted=7164.324354, expected=7191.000000\n",
      "predicted=7266.238541, expected=7489.900000\n",
      "predicted=7512.093253, expected=7316.700000\n",
      "predicted=7407.825367, expected=7186.800000\n",
      "predicted=7135.793483, expected=7364.300000\n",
      "predicted=7440.824509, expected=7376.900000\n",
      "predicted=7435.657469, expected=7195.000000\n",
      "predicted=7208.926001, expected=6704.100000\n",
      "predicted=6700.998647, expected=6079.300000\n",
      "predicted=5965.979839, expected=6241.000000\n",
      "predicted=6174.557138, expected=6570.000000\n",
      "predicted=6682.627290, expected=6565.000000\n"
     ]
    }
   ],
   "source": [
    "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",
    "import numpy as np\n",
    "def parser(x):    \n",
    "    return datetime.strptime(x, '%Y-%m-%d')\n",
    "\n",
    "series = read_csv('bitcoinsort.csv', header='infer', dtype={'time':np.str,'price':np.float64},parse_dates=[0], index_col=0, squeeze=True, date_parser=parser)\n",
    "\n",
    "X = series.values\n",
    "size = int(len(X) * 0.66)\n",
    "train, test = X[0:size], X[size:len(X)]\n",
    "history = [x for x in train]\n",
    "predictions = list()\n",
    "for t in range(len(test)):\n",
    "    model = ARIMA(history, order=(10,1,0))\n",
    "    model_fit = model.fit(disp=0)\n",
    "    output = model_fit.forecast()\n",
    "    yhat = output[0]\n",
    "    predictions.append(yhat)\n",
    "    obs = test[t]\n",
    "    history.append(obs)\n",
    "    print('predicted=%f, expected=%f' % (yhat, obs))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>time</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2016-02-27</td>\n",
       "      <td>427.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2016-11-18</td>\n",
       "      <td>750.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2016-06-13</td>\n",
       "      <td>690.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2016-08-08</td>\n",
       "      <td>588.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2017-02-06</td>\n",
       "      <td>1047.3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        time   price\n",
       "0 2016-02-27   427.1\n",
       "1 2016-11-18   750.9\n",
       "2 2016-06-13   690.9\n",
       "3 2016-08-08   588.7\n",
       "4 2017-02-06  1047.3"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "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",
    "def parser(x):\n",
    "    return datetime.strptime(x, '%Y%m%d')\n",
    "\n",
    "y = read_csv('bitcoinprices.txt', header=None,names=['time','price'], parse_dates=[0], index_col=False, squeeze=True, date_parser=parser)\n",
    "y.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "#x=y.sort([\"time\"])\n",
    "x = y.sort_values('time',axis=0)\n",
    "x.to_csv('bitcoinsort.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test MSE: 32409.630\n"
     ]
    },
    {
     "data": {
      "image/png": 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HzekTGWg0/2S4+9O/LM9IlAAghBBdR+yBPG7dvQa3qkr8H7yPR+YMo9TLH1vf/hRsjmN0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\n1ORmAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1d97f4ae048>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.metrics import mean_squared_error\n",
    "error = mean_squared_error(test, predictions)\n",
    "print('Test MSE: %.3f' % error)\n",
    "# plot\n",
    "pyplot.plot(test)\n",
    "pyplot.plot(predictions, color='red')\n",
    "pyplot.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}