Bitcoin_dataframe.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "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": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 2,
     "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()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": true
   },
   "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": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Date\n",
       "2011-12-31    4.472579\n",
       "2012-01-01    4.680778\n",
       "2012-01-02    5.000000\n",
       "2012-01-03    5.145917\n",
       "2012-01-04    5.195443\n",
       "Name: Weighted_Price, dtype: float64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "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": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# convert the date column into a time series with daily frequency\n",
    "\n",
    "df['Date'] = pd.to_datetime(df['Date'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "indexed_df = df.set_index('Date')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "ts = indexed_df['Close']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Date\n",
       "2011-12-31    4.39\n",
       "2011-12-31    4.39\n",
       "2011-12-31    4.39\n",
       "2011-12-31    4.39\n",
       "2011-12-31    4.39\n",
       "Name: Close, dtype: float64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7fb11ce5df98>]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fb11ce3cb00>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# visualize the time series to see how Bitcoin changes over time\n",
    "\n",
    "plt.plot(ts)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# resamply by week\n",
    "\n",
    "ts_week = ts.resample('D').mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2011-12-31', '2012-01-01', '2012-01-02', '2012-01-03',\n",
       "               '2012-01-04', '2012-01-05', '2012-01-06', '2012-01-07',\n",
       "               '2012-01-08', '2012-01-09',\n",
       "               ...\n",
       "               '2017-12-30', '2017-12-31', '2018-01-01', '2018-01-02',\n",
       "               '2018-01-03', '2018-01-04', '2018-01-05', '2018-01-06',\n",
       "               '2018-01-07', '2018-01-08'],\n",
       "              dtype='datetime64[ns]', name='Date', length=2201, freq='D')"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts_week"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "new_chau= pd.DataFrame(list(ts_week), index = ts_week.index, columns = [\"close\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "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",
       "    <tr>\n",
       "      <th>2012-01-05</th>\n",
       "      <td>6.134917</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-06</th>\n",
       "      <td>6.551583</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-07</th>\n",
       "      <td>6.192222</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-08</th>\n",
       "      <td>6.842292</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-09</th>\n",
       "      <td>6.685375</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-10</th>\n",
       "      <td>6.558201</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-11</th>\n",
       "      <td>7.007750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-12</th>\n",
       "      <td>6.877167</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-13</th>\n",
       "      <td>6.768799</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-14</th>\n",
       "      <td>6.393889</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-15</th>\n",
       "      <td>6.781076</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-16</th>\n",
       "      <td>7.061111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-17</th>\n",
       "      <td>6.910347</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-18</th>\n",
       "      <td>6.553972</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-19</th>\n",
       "      <td>6.323160</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-20</th>\n",
       "      <td>6.655063</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-21</th>\n",
       "      <td>6.887750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-22</th>\n",
       "      <td>6.614472</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-23</th>\n",
       "      <td>6.543410</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-24</th>\n",
       "      <td>6.506604</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-25</th>\n",
       "      <td>6.511646</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-26</th>\n",
       "      <td>6.258729</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-27</th>\n",
       "      <td>5.808681</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-28</th>\n",
       "      <td>5.074361</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-29</th>\n",
       "      <td>5.650951</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-12-10</th>\n",
       "      <td>14134.664792</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-12-11</th>\n",
       "      <td>16202.500125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-12-12</th>\n",
       "      <td>16730.929660</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-12-13</th>\n",
       "      <td>16540.327313</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-12-14</th>\n",
       "      <td>16343.036833</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-12-15</th>\n",
       "      <td>17365.627924</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-12-16</th>\n",
       "      <td>18176.730139</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-12-17</th>\n",
       "      <td>19190.340632</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-12-18</th>\n",
       "      <td>18720.013500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-12-19</th>\n",
       "      <td>18320.416264</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-12-20</th>\n",
       "      <td>16834.521375</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-12-21</th>\n",
       "      <td>16221.424986</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-12-22</th>\n",
       "      <td>13761.145826</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-12-23</th>\n",
       "      <td>14768.556035</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-12-24</th>\n",
       "      <td>13656.086035</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-12-25</th>\n",
       "      <td>13966.067694</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-12-26</th>\n",
       "      <td>15243.615264</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-12-27</th>\n",
       "      <td>15581.444701</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-12-28</th>\n",
       "      <td>14248.632049</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-12-29</th>\n",
       "      <td>14534.974931</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-12-30</th>\n",
       "      <td>13212.859868</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-12-31</th>\n",
       "      <td>13339.221194</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-01</th>\n",
       "      <td>13425.415125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-02</th>\n",
       "      <td>13879.358896</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-03</th>\n",
       "      <td>14963.699340</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-04</th>\n",
       "      <td>14818.780319</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-05</th>\n",
       "      <td>15792.452507</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-06</th>\n",
       "      <td>16666.922951</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-07</th>\n",
       "      <td>16517.014097</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-08</th>\n",
       "      <td>16166.780000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2201 rows × 1 columns</p>\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\n",
       "2012-01-05      6.134917\n",
       "2012-01-06      6.551583\n",
       "2012-01-07      6.192222\n",
       "2012-01-08      6.842292\n",
       "2012-01-09      6.685375\n",
       "2012-01-10      6.558201\n",
       "2012-01-11      7.007750\n",
       "2012-01-12      6.877167\n",
       "2012-01-13      6.768799\n",
       "2012-01-14      6.393889\n",
       "2012-01-15      6.781076\n",
       "2012-01-16      7.061111\n",
       "2012-01-17      6.910347\n",
       "2012-01-18      6.553972\n",
       "2012-01-19      6.323160\n",
       "2012-01-20      6.655063\n",
       "2012-01-21      6.887750\n",
       "2012-01-22      6.614472\n",
       "2012-01-23      6.543410\n",
       "2012-01-24      6.506604\n",
       "2012-01-25      6.511646\n",
       "2012-01-26      6.258729\n",
       "2012-01-27      5.808681\n",
       "2012-01-28      5.074361\n",
       "2012-01-29      5.650951\n",
       "...                  ...\n",
       "2017-12-10  14134.664792\n",
       "2017-12-11  16202.500125\n",
       "2017-12-12  16730.929660\n",
       "2017-12-13  16540.327313\n",
       "2017-12-14  16343.036833\n",
       "2017-12-15  17365.627924\n",
       "2017-12-16  18176.730139\n",
       "2017-12-17  19190.340632\n",
       "2017-12-18  18720.013500\n",
       "2017-12-19  18320.416264\n",
       "2017-12-20  16834.521375\n",
       "2017-12-21  16221.424986\n",
       "2017-12-22  13761.145826\n",
       "2017-12-23  14768.556035\n",
       "2017-12-24  13656.086035\n",
       "2017-12-25  13966.067694\n",
       "2017-12-26  15243.615264\n",
       "2017-12-27  15581.444701\n",
       "2017-12-28  14248.632049\n",
       "2017-12-29  14534.974931\n",
       "2017-12-30  13212.859868\n",
       "2017-12-31  13339.221194\n",
       "2018-01-01  13425.415125\n",
       "2018-01-02  13879.358896\n",
       "2018-01-03  14963.699340\n",
       "2018-01-04  14818.780319\n",
       "2018-01-05  15792.452507\n",
       "2018-01-06  16666.922951\n",
       "2018-01-07  16517.014097\n",
       "2018-01-08  16166.780000\n",
       "\n",
       "[2201 rows x 1 columns]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_chau"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7fb110317ba8>]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fb110352cc0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(ts_week)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# check for stationarity\n",
    "\n",
    "def test_stationarity(timeseries):\n",
    "    \n",
    "    #Determing rolling statistics\n",
    "    rolmean = timeseries.rolling(window=52,center=False).mean() \n",
    "    rolstd = timeseries.rolling(window=52,center=False).std()\n",
    "\n",
    "    #Plot rolling statistics:\n",
    "    orig = plt.plot(timeseries, color='blue',label='Original')\n",
    "    mean = plt.plot(rolmean, color='red', label='Rolling Mean')\n",
    "    std = plt.plot(rolstd, color='black', label = 'Rolling Std')\n",
    "    plt.legend(loc='best')\n",
    "    plt.title('Rolling Mean & Standard Deviation')\n",
    "    plt.show(block=False)\n",
    "    \n",
    "    #Perform Dickey-Fuller test:\n",
    "    print('Results of Dickey-Fuller Test:')\n",
    "    dftest = adfuller(timeseries, 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": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe8050b7588>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Results of Dickey-Fuller Test:\n",
      "Test Statistic                   3.544263\n",
      "p-value                          1.000000\n",
      "#Lags Used                      15.000000\n",
      "Number of Observations Used    300.000000\n",
      "Critical Value (1%)             -3.452337\n",
      "Critical Value (5%)             -2.871223\n",
      "Critical Value (10%)            -2.571929\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "test_stationarity(ts_week)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Because the test statistic is more than the 5% critical value and the p-value is larger than 0.05, the moving average is not constant over time and the null hypothesis of the Dickey-Fuller test cannot be rejected. This shows that the weekly time series is not stationary.\n",
    "Before you can apply ARIMA models for forecasting, you need to transform this time series into a stationary time series."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# apply a non linear log transform\n",
    "\n",
    "ts_week_log = np.log(ts_week)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe8050a9400>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Results of Dickey-Fuller Test:\n",
      "Test Statistic                  -0.202453\n",
      "p-value                          0.938229\n",
      "#Lags Used                       1.000000\n",
      "Number of Observations Used    314.000000\n",
      "Critical Value (1%)             -3.451349\n",
      "Critical Value (5%)             -2.870789\n",
      "Critical Value (10%)            -2.571698\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "test_stationarity(ts_week_log)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The Dickey-Fuller test results confirm that the series is still non-stationary. Again the test statistic is larger than the 5% critical value and the p-value larger than 0.05."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe804f0bd68>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# remove trend and seasonality with decomposition\n",
    "\n",
    "decomposition = seasonal_decompose(ts_week)\n",
    "\n",
    "trend = decomposition.trend\n",
    "seasonal = decomposition.seasonal\n",
    "residual = decomposition.resid\n",
    "\n",
    "plt.subplot(411)\n",
    "plt.plot(ts_week_log[-80:], label='Original')\n",
    "plt.legend(loc='best')\n",
    "plt.subplot(412)\n",
    "plt.plot(trend[-80:], label='Trend')\n",
    "plt.legend(loc='best')\n",
    "plt.subplot(413)\n",
    "plt.plot(seasonal[-80:],label='Seasonality')\n",
    "plt.legend(loc='best')\n",
    "plt.subplot(414)\n",
    "plt.plot(residual[-80:], label='Residuals')\n",
    "plt.legend(loc='best')\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7fe844990780>]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe804fb0358>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# remove trend and seasonality with differencing\n",
    "\n",
    "ts_week_log_diff = ts_week_log - ts_week_log.shift()\n",
    "plt.plot(ts_week_log_diff)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe8449a4780>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Results of Dickey-Fuller Test:\n",
      "Test Statistic                -1.177937e+01\n",
      "p-value                        1.043719e-21\n",
      "#Lags Used                     0.000000e+00\n",
      "Number of Observations Used    3.140000e+02\n",
      "Critical Value (1%)           -3.451349e+00\n",
      "Critical Value (5%)           -2.870789e+00\n",
      "Critical Value (10%)          -2.571698e+00\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "ts_week_log_diff.dropna(inplace=True)\n",
    "test_stationarity(ts_week_log_diff)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Printing Predicted vs Expected Values...\n",
      "\n",
      "\n",
      "0.9940155964043974\n",
      "\n",
      "\n",
      "Printing Mean Squared Error of Predictions...\n",
      "Test MSE: 0.006440\n"
     ]
    }
   ],
   "source": [
    "size = int(len(ts_week_log)*(0.7))\n",
    "train, test = ts_week_log[0:size], ts_week_log[size:len(ts_week_log)]\n",
    "history = [x for x in train]\n",
    "predictions = list()\n",
    "\n",
    "print('Printing Predicted vs Expected Values...')\n",
    "print('\\n')\n",
    "for t in range(len(test)):\n",
    "    model = ARIMA(history, order=(2,1,1))\n",
    "    model_fit = model.fit(disp=0)\n",
    "    output = model_fit.forecast()\n",
    "    yhat = output[0]\n",
    "    predictions.append(float(yhat))\n",
    "    obs = test[t]\n",
    "    history.append(obs)\n",
    "    #print('predicted=%f, expected=%f' % (np.exp(yhat), np.exp(obs)))\n",
    "\n",
    "error = mean_squared_error(test, predictions)\n",
    "r2 = r2_score(test, predictions)\n",
    "print(r2)\n",
    "\n",
    "print('\\n')\n",
    "print('Printing Mean Squared Error of Predictions...')\n",
    "print('Test MSE: %.6f' % error)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "predictions_series = pd.Series(predictions, index = test.index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe844552240>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "ax.set(title='Spot Exchange Rate, Euro into USD', xlabel='Date', ylabel='Euro into USD')\n",
    "ax.plot(ts_week[-60:], 'o', label='observed')\n",
    "ax.plot(np.exp(predictions_series), 'g', label='rolling one-step out-of-sample forecast')\n",
    "legend = ax.legend(loc='upper left')\n",
    "legend.get_frame().set_facecolor('w')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9940155964043974"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r2"
   ]
  },
  {
   "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": "3.6.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}