Naive_Average_Holt_winter_not_same_data.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 303,
   "metadata": {},
   "outputs": [],
   "source": [
    "import requests, pandas as pd, numpy as np\n",
    "from pandas import DataFrame\n",
    "from io import StringIO\n",
    "import time, json\n",
    "from datetime import date\n",
    "from statsmodels.tsa.stattools import adfuller, acf, pacf\n",
    "from statsmodels.tsa.arima_model import ARIMA\n",
    "from statsmodels.tsa.seasonal import seasonal_decompose\n",
    "from sklearn.metrics import *\n",
    "import matplotlib.pylab as plt\n",
    "%matplotlib inline\n",
    "from matplotlib.pylab import rcParams\n",
    "rcParams['figure.figsize'] = 15, 6"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 304,
   "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>Timestamp</th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Volume_(BTC)</th>\n",
       "      <th>Volume_(Currency)</th>\n",
       "      <th>Weighted_Price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1325317920</td>\n",
       "      <td>4.39</td>\n",
       "      <td>4.39</td>\n",
       "      <td>4.39</td>\n",
       "      <td>4.39</td>\n",
       "      <td>0.455581</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1325317980</td>\n",
       "      <td>4.39</td>\n",
       "      <td>4.39</td>\n",
       "      <td>4.39</td>\n",
       "      <td>4.39</td>\n",
       "      <td>0.455581</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1325318040</td>\n",
       "      <td>4.39</td>\n",
       "      <td>4.39</td>\n",
       "      <td>4.39</td>\n",
       "      <td>4.39</td>\n",
       "      <td>0.455581</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1325318100</td>\n",
       "      <td>4.39</td>\n",
       "      <td>4.39</td>\n",
       "      <td>4.39</td>\n",
       "      <td>4.39</td>\n",
       "      <td>0.455581</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1325318160</td>\n",
       "      <td>4.39</td>\n",
       "      <td>4.39</td>\n",
       "      <td>4.39</td>\n",
       "      <td>4.39</td>\n",
       "      <td>0.455581</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1325318220</td>\n",
       "      <td>4.39</td>\n",
       "      <td>4.39</td>\n",
       "      <td>4.39</td>\n",
       "      <td>4.39</td>\n",
       "      <td>0.455581</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1325318280</td>\n",
       "      <td>4.39</td>\n",
       "      <td>4.39</td>\n",
       "      <td>4.39</td>\n",
       "      <td>4.39</td>\n",
       "      <td>0.455581</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1325318340</td>\n",
       "      <td>4.39</td>\n",
       "      <td>4.39</td>\n",
       "      <td>4.39</td>\n",
       "      <td>4.39</td>\n",
       "      <td>0.455581</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1325318400</td>\n",
       "      <td>4.39</td>\n",
       "      <td>4.39</td>\n",
       "      <td>4.39</td>\n",
       "      <td>4.39</td>\n",
       "      <td>0.455581</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1325318460</td>\n",
       "      <td>4.39</td>\n",
       "      <td>4.39</td>\n",
       "      <td>4.39</td>\n",
       "      <td>4.39</td>\n",
       "      <td>0.455581</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.39</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Timestamp  Open  High   Low  Close  Volume_(BTC)  Volume_(Currency)  \\\n",
       "0  1325317920  4.39  4.39  4.39   4.39      0.455581                2.0   \n",
       "1  1325317980  4.39  4.39  4.39   4.39      0.455581                2.0   \n",
       "2  1325318040  4.39  4.39  4.39   4.39      0.455581                2.0   \n",
       "3  1325318100  4.39  4.39  4.39   4.39      0.455581                2.0   \n",
       "4  1325318160  4.39  4.39  4.39   4.39      0.455581                2.0   \n",
       "5  1325318220  4.39  4.39  4.39   4.39      0.455581                2.0   \n",
       "6  1325318280  4.39  4.39  4.39   4.39      0.455581                2.0   \n",
       "7  1325318340  4.39  4.39  4.39   4.39      0.455581                2.0   \n",
       "8  1325318400  4.39  4.39  4.39   4.39      0.455581                2.0   \n",
       "9  1325318460  4.39  4.39  4.39   4.39      0.455581                2.0   \n",
       "\n",
       "   Weighted_Price  \n",
       "0            4.39  \n",
       "1            4.39  \n",
       "2            4.39  \n",
       "3            4.39  \n",
       "4            4.39  \n",
       "5            4.39  \n",
       "6            4.39  \n",
       "7            4.39  \n",
       "8            4.39  \n",
       "9            4.39  "
      ]
     },
     "execution_count": 304,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# df = pd.read_csv('./Datasets/bitcoin_price.csv', parse_dates=['Date'], usecols=['Date','Close'])\n",
    "# df = pd.read_csv('bitstampUSD_1-min_data_2012-01-01_to_2018-01-08.csv', parse_dates=['Timestamp'], usecols=['Timestamp','Close'])\n",
    "df = pd.read_csv('bitstampUSD_1-min_data_2012-01-01_to_2018-01-08.csv')\n",
    "df.isnull().values.any()\n",
    "df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 357,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Date'] = pd.to_datetime(df['Timestamp'],unit='s').dt.date\n",
    "# group = df.groupby('Date')\n",
    "# Daily_Price = group['Weighted_Price'].mean()\n",
    "\n",
    "# Daily_Price.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 358,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f8e3c13ff28>]"
      ]
     },
     "execution_count": 358,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f8e2cd41978>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# convert the date column into a time series with daily frequency\n",
    "\n",
    "df['Date'] = pd.to_datetime(df['Date'])\n",
    "\n",
    "indexed_df = df.set_index('Date')\n",
    "\n",
    "ts = indexed_df['Close']\n",
    "\n",
    "ts.head()\n",
    "\n",
    "# visualize the time series to see how Bitcoin changes over time\n",
    "\n",
    "plt.plot(ts)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 401,
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "Of the three parameters: start, end, and periods, exactly two must be specified",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-401-358ec50b6d94>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      6\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      7\u001b[0m \u001b[0;31m# new_df= pd.DataFrame(list(ts_week), index = ts_week.index, columns = [\"Close\"])\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 8\u001b[0;31m \u001b[0mnew_df\u001b[0m\u001b[0;34m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mDataFrame\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mts_week\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mindex\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdate_range\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mts_week\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mfreq\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'D'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcolumns\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m\"Close\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/pandas/core/indexes/datetimes.py\u001b[0m in \u001b[0;36mdate_range\u001b[0;34m(start, end, periods, freq, tz, normalize, name, closed, **kwargs)\u001b[0m\n\u001b[1;32m   2055\u001b[0m     return DatetimeIndex(start=start, end=end, periods=periods,\n\u001b[1;32m   2056\u001b[0m                          \u001b[0mfreq\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mfreq\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtz\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtz\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnormalize\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mnormalize\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2057\u001b[0;31m                          closed=closed, **kwargs)\n\u001b[0m\u001b[1;32m   2058\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2059\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/pandas/util/_decorators.py\u001b[0m in \u001b[0;36mwrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    116\u001b[0m                 \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    117\u001b[0m                     \u001b[0mkwargs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mnew_arg_name\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnew_arg_value\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 118\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    119\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mwrapper\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    120\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0m_deprecate_kwarg\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/pandas/core/indexes/datetimes.py\u001b[0m in \u001b[0;36m__new__\u001b[0;34m(cls, data, freq, start, end, periods, copy, name, tz, verify_integrity, normalize, closed, ambiguous, dtype, **kwargs)\u001b[0m\n\u001b[1;32m    322\u001b[0m             return cls._generate(start, end, periods, name, freq,\n\u001b[1;32m    323\u001b[0m                                  \u001b[0mtz\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtz\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnormalize\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mnormalize\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mclosed\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mclosed\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 324\u001b[0;31m                                  ambiguous=ambiguous)\n\u001b[0m\u001b[1;32m    325\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    326\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mndarray\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mIndex\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mABCSeries\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/pandas/core/indexes/datetimes.py\u001b[0m in \u001b[0;36m_generate\u001b[0;34m(cls, start, end, periods, name, offset, tz, normalize, ambiguous, closed)\u001b[0m\n\u001b[1;32m    419\u001b[0m                   tz=None, normalize=False, ambiguous='raise', closed=None):\n\u001b[1;32m    420\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mcom\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_count_not_none\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstart\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mend\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mperiods\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0;36m2\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 421\u001b[0;31m             raise ValueError('Of the three parameters: start, end, and '\n\u001b[0m\u001b[1;32m    422\u001b[0m                              'periods, exactly two must be specified')\n\u001b[1;32m    423\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mValueError\u001b[0m: Of the three parameters: start, end, and periods, exactly two must be specified"
     ]
    }
   ],
   "source": [
    "# resamply by week\n",
    "\n",
    "ts_week = ts.resample('D').mean()\n",
    "\n",
    "ts_week\n",
    "\n",
    "# new_df= pd.DataFrame(list(ts_week), index = ts_week.index, columns = [\"Close\"])\n",
    "# new_df= pd.DataFrame(list(ts_week), index = pd.date_range(ts_week.index,freq='D'), columns = [\"Close\"])\n",
    "new_df= pd.DataFrame(list(ts_week), index = ts_week.index, columns = [\"Close\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 360,
   "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>Close</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2011-12-31</th>\n",
       "      <td>4.472624</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-01</th>\n",
       "      <td>4.680778</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-02</th>\n",
       "      <td>5.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-03</th>\n",
       "      <td>5.145917</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-04</th>\n",
       "      <td>5.228729</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Close\n",
       "Date                \n",
       "2011-12-31  4.472624\n",
       "2012-01-01  4.680778\n",
       "2012-01-02  5.000000\n",
       "2012-01-03  5.145917\n",
       "2012-01-04  5.228729"
      ]
     },
     "execution_count": 360,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 361,
   "metadata": {},
   "outputs": [],
   "source": [
    "# new_df = new_df.fillna(lambda x: x.median())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 362,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2201"
      ]
     },
     "execution_count": 362,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(new_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 382,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                  Close\n",
      "Date                   \n",
      "2017-06-22  2680.862333\n",
      "2017-06-23  2718.264889\n",
      "2017-06-24  2657.009215\n",
      "2017-06-25  2559.341569\n",
      "2017-06-26  2464.920389\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 382,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train=new_df[0:2000] \n",
    "test=new_df[2000:]\n",
    "# print(train.tail())\n",
    "print(test.head())\n",
    "# print(test.tail())\n",
    "np.any(np.isnan(train.Close))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 383,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 383,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.all(np.isfinite(train.Close))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 384,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/rahulworld/anaconda3/lib/python3.6/site-packages/pandas/core/generic.py:3643: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  self[name] = value\n"
     ]
    }
   ],
   "source": [
    "train.Close = train.Close.dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 385,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/rahulworld/anaconda3/lib/python3.6/site-packages/pandas/core/generic.py:3643: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  self[name] = value\n"
     ]
    }
   ],
   "source": [
    "test.Close = test.Close.dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 386,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/rahulworld/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n",
      "/home/rahulworld/anaconda3/lib/python3.6/site-packages/pandas/core/frame.py:2554: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  self._where(-key, value, inplace=True)\n"
     ]
    }
   ],
   "source": [
    "test[test==np.inf]=np.nan"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 387,
   "metadata": {},
   "outputs": [],
   "source": [
    "test = test.dropna()\n",
    "\n",
    "train = train.dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 388,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 388,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.all(np.isfinite(train.Close))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 389,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f8e3fa32b70>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "train.Close.plot(figsize=(15,8), title= 'Daily Price', fontsize=14)\n",
    "test.Close.plot(figsize=(15,8), title= 'Daily Price', fontsize=14)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 390,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f8e3c1792b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dd= np.asarray(train.Close)\n",
    "y_hat = test.copy()\n",
    "y_hat['naive'] = dd[len(dd)-1]\n",
    "\n",
    "#ploting Graph between Train test NAIVE Approach for forecast\n",
    "plt.figure(figsize=(12,8))\n",
    "plt.plot(train.index, train['Close'], label='Train')\n",
    "plt.plot(test.index,test['Close'], label='Test')\n",
    "plt.plot(y_hat.index,y_hat['naive'], label='Naive Forecast')\n",
    "plt.legend(loc='best')\n",
    "plt.title(\"Naive Forecast\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 391,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5956.102669944819\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import mean_squared_error\n",
    "from math import sqrt\n",
    "rms = sqrt(mean_squared_error(test.Close, y_hat.naive))\n",
    "print(rms)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 392,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f8e5141a160>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "y_hat_avg = test.copy()\n",
    "y_hat_avg['avg_forecast'] = train['Close'].mean()\n",
    "\n",
    "plt.figure(figsize=(12,8))\n",
    "plt.plot(train['Close'], label='Train')\n",
    "plt.plot(test['Close'], label='Test')\n",
    "plt.plot(y_hat_avg['avg_forecast'], label='Average Forecast')\n",
    "plt.legend(loc='best')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 393,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "7653.748461193343\n"
     ]
    }
   ],
   "source": [
    "rms = sqrt(mean_squared_error(test.Close, y_hat_avg.avg_forecast))\n",
    "print(rms)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 394,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f8e3c163eb8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "y_hat_avg = test.copy()\n",
    "y_hat_avg['moving_avg_forecast'] = train['Close'].rolling(10).mean().iloc[-1]\n",
    "\n",
    "plt.figure(figsize=(16,8))\n",
    "plt.plot(train['Close'], label='Train')\n",
    "plt.plot(test['Close'], label='Test')\n",
    "plt.plot(y_hat_avg['moving_avg_forecast'], label='Moving Average Forecast')\n",
    "plt.legend(loc='best')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 395,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "6027.1875898986145\n"
     ]
    }
   ],
   "source": [
    "rms = sqrt(mean_squared_error(test.Close, y_hat_avg.moving_avg_forecast))\n",
    "print(rms)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 396,
   "metadata": {},
   "outputs": [],
   "source": [
    "from statsmodels.tsa.api import ExponentialSmoothing, SimpleExpSmoothing, Holt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 397,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f8e2e0017f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "y_hat_avg = test.copy()\n",
    "# print(y_hat_avg)\n",
    "fit2 = SimpleExpSmoothing(np.asarray(train['Close'])).fit(smoothing_level=0.6,optimized=False)\n",
    "\n",
    "y_hat_avg['SES'] = fit2.forecast(len(test))\n",
    "# print(y_hat_avg['SES'])\n",
    "\n",
    "plt.figure(figsize=(16,8))\n",
    "plt.plot(train['Close'], label='Train')\n",
    "plt.plot(test['Close'], label='Test')\n",
    "plt.plot(y_hat_avg['SES'], label='SES')\n",
    "plt.legend(loc='best')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 398,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5980.438676360839\n"
     ]
    }
   ],
   "source": [
    "rms = sqrt(mean_squared_error(test.Close, y_hat_avg.SES))\n",
    "print(rms)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 400,
   "metadata": {},
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "not all arguments converted during string formatting",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-400-192a7e50bed2>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mstatsmodels\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mapi\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0msm\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0msm\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtsa\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mseasonal_decompose\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtrain\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mClose\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mfreq\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'D'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      3\u001b[0m \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msm\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtsa\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstattools\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0madfuller\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtrain\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mClose\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshow\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/statsmodels-0.9.0rc1-py3.6-linux-x86_64.egg/statsmodels/tsa/seasonal.py\u001b[0m in \u001b[0;36mseasonal_decompose\u001b[0;34m(x, model, filt, freq, two_sided, extrapolate_trend)\u001b[0m\n\u001b[1;32m    130\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    131\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mfilt\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 132\u001b[0;31m         \u001b[0;32mif\u001b[0m \u001b[0mfreq\u001b[0m \u001b[0;34m%\u001b[0m \u001b[0;36m2\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m  \u001b[0;31m# split weights at ends\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    133\u001b[0m             \u001b[0mfilt\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m.5\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mfreq\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;36m.5\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m/\u001b[0m \u001b[0mfreq\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    134\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mTypeError\u001b[0m: not all arguments converted during string formatting"
     ]
    }
   ],
   "source": [
    "import statsmodels.api as sm\n",
    "sm.tsa.seasonal_decompose(train.Close,freq='D').plot()\n",
    "result = sm.tsa.stattools.adfuller(train.Close)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 356,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f8e2d7e8b70>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "y_hat_avg = test.copy()\n",
    "\n",
    "fit1 = Holt(np.asarray(train['Close'])).fit(smoothing_level = 0.3,smoothing_slope = 0.1)\n",
    "y_hat_avg['Holt_linear'] = fit1.forecast(len(test))\n",
    "\n",
    "plt.figure(figsize=(16,8))\n",
    "plt.plot(train['Close'], label='Train')\n",
    "plt.plot(test['Close'], label='Test')\n",
    "plt.plot(y_hat_avg['Holt_linear'], label='Holt_linear')\n",
    "plt.legend(loc='best')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 201,
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "Input contains NaN, infinity or a value too large for dtype('float64').",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-201-f55e93df12ee>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mrms\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msqrt\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmean_squared_error\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtest\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mClose\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_hat_avg\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mHolt_linear\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrms\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/sklearn/metrics/regression.py\u001b[0m in \u001b[0;36mmean_squared_error\u001b[0;34m(y_true, y_pred, sample_weight, multioutput)\u001b[0m\n\u001b[1;32m    236\u001b[0m     \"\"\"\n\u001b[1;32m    237\u001b[0m     y_type, y_true, y_pred, multioutput = _check_reg_targets(\n\u001b[0;32m--> 238\u001b[0;31m         y_true, y_pred, multioutput)\n\u001b[0m\u001b[1;32m    239\u001b[0m     output_errors = np.average((y_true - y_pred) ** 2, axis=0,\n\u001b[1;32m    240\u001b[0m                                weights=sample_weight)\n",
      "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/sklearn/metrics/regression.py\u001b[0m in \u001b[0;36m_check_reg_targets\u001b[0;34m(y_true, y_pred, multioutput)\u001b[0m\n\u001b[1;32m     75\u001b[0m     \u001b[0mcheck_consistent_length\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_true\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_pred\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     76\u001b[0m     \u001b[0my_true\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcheck_array\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_true\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mensure_2d\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 77\u001b[0;31m     \u001b[0my_pred\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcheck_array\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_pred\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mensure_2d\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     78\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     79\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0my_true\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mndim\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/sklearn/utils/validation.py\u001b[0m in \u001b[0;36mcheck_array\u001b[0;34m(array, accept_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, ensure_min_samples, ensure_min_features, warn_on_dtype, estimator)\u001b[0m\n\u001b[1;32m    451\u001b[0m                              % (array.ndim, estimator_name))\n\u001b[1;32m    452\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mforce_all_finite\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 453\u001b[0;31m             \u001b[0m_assert_all_finite\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    454\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    455\u001b[0m     \u001b[0mshape_repr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_shape_repr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/sklearn/utils/validation.py\u001b[0m in \u001b[0;36m_assert_all_finite\u001b[0;34m(X)\u001b[0m\n\u001b[1;32m     42\u001b[0m             and not np.isfinite(X).all()):\n\u001b[1;32m     43\u001b[0m         raise ValueError(\"Input contains NaN, infinity\"\n\u001b[0;32m---> 44\u001b[0;31m                          \" or a value too large for %r.\" % X.dtype)\n\u001b[0m\u001b[1;32m     45\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     46\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mValueError\u001b[0m: Input contains NaN, infinity or a value too large for dtype('float64')."
     ]
    }
   ],
   "source": [
    "rms = sqrt(mean_squared_error(test.Close, y_hat_avg.Holt_linear))\n",
    "print(rms)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f8e2df926a0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# y_hat_avg = test.copy()\n",
    "# fit1 = ExponentialSmoothing(np.asarray(train['Close']) ,seasonal_periods=7 ,trend='add', seasonal='add',).fit()\n",
    "# y_hat_avg['Holt_Winter'] = fit1.forecast(len(test))\n",
    "\n",
    "y_hat_avg = test.copy()\n",
    "fit1 = ExponentialSmoothing(np.asarray(train['Close']) ,seasonal_periods=60 ,trend='add', seasonal='add',).fit()\n",
    "y_hat_avg['Holt_Winter'] = fit1.forecast(len(test))\n",
    "\n",
    "plt.figure(figsize=(16,8))\n",
    "plt.plot( train['Close'], label='Train')\n",
    "plt.plot(test['Close'], label='Test')\n",
    "plt.plot(y_hat_avg['Holt_Winter'], label='Holt_Winter')\n",
    "plt.legend(loc='best')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "Input contains NaN, infinity or a value too large for dtype('float64').",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-181-71081bbd0019>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mrms\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msqrt\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmean_squared_error\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtest\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mClose\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_hat_avg\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mHolt_Winter\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrms\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/sklearn/metrics/regression.py\u001b[0m in \u001b[0;36mmean_squared_error\u001b[0;34m(y_true, y_pred, sample_weight, multioutput)\u001b[0m\n\u001b[1;32m    236\u001b[0m     \"\"\"\n\u001b[1;32m    237\u001b[0m     y_type, y_true, y_pred, multioutput = _check_reg_targets(\n\u001b[0;32m--> 238\u001b[0;31m         y_true, y_pred, multioutput)\n\u001b[0m\u001b[1;32m    239\u001b[0m     output_errors = np.average((y_true - y_pred) ** 2, axis=0,\n\u001b[1;32m    240\u001b[0m                                weights=sample_weight)\n",
      "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/sklearn/metrics/regression.py\u001b[0m in \u001b[0;36m_check_reg_targets\u001b[0;34m(y_true, y_pred, multioutput)\u001b[0m\n\u001b[1;32m     75\u001b[0m     \u001b[0mcheck_consistent_length\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_true\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_pred\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     76\u001b[0m     \u001b[0my_true\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcheck_array\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_true\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mensure_2d\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 77\u001b[0;31m     \u001b[0my_pred\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcheck_array\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_pred\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mensure_2d\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     78\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     79\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0my_true\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mndim\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/sklearn/utils/validation.py\u001b[0m in \u001b[0;36mcheck_array\u001b[0;34m(array, accept_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, ensure_min_samples, ensure_min_features, warn_on_dtype, estimator)\u001b[0m\n\u001b[1;32m    451\u001b[0m                              % (array.ndim, estimator_name))\n\u001b[1;32m    452\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mforce_all_finite\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 453\u001b[0;31m             \u001b[0m_assert_all_finite\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    454\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    455\u001b[0m     \u001b[0mshape_repr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_shape_repr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/sklearn/utils/validation.py\u001b[0m in \u001b[0;36m_assert_all_finite\u001b[0;34m(X)\u001b[0m\n\u001b[1;32m     42\u001b[0m             and not np.isfinite(X).all()):\n\u001b[1;32m     43\u001b[0m         raise ValueError(\"Input contains NaN, infinity\"\n\u001b[0;32m---> 44\u001b[0;31m                          \" or a value too large for %r.\" % X.dtype)\n\u001b[0m\u001b[1;32m     45\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     46\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mValueError\u001b[0m: Input contains NaN, infinity or a value too large for dtype('float64')."
     ]
    }
   ],
   "source": [
    "rms = sqrt(mean_squared_error(test.Close, y_hat_avg.Holt_Winter))\n",
    "print(rms)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "# # y_hat_avg = test.copy()\n",
    "# # fit1 = sm.tsa.statespace.SARIMAX(train.Close, order=(2, 1, 2),seasonal_order=(0,1,1,7)).fit()\n",
    "# # y_hat_avg['SARIMA'] = fit1.predict(start=\"2017-06-06\", end=\"2017-11-03\", dynamic=True)\n",
    "\n",
    "# y_hat_avg = test.copy()\n",
    "# fit1 = sm.tsa.statespace.SARIMAX(train.Close, order=(2, 1, 4),seasonal_order=(0,1,1,7)).fit()\n",
    "# y_hat_avg['SARIMA'] = fit1.predict(start=\"2017-06-04\", end=\"2017-11-03\", dynamic=True)\n",
    "\n",
    "# # y_hat_avg = test.copy()\n",
    "# # fit1 = sm.tsa.statespace.SARIMAX(train.Close, order=(2, 1, 1)).fit()\n",
    "# # y_hat_avg['SARIMA'] = fit1.predict(start=\"2017-06-06\", end=\"2017-11-03\", dynamic=True)\n",
    "\n",
    "# # y_hat_avg = test.copy()\n",
    "# # fit1 = sm.tsa.statespace.SARIMAX(train.Close, order=(2, 1, 2),seasonal_order=(-3,0,1,3)).fit()\n",
    "# # y_hat_avg['SARIMA'] = fit1.predict(start=\"2017-06-06\", end=\"2017-11-03\", dynamic=True)\n",
    "\n",
    "# # y_hat_avg = test.copy()\n",
    "# # fit1 = sm.tsa.statespace.SARIMAX(train.Close, order=(2, 1, 4),seasonal_order=(0,1,1,7)).fit(disp=-1)\n",
    "# # y_hat_avg['SARIMA'] = fit1.predict(start=\"2017-06-06\", end=\"2017-11-03\", dynamic=True)\n",
    "\n",
    "# plt.figure(figsize=(16,8))\n",
    "# plt.plot( train['Close'], label='Train')\n",
    "# plt.plot(test['Close'], label='Test')\n",
    "# plt.plot(y_hat_avg['SARIMA'], label='SARIMA',linewidth=3)\n",
    "# plt.legend(loc='best')\n",
    "# plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "# rms = sqrt(mean_squared_error(test.Close, y_hat_avg.SARIMA))\n",
    "# print(rms)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "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": "3.6.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}