Salinee_Kingbaisomboon-L08-TimeseriesOpEd.ipynb.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Lesson 8 - Time Series Analysis \n",
    "## Author - Salinee Kingbaisomboon\n",
    "### UW NetID: 1950831"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Instructions\n",
    "\n",
    "Using the California dairy production dataset, perform time series analysis on the data for one of Cottage Cheese Production, Milk Production, or Ice Cream Production (your choice) to answer the following questions:\n",
    "\n",
    "1. Is this time series stationary?\n",
    "2. Is there a significant seasonal component?\n",
    "3. For the residual from the STL decomposition of the time series, what is the order of the ARMA(p,q) process that best fits?\n",
    "\n",
    "Then forecast production for 12 months and report both numeric values and plot the confidence intervals.\n",
    "\n",
    "1. Are the confidence intervals reasonably small compared to the forecast means?\n",
    "2. How do the confidence intervals behave as time moves to the future?\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Note\n",
    "Please clearly outline the results with markdown text. Be sure your discussion includes narrative for your figures and tables so it is clear to the reader the importance of each bit of evidence."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Dataset location\n",
    "fileName = \"https://library.startlearninglabs.uw.edu/DATASCI410/Datasets/CADairyProduction.csv\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load necessary libraries\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import scipy.stats as ss\n",
    "from scipy.stats import zscore\n",
    "import sklearn.linear_model as lm\n",
    "from statsmodels.tsa.arima_model import ARIMA\n",
    "from statsmodels.tsa.arima_model import ARIMAResults\n",
    "from statsmodels.tsa.stattools import adfuller\n",
    "import statsmodels.graphics.tsaplots as splt\n",
    "import statsmodels.tsa.seasonal as sts\n",
    "\n",
    "import warnings\n",
    "\n",
    "warnings.filterwarnings(\"ignore\") # To suppress warning\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Year</th>\n",
       "      <th>Month</th>\n",
       "      <th>Cotagecheese.Prod</th>\n",
       "      <th>Icecream.Prod</th>\n",
       "      <th>Milk.Prod</th>\n",
       "      <th>N.CA.Fat.Price</th>\n",
       "      <th>Month.Count</th>\n",
       "      <th>monthNumSqred</th>\n",
       "      <th>monthNumCubed</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1995</td>\n",
       "      <td>Jan</td>\n",
       "      <td>4.370</td>\n",
       "      <td>51.595</td>\n",
       "      <td>2.112</td>\n",
       "      <td>0.9803</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1995</td>\n",
       "      <td>Feb</td>\n",
       "      <td>3.695</td>\n",
       "      <td>56.086</td>\n",
       "      <td>1.932</td>\n",
       "      <td>0.8924</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1995</td>\n",
       "      <td>Mar</td>\n",
       "      <td>4.538</td>\n",
       "      <td>68.453</td>\n",
       "      <td>2.162</td>\n",
       "      <td>0.8924</td>\n",
       "      <td>3</td>\n",
       "      <td>9</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1995</td>\n",
       "      <td>Apr</td>\n",
       "      <td>4.280</td>\n",
       "      <td>65.722</td>\n",
       "      <td>2.130</td>\n",
       "      <td>0.8967</td>\n",
       "      <td>4</td>\n",
       "      <td>16</td>\n",
       "      <td>64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1995</td>\n",
       "      <td>May</td>\n",
       "      <td>4.470</td>\n",
       "      <td>73.730</td>\n",
       "      <td>2.227</td>\n",
       "      <td>0.8967</td>\n",
       "      <td>5</td>\n",
       "      <td>25</td>\n",
       "      <td>125</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Year Month  Cotagecheese.Prod  Icecream.Prod  Milk.Prod  N.CA.Fat.Price  \\\n",
       "0  1995   Jan              4.370         51.595      2.112          0.9803   \n",
       "1  1995   Feb              3.695         56.086      1.932          0.8924   \n",
       "2  1995   Mar              4.538         68.453      2.162          0.8924   \n",
       "3  1995   Apr              4.280         65.722      2.130          0.8967   \n",
       "4  1995   May              4.470         73.730      2.227          0.8967   \n",
       "\n",
       "   Month.Count  monthNumSqred  monthNumCubed  \n",
       "0            1              1              1  \n",
       "1            2              4              8  \n",
       "2            3              9             27  \n",
       "3            4             16             64  \n",
       "4            5             25            125  "
      ]
     },
     "execution_count": 156,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Read the .csv file with the pandas read_csv method\n",
    "df = pd.read_csv(fileName)\n",
    "\n",
    "# View the first 5 rows of this dataframe (Start from Jan 1995)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Year</th>\n",
       "      <th>Month</th>\n",
       "      <th>Cotagecheese.Prod</th>\n",
       "      <th>Icecream.Prod</th>\n",
       "      <th>Milk.Prod</th>\n",
       "      <th>N.CA.Fat.Price</th>\n",
       "      <th>Month.Count</th>\n",
       "      <th>monthNumSqred</th>\n",
       "      <th>monthNumCubed</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>223</th>\n",
       "      <td>2013</td>\n",
       "      <td>Aug</td>\n",
       "      <td>2.169</td>\n",
       "      <td>78.382</td>\n",
       "      <td>3.400</td>\n",
       "      <td>1.6273</td>\n",
       "      <td>224</td>\n",
       "      <td>50176</td>\n",
       "      <td>11239424</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>224</th>\n",
       "      <td>2013</td>\n",
       "      <td>Sep</td>\n",
       "      <td>2.193</td>\n",
       "      <td>74.871</td>\n",
       "      <td>3.197</td>\n",
       "      <td>1.5519</td>\n",
       "      <td>225</td>\n",
       "      <td>50625</td>\n",
       "      <td>11390625</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>225</th>\n",
       "      <td>2013</td>\n",
       "      <td>Oct</td>\n",
       "      <td>2.284</td>\n",
       "      <td>70.011</td>\n",
       "      <td>3.338</td>\n",
       "      <td>1.5566</td>\n",
       "      <td>226</td>\n",
       "      <td>51076</td>\n",
       "      <td>11543176</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>226</th>\n",
       "      <td>2013</td>\n",
       "      <td>Nov</td>\n",
       "      <td>1.865</td>\n",
       "      <td>61.394</td>\n",
       "      <td>3.275</td>\n",
       "      <td>1.7537</td>\n",
       "      <td>227</td>\n",
       "      <td>51529</td>\n",
       "      <td>11697083</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>227</th>\n",
       "      <td>2013</td>\n",
       "      <td>Dec</td>\n",
       "      <td>2.345</td>\n",
       "      <td>53.653</td>\n",
       "      <td>3.473</td>\n",
       "      <td>1.6747</td>\n",
       "      <td>228</td>\n",
       "      <td>51984</td>\n",
       "      <td>11852352</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Year Month  Cotagecheese.Prod  Icecream.Prod  Milk.Prod  N.CA.Fat.Price  \\\n",
       "223  2013   Aug              2.169         78.382      3.400          1.6273   \n",
       "224  2013   Sep              2.193         74.871      3.197          1.5519   \n",
       "225  2013   Oct              2.284         70.011      3.338          1.5566   \n",
       "226  2013   Nov              1.865         61.394      3.275          1.7537   \n",
       "227  2013   Dec              2.345         53.653      3.473          1.6747   \n",
       "\n",
       "     Month.Count  monthNumSqred  monthNumCubed  \n",
       "223          224          50176       11239424  \n",
       "224          225          50625       11390625  \n",
       "225          226          51076       11543176  \n",
       "226          227          51529       11697083  \n",
       "227          228          51984       11852352  "
      ]
     },
     "execution_count": 157,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# View the last 5 rows of this dataframe (End at Dec 2013)\n",
    "df.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>Year</th>\n",
       "      <th>Month</th>\n",
       "      <th>Cotagecheese.Prod</th>\n",
       "      <th>Icecream.Prod</th>\n",
       "      <th>Milk.Prod</th>\n",
       "      <th>N.CA.Fat.Price</th>\n",
       "      <th>Month.Count</th>\n",
       "      <th>monthNumSqred</th>\n",
       "      <th>monthNumCubed</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1995-01-31</th>\n",
       "      <td>1995</td>\n",
       "      <td>Jan</td>\n",
       "      <td>4.370</td>\n",
       "      <td>51.595</td>\n",
       "      <td>2.112</td>\n",
       "      <td>0.9803</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1995-02-28</th>\n",
       "      <td>1995</td>\n",
       "      <td>Feb</td>\n",
       "      <td>3.695</td>\n",
       "      <td>56.086</td>\n",
       "      <td>1.932</td>\n",
       "      <td>0.8924</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1995-03-31</th>\n",
       "      <td>1995</td>\n",
       "      <td>Mar</td>\n",
       "      <td>4.538</td>\n",
       "      <td>68.453</td>\n",
       "      <td>2.162</td>\n",
       "      <td>0.8924</td>\n",
       "      <td>3</td>\n",
       "      <td>9</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1995-04-30</th>\n",
       "      <td>1995</td>\n",
       "      <td>Apr</td>\n",
       "      <td>4.280</td>\n",
       "      <td>65.722</td>\n",
       "      <td>2.130</td>\n",
       "      <td>0.8967</td>\n",
       "      <td>4</td>\n",
       "      <td>16</td>\n",
       "      <td>64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1995-05-31</th>\n",
       "      <td>1995</td>\n",
       "      <td>May</td>\n",
       "      <td>4.470</td>\n",
       "      <td>73.730</td>\n",
       "      <td>2.227</td>\n",
       "      <td>0.8967</td>\n",
       "      <td>5</td>\n",
       "      <td>25</td>\n",
       "      <td>125</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Year Month  Cotagecheese.Prod  Icecream.Prod  Milk.Prod  \\\n",
       "1995-01-31  1995   Jan              4.370         51.595      2.112   \n",
       "1995-02-28  1995   Feb              3.695         56.086      1.932   \n",
       "1995-03-31  1995   Mar              4.538         68.453      2.162   \n",
       "1995-04-30  1995   Apr              4.280         65.722      2.130   \n",
       "1995-05-31  1995   May              4.470         73.730      2.227   \n",
       "\n",
       "            N.CA.Fat.Price  Month.Count  monthNumSqred  monthNumCubed  \n",
       "1995-01-31          0.9803            1              1              1  \n",
       "1995-02-28          0.8924            2              4              8  \n",
       "1995-03-31          0.8924            3              9             27  \n",
       "1995-04-30          0.8967            4             16             64  \n",
       "1995-05-31          0.8967            5             25            125  "
      ]
     },
     "execution_count": 158,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Adds a new set of index values to the Pandas series from Jan 1, 1995 to Dec 31, 2013 (Frequenly = Month)\n",
    "df.index = pd.date_range(start = '1-1-1995', end = '12-31-2013', freq = 'M')\n",
    "# View the first 5 rows of this dataframe\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Year</th>\n",
       "      <th>Month</th>\n",
       "      <th>Cotagecheese.Prod</th>\n",
       "      <th>Icecream.Prod</th>\n",
       "      <th>Milk.Prod</th>\n",
       "      <th>N.CA.Fat.Price</th>\n",
       "      <th>Month.Count</th>\n",
       "      <th>monthNumSqred</th>\n",
       "      <th>monthNumCubed</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2013-08-31</th>\n",
       "      <td>2013</td>\n",
       "      <td>Aug</td>\n",
       "      <td>2.169</td>\n",
       "      <td>78.382</td>\n",
       "      <td>3.400</td>\n",
       "      <td>1.6273</td>\n",
       "      <td>224</td>\n",
       "      <td>50176</td>\n",
       "      <td>11239424</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-09-30</th>\n",
       "      <td>2013</td>\n",
       "      <td>Sep</td>\n",
       "      <td>2.193</td>\n",
       "      <td>74.871</td>\n",
       "      <td>3.197</td>\n",
       "      <td>1.5519</td>\n",
       "      <td>225</td>\n",
       "      <td>50625</td>\n",
       "      <td>11390625</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-10-31</th>\n",
       "      <td>2013</td>\n",
       "      <td>Oct</td>\n",
       "      <td>2.284</td>\n",
       "      <td>70.011</td>\n",
       "      <td>3.338</td>\n",
       "      <td>1.5566</td>\n",
       "      <td>226</td>\n",
       "      <td>51076</td>\n",
       "      <td>11543176</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-11-30</th>\n",
       "      <td>2013</td>\n",
       "      <td>Nov</td>\n",
       "      <td>1.865</td>\n",
       "      <td>61.394</td>\n",
       "      <td>3.275</td>\n",
       "      <td>1.7537</td>\n",
       "      <td>227</td>\n",
       "      <td>51529</td>\n",
       "      <td>11697083</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-12-31</th>\n",
       "      <td>2013</td>\n",
       "      <td>Dec</td>\n",
       "      <td>2.345</td>\n",
       "      <td>53.653</td>\n",
       "      <td>3.473</td>\n",
       "      <td>1.6747</td>\n",
       "      <td>228</td>\n",
       "      <td>51984</td>\n",
       "      <td>11852352</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Year Month  Cotagecheese.Prod  Icecream.Prod  Milk.Prod  \\\n",
       "2013-08-31  2013   Aug              2.169         78.382      3.400   \n",
       "2013-09-30  2013   Sep              2.193         74.871      3.197   \n",
       "2013-10-31  2013   Oct              2.284         70.011      3.338   \n",
       "2013-11-30  2013   Nov              1.865         61.394      3.275   \n",
       "2013-12-31  2013   Dec              2.345         53.653      3.473   \n",
       "\n",
       "            N.CA.Fat.Price  Month.Count  monthNumSqred  monthNumCubed  \n",
       "2013-08-31          1.6273          224          50176       11239424  \n",
       "2013-09-30          1.5519          225          50625       11390625  \n",
       "2013-10-31          1.5566          226          51076       11543176  \n",
       "2013-11-30          1.7537          227          51529       11697083  \n",
       "2013-12-31          1.6747          228          51984       11852352  "
      ]
     },
     "execution_count": 159,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# View the last 5 rows of this dataframe\n",
    "df.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Declare Functions used in this assignment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Function to plot distribution of the time series values\n",
    "def dist_ts(ts, lab = '', bins = 40):\n",
    "    ## Setup a figure with two subplots side by side\n",
    "    f, (ax1, ax2) = plt.subplots(1, 2, figsize=(7, 3))\n",
    "    ## Plot the histogram with labels\n",
    "    ts.hist(ax = ax1, bins = bins, alpha = 0.5)\n",
    "    ax1.set_xlabel('Value')\n",
    "    ax1.set_ylabel('Frequency')\n",
    "    ax1.set_title('Histogram of ' + lab)\n",
    "    ## Plot the q-q plot on the other axes\n",
    "    ss.probplot(ts, plot = ax2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Function to decomposition of Time Series with monthly seasonal component by default \n",
    "def decomp_ts(ts, freq = 'M', model = 'additive'):\n",
    "    res = sts.seasonal_decompose(ts, model = model)\n",
    "    res.plot()\n",
    "    return(pd.DataFrame({'resid': res.resid, # residuals\n",
    "                         'trend': res.trend, # trend\n",
    "                         'seasonal': res.seasonal}, # seasonal\n",
    "                       index = ts.index) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Function to define ARIMA model from statsmodels package\n",
    "def model_ARIMA(ts, order):\n",
    "    model = ARIMA(ts, order = order)\n",
    "    # mle = maximum likelihood estiator\n",
    "    # nc = we assume the trend has been removed and no displacement\n",
    "    model_fit = model.fit(disp=0, method='mle', trend='nc')\n",
    "    print(model_fit.summary())\n",
    "    return(model_fit)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Function to calculate BIC\n",
    "def model_ARIMA_2(ts, order):   \n",
    "    model = ARIMA(ts, order = order)\n",
    "    model_fit = model.fit(disp=0, method='mle', trend='nc')\n",
    "    BIC = ARIMAResults.bic(model_fit)\n",
    "    print('Testing model of order: ' + str(order) + ' with BIC = ' + str(BIC))\n",
    "    return(BIC, order, model_fit)\n",
    "\n",
    "# Function to find the best BIC\n",
    "def step_ARIMA(resid, p_max, d_max, q_max):\n",
    "    best_BIC = 9999999999999999.0\n",
    "    for p in range(p_max + 1):\n",
    "        for d in range(d_max + 1):\n",
    "            for q in range(q_max + 1):\n",
    "                if(p > 0 or q > 0): # you can't have 0,1,0 or 0,1,1 it's not stable\n",
    "                    try: # some model not gonna be solvable (root we can't find), that's why we have try-cache\n",
    "                        order = (p, d, q)\n",
    "                        BIC, order, model = model_ARIMA_2(resid, order)\n",
    "                        if(BIC < best_BIC):\n",
    "                            best_model = model\n",
    "                            best_BIC = BIC\n",
    "                            best_order = order\n",
    "                    except: \n",
    "                        pass        \n",
    "    return(best_BIC, best_order, best_model) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Function to plot predict value in green with 95% Confident Intervals in yellow shade  \n",
    "def plot_forecast(df, col):\n",
    "    fig = plt.figure(figsize=(10, 10)) # set plot area\n",
    "    ax = fig.gca() # define axis\n",
    "    \n",
    "    # http://sphweb.bumc.bu.edu/otlt/MPH-Modules/BS/BS704_Confidence_Intervals/BS704_Confidence_Intervals_print.html\n",
    "    # Z value for 95% confidence is Z=1.96\n",
    "    lower_ci = df.loc[:, 'scores'] - 1.96*df.loc[:, 'resids']\n",
    "    upper_ci = df.loc[:, 'scores'] + 1.96*df.loc[:, 'resids']\n",
    "    plt.plot(df.loc[:, 'scores'], color = 'g') # plot predict value\n",
    "    plt.fill_between(df.loc[:, 'scores'].index, lower_ci, upper_ci, color='y', alpha=.5) # plot 95% confident interval\n",
    "\n",
    "    ax.set_title('Predict (green) ' + col + ' with 95% Confident Intervals (yellow shade)') # Give the plot a main title\n",
    "    ax.set_xlabel('Date') # Set text for the x axis\n",
    "    ax.set_ylabel(col)# Set text for y axis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1. Is this time series stationary?\n",
    "\n",
    "I choose to perform time series analysis on **Ice Cream Production** in this assignment."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'Date')"
      ]
     },
     "execution_count": 165,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the Icecream Product data\n",
    "df['Icecream.Prod'].plot()\n",
    "plt.title('A California Icecream Production time series')\n",
    "plt.ylabel('Value')\n",
    "plt.xlabel('Date')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "execution_count": 166,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "splt.plot_acf(df['Icecream.Prod'], lags = 40) # Plot the Autocorrelation\n",
    "splt.plot_pacf(df['Icecream.Prod'], lags = 40) # Plot the Partial Autocorrelation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x216 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the distribution of Icecream Product data\n",
    "dist_ts(df['Icecream.Prod'], 'Icecream')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***\n",
    "#### Examine the results:\n",
    "1. **From Autocorrelation Plot:** it has has continued periodicity.\n",
    "2. **From Partial Autocorrelation Plot:** it has many significant values aside from 0.\n",
    "3. **From the Histogram of Icecream & Q-Q-Plot:** both does show that this data does not have normal distribution.\n",
    "\n",
    "Based from all those information, we can conclude that this icecream product time series is **non-stationary**.\n",
    "***"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2. Is there a significant seasonal component?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Generate STL Decomposition model\n",
    "decomp = decomp_ts(df['Icecream.Prod'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***\n",
    "#### Examine the results:\n",
    "Based on the plots above, on the **Seasonal**, we do see the periodic pattern of this icecream product data. Therefore, there is a **significant seasonal component**.\n",
    "***"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 3. For the residual from the STL decomposition of the time series what is the order of the ARMA(p,q) process that best fits?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Testing model of order: (0, 0, 1) with BIC = 1033.948397930495\n",
      "Testing model of order: (0, 0, 2) with BIC = 1015.4096856995327\n",
      "Testing model of order: (0, 0, 3) with BIC = 1034.858687921396\n",
      "Testing model of order: (0, 0, 4) with BIC = 984.8590571118594\n",
      "Testing model of order: (1, 0, 0) with BIC = 1034.1844707639357\n",
      "Testing model of order: (1, 0, 1) with BIC = 1008.9611862654511\n",
      "Testing model of order: (1, 0, 2) with BIC = 1001.0538950830589\n",
      "Testing model of order: (1, 0, 3) with BIC = 1006.2497713727558\n",
      "Testing model of order: (2, 0, 0) with BIC = 1038.491570599305\n",
      "Testing model of order: (2, 0, 1) with BIC = 998.1642671288267\n",
      "Testing model of order: (2, 0, 2) with BIC = 999.8422042595922\n",
      "Testing model of order: (2, 0, 3) with BIC = 1003.9167155383927\n",
      "Testing model of order: (3, 0, 0) with BIC = 1042.6228742699202\n",
      "Testing model of order: (3, 0, 1) with BIC = 996.8789468581685\n",
      "Testing model of order: (3, 0, 2) with BIC = 1001.4617859913453\n",
      "Testing model of order: (3, 0, 3) with BIC = 984.5822219934677\n",
      "Testing model of order: (4, 0, 0) with BIC = 1016.7230253370001\n",
      "Testing model of order: (4, 0, 1) with BIC = 964.165681610406\n",
      "Testing model of order: (4, 0, 2) with BIC = 969.4068804514807\n",
      "Testing model of order: (4, 0, 3) with BIC = 971.9932421886813\n",
      "Testing model of order: (4, 0, 4) with BIC = 971.0871791990635\n",
      "***************************************\n",
      "Best model with BIC = 964.165681610406 and with order (4, 0, 1)\n"
     ]
    }
   ],
   "source": [
    "# Compute p and q value for the best ARMA model from the STL decomposition model from above\n",
    "BIC, order, model = step_ARIMA(decomp.resid[6:-6], 4, 0, 4)    \n",
    "print('***************************************')\n",
    "print('Best model with BIC = ' + str(BIC) + ' and with order '+ str(order))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                              ARMA Model Results                              \n",
      "==============================================================================\n",
      "Dep. Variable:                  resid   No. Observations:                  216\n",
      "Model:                     ARMA(4, 1)   Log Likelihood                -465.957\n",
      "Method:                           mle   S.D. of innovations              2.069\n",
      "Date:                Mon, 13 Jan 2020   AIC                            943.914\n",
      "Time:                        23:02:39   BIC                            964.166\n",
      "Sample:                    07-31-1995   HQIC                           952.096\n",
      "                         - 06-30-2013                                         \n",
      "===============================================================================\n",
      "                  coef    std err          z      P>|z|      [0.025      0.975]\n",
      "-------------------------------------------------------------------------------\n",
      "ar.L1.resid     0.7519      0.063     11.984      0.000       0.629       0.875\n",
      "ar.L2.resid    -0.1594      0.080     -1.982      0.049      -0.317      -0.002\n",
      "ar.L3.resid     0.1596      0.080      1.989      0.048       0.002       0.317\n",
      "ar.L4.resid    -0.4059      0.063     -6.491      0.000      -0.529      -0.283\n",
      "ma.L1.resid    -0.9923      0.022    -44.720      0.000      -1.036      -0.949\n",
      "                                    Roots                                    \n",
      "=============================================================================\n",
      "                  Real          Imaginary           Modulus         Frequency\n",
      "-----------------------------------------------------------------------------\n",
      "AR.1            0.9448           -0.5980j            1.1181           -0.0898\n",
      "AR.2            0.9448           +0.5980j            1.1181            0.0898\n",
      "AR.3           -0.7483           -1.1876j            1.4037           -0.3395\n",
      "AR.4           -0.7483           +1.1876j            1.4037            0.3395\n",
      "MA.1            1.0078           +0.0000j            1.0078            0.0000\n",
      "-----------------------------------------------------------------------------\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<statsmodels.tsa.arima_model.ARMAResultsWrapper at 0x21cc790b108>"
      ]
     },
     "execution_count": 170,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# We founc the best order which is ARMA(4,1)\n",
    "model_ARIMA(decomp.resid[6:-6], (4,0,1))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***\n",
    "#### Examine the results:\n",
    "From the above calculation, we found the best order of the ARMA model which is **(4,1)** where **p = 4** and **q = 1 **.\n",
    "***"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 4. Then forecast production for 12 months and report both numeric values and plot the confidence intervals"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        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>Year</th>\n",
       "      <th>Month</th>\n",
       "      <th>Cotagecheese.Prod</th>\n",
       "      <th>Icecream.Prod</th>\n",
       "      <th>Milk.Prod</th>\n",
       "      <th>N.CA.Fat.Price</th>\n",
       "      <th>Month.Count</th>\n",
       "      <th>monthNumSqred</th>\n",
       "      <th>monthNumCubed</th>\n",
       "      <th>Month_Cnt</th>\n",
       "      <th>...</th>\n",
       "      <th>Dec</th>\n",
       "      <th>Feb</th>\n",
       "      <th>Jan</th>\n",
       "      <th>Jul</th>\n",
       "      <th>Jun</th>\n",
       "      <th>Mar</th>\n",
       "      <th>May</th>\n",
       "      <th>Nov</th>\n",
       "      <th>Oct</th>\n",
       "      <th>Sep</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1995-01-31</th>\n",
       "      <td>1995</td>\n",
       "      <td>Jan</td>\n",
       "      <td>4.370</td>\n",
       "      <td>51.595</td>\n",
       "      <td>2.112</td>\n",
       "      <td>0.9803</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>-1.724471</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1995-02-28</th>\n",
       "      <td>1995</td>\n",
       "      <td>Feb</td>\n",
       "      <td>3.695</td>\n",
       "      <td>56.086</td>\n",
       "      <td>1.932</td>\n",
       "      <td>0.8924</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>8</td>\n",
       "      <td>-1.709277</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1995-03-31</th>\n",
       "      <td>1995</td>\n",
       "      <td>Mar</td>\n",
       "      <td>4.538</td>\n",
       "      <td>68.453</td>\n",
       "      <td>2.162</td>\n",
       "      <td>0.8924</td>\n",
       "      <td>3</td>\n",
       "      <td>9</td>\n",
       "      <td>27</td>\n",
       "      <td>-1.694084</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1995-04-30</th>\n",
       "      <td>1995</td>\n",
       "      <td>Apr</td>\n",
       "      <td>4.280</td>\n",
       "      <td>65.722</td>\n",
       "      <td>2.130</td>\n",
       "      <td>0.8967</td>\n",
       "      <td>4</td>\n",
       "      <td>16</td>\n",
       "      <td>64</td>\n",
       "      <td>-1.678890</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1995-05-31</th>\n",
       "      <td>1995</td>\n",
       "      <td>May</td>\n",
       "      <td>4.470</td>\n",
       "      <td>73.730</td>\n",
       "      <td>2.227</td>\n",
       "      <td>0.8967</td>\n",
       "      <td>5</td>\n",
       "      <td>25</td>\n",
       "      <td>125</td>\n",
       "      <td>-1.663696</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1995-06-30</th>\n",
       "      <td>1995</td>\n",
       "      <td>Jun</td>\n",
       "      <td>4.238</td>\n",
       "      <td>77.994</td>\n",
       "      <td>2.124</td>\n",
       "      <td>0.9160</td>\n",
       "      <td>6</td>\n",
       "      <td>36</td>\n",
       "      <td>216</td>\n",
       "      <td>-1.648503</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1995-07-31</th>\n",
       "      <td>1995</td>\n",
       "      <td>Jul</td>\n",
       "      <td>4.377</td>\n",
       "      <td>81.475</td>\n",
       "      <td>2.184</td>\n",
       "      <td>0.9160</td>\n",
       "      <td>7</td>\n",
       "      <td>49</td>\n",
       "      <td>343</td>\n",
       "      <td>-1.633309</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1995-08-31</th>\n",
       "      <td>1995</td>\n",
       "      <td>Aug</td>\n",
       "      <td>4.368</td>\n",
       "      <td>74.981</td>\n",
       "      <td>2.152</td>\n",
       "      <td>0.8934</td>\n",
       "      <td>8</td>\n",
       "      <td>64</td>\n",
       "      <td>512</td>\n",
       "      <td>-1.618116</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1995-09-30</th>\n",
       "      <td>1995</td>\n",
       "      <td>Sep</td>\n",
       "      <td>3.917</td>\n",
       "      <td>61.530</td>\n",
       "      <td>2.062</td>\n",
       "      <td>0.8934</td>\n",
       "      <td>9</td>\n",
       "      <td>81</td>\n",
       "      <td>729</td>\n",
       "      <td>-1.602922</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1995-10-31</th>\n",
       "      <td>1995</td>\n",
       "      <td>Oct</td>\n",
       "      <td>4.078</td>\n",
       "      <td>60.022</td>\n",
       "      <td>2.121</td>\n",
       "      <td>0.9434</td>\n",
       "      <td>10</td>\n",
       "      <td>100</td>\n",
       "      <td>1000</td>\n",
       "      <td>-1.587729</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1995-11-30</th>\n",
       "      <td>1995</td>\n",
       "      <td>Nov</td>\n",
       "      <td>3.611</td>\n",
       "      <td>52.772</td>\n",
       "      <td>2.030</td>\n",
       "      <td>0.9434</td>\n",
       "      <td>11</td>\n",
       "      <td>121</td>\n",
       "      <td>1331</td>\n",
       "      <td>-1.572535</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1995-12-31</th>\n",
       "      <td>1995</td>\n",
       "      <td>Dec</td>\n",
       "      <td>3.591</td>\n",
       "      <td>50.850</td>\n",
       "      <td>2.091</td>\n",
       "      <td>1.0811</td>\n",
       "      <td>12</td>\n",
       "      <td>144</td>\n",
       "      <td>1728</td>\n",
       "      <td>-1.557341</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>12 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            Year Month  Cotagecheese.Prod  Icecream.Prod  Milk.Prod  \\\n",
       "1995-01-31  1995   Jan              4.370         51.595      2.112   \n",
       "1995-02-28  1995   Feb              3.695         56.086      1.932   \n",
       "1995-03-31  1995   Mar              4.538         68.453      2.162   \n",
       "1995-04-30  1995   Apr              4.280         65.722      2.130   \n",
       "1995-05-31  1995   May              4.470         73.730      2.227   \n",
       "1995-06-30  1995   Jun              4.238         77.994      2.124   \n",
       "1995-07-31  1995   Jul              4.377         81.475      2.184   \n",
       "1995-08-31  1995   Aug              4.368         74.981      2.152   \n",
       "1995-09-30  1995   Sep              3.917         61.530      2.062   \n",
       "1995-10-31  1995   Oct              4.078         60.022      2.121   \n",
       "1995-11-30  1995   Nov              3.611         52.772      2.030   \n",
       "1995-12-31  1995   Dec              3.591         50.850      2.091   \n",
       "\n",
       "            N.CA.Fat.Price  Month.Count  monthNumSqred  monthNumCubed  \\\n",
       "1995-01-31          0.9803            1              1              1   \n",
       "1995-02-28          0.8924            2              4              8   \n",
       "1995-03-31          0.8924            3              9             27   \n",
       "1995-04-30          0.8967            4             16             64   \n",
       "1995-05-31          0.8967            5             25            125   \n",
       "1995-06-30          0.9160            6             36            216   \n",
       "1995-07-31          0.9160            7             49            343   \n",
       "1995-08-31          0.8934            8             64            512   \n",
       "1995-09-30          0.8934            9             81            729   \n",
       "1995-10-31          0.9434           10            100           1000   \n",
       "1995-11-30          0.9434           11            121           1331   \n",
       "1995-12-31          1.0811           12            144           1728   \n",
       "\n",
       "            Month_Cnt  ...  Dec  Feb  Jan  Jul  Jun  Mar  May  Nov  Oct  Sep  \n",
       "1995-01-31  -1.724471  ...    0    0    1    0    0    0    0    0    0    0  \n",
       "1995-02-28  -1.709277  ...    0    1    0    0    0    0    0    0    0    0  \n",
       "1995-03-31  -1.694084  ...    0    0    0    0    0    1    0    0    0    0  \n",
       "1995-04-30  -1.678890  ...    0    0    0    0    0    0    0    0    0    0  \n",
       "1995-05-31  -1.663696  ...    0    0    0    0    0    0    1    0    0    0  \n",
       "1995-06-30  -1.648503  ...    0    0    0    0    1    0    0    0    0    0  \n",
       "1995-07-31  -1.633309  ...    0    0    0    1    0    0    0    0    0    0  \n",
       "1995-08-31  -1.618116  ...    0    0    0    0    0    0    0    0    0    0  \n",
       "1995-09-30  -1.602922  ...    0    0    0    0    0    0    0    0    0    1  \n",
       "1995-10-31  -1.587729  ...    0    0    0    0    0    0    0    0    1    0  \n",
       "1995-11-30  -1.572535  ...    0    0    0    0    0    0    0    1    0    0  \n",
       "1995-12-31  -1.557341  ...    1    0    0    0    0    0    0    0    0    0  \n",
       "\n",
       "[12 rows x 23 columns]"
      ]
     },
     "execution_count": 171,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Create new features, the count of months from the start of the\n",
    "## series and the square of the count of months\n",
    "df.loc[:, 'Month_Cnt'] = [float(i + 1) for i in range(len(df['Icecream.Prod']))]\n",
    "df.loc[:, 'Month_Cnt2'] = [x**2 for x in df.Month_Cnt]\n",
    "\n",
    "## Normalize the time features\n",
    "df.loc[:, ['Month_Cnt', 'Month_Cnt2']] = df.loc[:, ['Month_Cnt', 'Month_Cnt2']].apply(zscore)\n",
    "\n",
    "## Create dummy variables for the months\n",
    "dummies = pd.get_dummies(df.loc[:, 'Month'])\n",
    "df[list(dummies.columns)] = dummies\n",
    "## Print the head of the data frame to look at the dummy variables.\n",
    "df.head(12) # look at the first year"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Year</th>\n",
       "      <th>Month</th>\n",
       "      <th>Cotagecheese.Prod</th>\n",
       "      <th>Icecream.Prod</th>\n",
       "      <th>Milk.Prod</th>\n",
       "      <th>N.CA.Fat.Price</th>\n",
       "      <th>Month.Count</th>\n",
       "      <th>monthNumSqred</th>\n",
       "      <th>monthNumCubed</th>\n",
       "      <th>Month_Cnt</th>\n",
       "      <th>...</th>\n",
       "      <th>Dec</th>\n",
       "      <th>Feb</th>\n",
       "      <th>Jan</th>\n",
       "      <th>Jul</th>\n",
       "      <th>Jun</th>\n",
       "      <th>Mar</th>\n",
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       "      <th>Nov</th>\n",
       "      <th>Oct</th>\n",
       "      <th>Sep</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2013-01-31</th>\n",
       "      <td>2013</td>\n",
       "      <td>Jan</td>\n",
       "      <td>2.282</td>\n",
       "      <td>62.612</td>\n",
       "      <td>3.462</td>\n",
       "      <td>1.7744</td>\n",
       "      <td>217</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-02-28</th>\n",
       "      <td>2013</td>\n",
       "      <td>Feb</td>\n",
       "      <td>1.941</td>\n",
       "      <td>66.274</td>\n",
       "      <td>3.231</td>\n",
       "      <td>1.6224</td>\n",
       "      <td>218</td>\n",
       "      <td>47524</td>\n",
       "      <td>10360232</td>\n",
       "      <td>1.572535</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-03-31</th>\n",
       "      <td>2013</td>\n",
       "      <td>Mar</td>\n",
       "      <td>2.150</td>\n",
       "      <td>75.770</td>\n",
       "      <td>3.676</td>\n",
       "      <td>1.6968</td>\n",
       "      <td>219</td>\n",
       "      <td>47961</td>\n",
       "      <td>10503459</td>\n",
       "      <td>1.587729</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-04-30</th>\n",
       "      <td>2013</td>\n",
       "      <td>Apr</td>\n",
       "      <td>2.217</td>\n",
       "      <td>76.396</td>\n",
       "      <td>3.622</td>\n",
       "      <td>1.7444</td>\n",
       "      <td>220</td>\n",
       "      <td>48400</td>\n",
       "      <td>10648000</td>\n",
       "      <td>1.602922</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>2013-05-31</th>\n",
       "      <td>2013</td>\n",
       "      <td>May</td>\n",
       "      <td>2.341</td>\n",
       "      <td>80.899</td>\n",
       "      <td>3.720</td>\n",
       "      <td>1.8552</td>\n",
       "      <td>221</td>\n",
       "      <td>48841</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-06-30</th>\n",
       "      <td>2013</td>\n",
       "      <td>Jun</td>\n",
       "      <td>2.694</td>\n",
       "      <td>92.091</td>\n",
       "      <td>3.489</td>\n",
       "      <td>1.8314</td>\n",
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       "      <td>49284</td>\n",
       "      <td>10941048</td>\n",
       "      <td>1.633309</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-07-31</th>\n",
       "      <td>2013</td>\n",
       "      <td>Jul</td>\n",
       "      <td>2.433</td>\n",
       "      <td>84.026</td>\n",
       "      <td>3.373</td>\n",
       "      <td>1.6923</td>\n",
       "      <td>223</td>\n",
       "      <td>49729</td>\n",
       "      <td>11089567</td>\n",
       "      <td>1.648503</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-08-31</th>\n",
       "      <td>2013</td>\n",
       "      <td>Aug</td>\n",
       "      <td>2.169</td>\n",
       "      <td>78.382</td>\n",
       "      <td>3.400</td>\n",
       "      <td>1.6273</td>\n",
       "      <td>224</td>\n",
       "      <td>50176</td>\n",
       "      <td>11239424</td>\n",
       "      <td>1.663696</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-09-30</th>\n",
       "      <td>2013</td>\n",
       "      <td>Sep</td>\n",
       "      <td>2.193</td>\n",
       "      <td>74.871</td>\n",
       "      <td>3.197</td>\n",
       "      <td>1.5519</td>\n",
       "      <td>225</td>\n",
       "      <td>50625</td>\n",
       "      <td>11390625</td>\n",
       "      <td>1.678890</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-10-31</th>\n",
       "      <td>2013</td>\n",
       "      <td>Oct</td>\n",
       "      <td>2.284</td>\n",
       "      <td>70.011</td>\n",
       "      <td>3.338</td>\n",
       "      <td>1.5566</td>\n",
       "      <td>226</td>\n",
       "      <td>51076</td>\n",
       "      <td>11543176</td>\n",
       "      <td>1.694084</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-11-30</th>\n",
       "      <td>2013</td>\n",
       "      <td>Nov</td>\n",
       "      <td>1.865</td>\n",
       "      <td>61.394</td>\n",
       "      <td>3.275</td>\n",
       "      <td>1.7537</td>\n",
       "      <td>227</td>\n",
       "      <td>51529</td>\n",
       "      <td>11697083</td>\n",
       "      <td>1.709277</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-12-31</th>\n",
       "      <td>2013</td>\n",
       "      <td>Dec</td>\n",
       "      <td>2.345</td>\n",
       "      <td>53.653</td>\n",
       "      <td>3.473</td>\n",
       "      <td>1.6747</td>\n",
       "      <td>228</td>\n",
       "      <td>51984</td>\n",
       "      <td>11852352</td>\n",
       "      <td>1.724471</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>12 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            Year Month  Cotagecheese.Prod  Icecream.Prod  Milk.Prod  \\\n",
       "2013-01-31  2013   Jan              2.282         62.612      3.462   \n",
       "2013-02-28  2013   Feb              1.941         66.274      3.231   \n",
       "2013-03-31  2013   Mar              2.150         75.770      3.676   \n",
       "2013-04-30  2013   Apr              2.217         76.396      3.622   \n",
       "2013-05-31  2013   May              2.341         80.899      3.720   \n",
       "2013-06-30  2013   Jun              2.694         92.091      3.489   \n",
       "2013-07-31  2013   Jul              2.433         84.026      3.373   \n",
       "2013-08-31  2013   Aug              2.169         78.382      3.400   \n",
       "2013-09-30  2013   Sep              2.193         74.871      3.197   \n",
       "2013-10-31  2013   Oct              2.284         70.011      3.338   \n",
       "2013-11-30  2013   Nov              1.865         61.394      3.275   \n",
       "2013-12-31  2013   Dec              2.345         53.653      3.473   \n",
       "\n",
       "            N.CA.Fat.Price  Month.Count  monthNumSqred  monthNumCubed  \\\n",
       "2013-01-31          1.7744          217          47089       10218313   \n",
       "2013-02-28          1.6224          218          47524       10360232   \n",
       "2013-03-31          1.6968          219          47961       10503459   \n",
       "2013-04-30          1.7444          220          48400       10648000   \n",
       "2013-05-31          1.8552          221          48841       10793861   \n",
       "2013-06-30          1.8314          222          49284       10941048   \n",
       "2013-07-31          1.6923          223          49729       11089567   \n",
       "2013-08-31          1.6273          224          50176       11239424   \n",
       "2013-09-30          1.5519          225          50625       11390625   \n",
       "2013-10-31          1.5566          226          51076       11543176   \n",
       "2013-11-30          1.7537          227          51529       11697083   \n",
       "2013-12-31          1.6747          228          51984       11852352   \n",
       "\n",
       "            Month_Cnt  ...  Dec  Feb  Jan  Jul  Jun  Mar  May  Nov  Oct  Sep  \n",
       "2013-01-31   1.557341  ...    0    0    1    0    0    0    0    0    0    0  \n",
       "2013-02-28   1.572535  ...    0    1    0    0    0    0    0    0    0    0  \n",
       "2013-03-31   1.587729  ...    0    0    0    0    0    1    0    0    0    0  \n",
       "2013-04-30   1.602922  ...    0    0    0    0    0    0    0    0    0    0  \n",
       "2013-05-31   1.618116  ...    0    0    0    0    0    0    1    0    0    0  \n",
       "2013-06-30   1.633309  ...    0    0    0    0    1    0    0    0    0    0  \n",
       "2013-07-31   1.648503  ...    0    0    0    1    0    0    0    0    0    0  \n",
       "2013-08-31   1.663696  ...    0    0    0    0    0    0    0    0    0    0  \n",
       "2013-09-30   1.678890  ...    0    0    0    0    0    0    0    0    0    1  \n",
       "2013-10-31   1.694084  ...    0    0    0    0    0    0    0    0    1    0  \n",
       "2013-11-30   1.709277  ...    0    0    0    0    0    0    0    1    0    0  \n",
       "2013-12-31   1.724471  ...    1    0    0    0    0    0    0    0    0    0  \n",
       "\n",
       "[12 rows x 23 columns]"
      ]
     },
     "execution_count": 172,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.tail(12) # look at the last year"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Extract values for last 12 months used for forecasting\n",
    "X = df.loc[:'2012-12-31', ['Month_Cnt', 'Month_Cnt2', 'Jan', 'Feb', 'Mar', 'Apr', 'May', 'Jun', 'Jul', 'Aug', 'Sep', 'Oct', 'Nov', 'Dec']].values\n",
    "Y = df.loc[:'2012-12-31', 'Icecream.Prod'].values\n",
    "lm_mod = lm.LinearRegression(fit_intercept = False)\n",
    "mod_fit = lm_mod.fit(X, Y)\n",
    "# Predictions and the residual\n",
    "df.loc[:'2012-12-31', 'scores'] = mod_fit.predict(X)\n",
    "df.loc[:'2012-12-31', 'resids'] = df.loc[:'2012-12-31', 'scores'] - df.loc[:'2012-12-31', 'Icecream.Prod']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Testing model of order: (0, 0, 1) with BIC = 1077.7895234312405\n",
      "Testing model of order: (0, 0, 2) with BIC = 1082.5839455708121\n",
      "Testing model of order: (0, 0, 3) with BIC = 1060.527672990147\n",
      "Testing model of order: (0, 0, 4) with BIC = 1065.613847144987\n",
      "Testing model of order: (0, 1, 1) with BIC = 1079.125763849743\n",
      "Testing model of order: (0, 1, 3) with BIC = 1087.4255892356518\n",
      "Testing model of order: (0, 2, 1) with BIC = 1169.5315527172963\n",
      "Testing model of order: (1, 0, 0) with BIC = 1075.4541317025698\n",
      "Testing model of order: (1, 0, 1) with BIC = 1074.7202626876535\n",
      "Testing model of order: (1, 0, 2) with BIC = 1079.3882909928798\n",
      "Testing model of order: (1, 0, 3) with BIC = 1065.482569198243\n",
      "Testing model of order: (1, 1, 0) with BIC = 1120.8044126058796\n",
      "Testing model of order: (1, 1, 1) with BIC = 1081.901419618533\n",
      "Testing model of order: (1, 1, 2) with BIC = 1077.165442616407\n",
      "Testing model of order: (1, 1, 3) with BIC = 1082.5049769638074\n",
      "Testing model of order: (1, 1, 4) with BIC = 1071.379451257225\n",
      "Testing model of order: (1, 2, 0) with BIC = 1290.7014319210505\n",
      "Testing model of order: (1, 2, 3) with BIC = 1087.356163565479\n",
      "Testing model of order: (2, 0, 0) with BIC = 1077.513664673302\n",
      "Testing model of order: (2, 0, 1) with BIC = 1079.8712234808668\n",
      "Testing model of order: (2, 0, 2) with BIC = 1075.4823699574092\n",
      "Testing model of order: (2, 0, 3) with BIC = 1070.2980560779947\n",
      "Testing model of order: (2, 1, 0) with BIC = 1088.5159655917096\n",
      "Testing model of order: (2, 1, 1) with BIC = 1091.18509975978\n",
      "Testing model of order: (2, 1, 2) with BIC = 1082.4725419555089\n",
      "Testing model of order: (2, 1, 3) with BIC = 1081.712788339522\n",
      "Testing model of order: (2, 1, 4) with BIC = 1078.1197425369778\n",
      "Testing model of order: (2, 2, 0) with BIC = 1179.3803103730513\n",
      "Testing model of order: (2, 2, 1) with BIC = 1096.6517398364967\n",
      "Testing model of order: (3, 0, 0) with BIC = 1070.9871049747135\n",
      "Testing model of order: (3, 0, 1) with BIC = 1065.6187831938755\n",
      "Testing model of order: (3, 0, 2) with BIC = 1070.9869337991533\n",
      "Testing model of order: (3, 0, 3) with BIC = 1075.2223819277579\n",
      "Testing model of order: (3, 1, 0) with BIC = 1093.1416970543528\n",
      "Testing model of order: (3, 1, 1) with BIC = 1076.4033170943728\n",
      "Testing model of order: (3, 1, 2) with BIC = 1071.2373660460053\n",
      "Testing model of order: (3, 1, 3) with BIC = 1072.9971953798342\n",
      "Testing model of order: (3, 1, 4) with BIC = 1080.9296943410075\n",
      "Testing model of order: (3, 2, 0) with BIC = 1173.2517721403742\n",
      "Testing model of order: (3, 2, 1) with BIC = 1101.1512023751243\n",
      "Testing model of order: (4, 0, 0) with BIC = 1066.821365263115\n",
      "Testing model of order: (4, 0, 1) with BIC = 1070.9792793958188\n",
      "Testing model of order: (4, 0, 2) with BIC = 1075.944849464876\n",
      "Testing model of order: (4, 0, 3) with BIC = 1078.6133329635582\n",
      "Testing model of order: (4, 0, 4) with BIC = 1078.8014481641453\n",
      "Testing model of order: (4, 1, 0) with BIC = 1090.0598134396075\n",
      "Testing model of order: (4, 1, 1) with BIC = 1072.6837495762227\n",
      "Testing model of order: (4, 1, 2) with BIC = 1076.6058136600154\n",
      "Testing model of order: (4, 2, 0) with BIC = 1159.466916787099\n",
      "Testing model of order: (4, 2, 1) with BIC = 1098.4568932191924\n",
      "Best order = (0, 0, 3) best BIC = 1060.527672990147\n"
     ]
    }
   ],
   "source": [
    "# Compute the best ARIMA order\n",
    "BIC, order, model_fit = step_ARIMA(df.loc[:'2012-12-31', 'resids'], 4, 3, 4)    \n",
    "print('Best order = ' + str(order) + ' best BIC = ' + str(BIC))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                              ARMA Model Results                              \n",
      "==============================================================================\n",
      "Dep. Variable:                 resids   No. Observations:                  216\n",
      "Model:                     ARMA(0, 3)   Log Likelihood                -519.513\n",
      "Method:                           mle   S.D. of innovations              2.678\n",
      "Date:                Mon, 13 Jan 2020   AIC                           1047.027\n",
      "Time:                        23:03:05   BIC                           1060.528\n",
      "Sample:                    01-31-1995   HQIC                          1052.481\n",
      "                         - 12-31-2012                                         \n",
      "================================================================================\n",
      "                   coef    std err          z      P>|z|      [0.025      0.975]\n",
      "--------------------------------------------------------------------------------\n",
      "ma.L1.resids     0.2807      0.065      4.318      0.000       0.153       0.408\n",
      "ma.L2.resids     0.0896      0.067      1.330      0.185      -0.042       0.222\n",
      "ma.L3.resids     0.3649      0.062      5.879      0.000       0.243       0.487\n",
      "                                    Roots                                    \n",
      "=============================================================================\n",
      "                  Real          Imaginary           Modulus         Frequency\n",
      "-----------------------------------------------------------------------------\n",
      "MA.1           -1.2919           -0.0000j            1.2919           -0.5000\n",
      "MA.2            0.5232           -1.3592j            1.4564           -0.1915\n",
      "MA.3            0.5232           +1.3592j            1.4564            0.1915\n",
      "-----------------------------------------------------------------------------\n"
     ]
    }
   ],
   "source": [
    "# The best ARIMA order is (0,0,3)\n",
    "arima_remainder = model_ARIMA(df.loc[:'2012-12-31', 'resids'], order = (0,0,3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2012-12-31    0.186891\n",
       "2013-01-31   -0.023234\n",
       "2013-02-28    1.088693\n",
       "2013-03-31    1.060365\n",
       "2013-04-30    0.000000\n",
       "2013-05-31    0.000000\n",
       "2013-06-30    0.000000\n",
       "2013-07-31    0.000000\n",
       "2013-08-31    0.000000\n",
       "2013-09-30    0.000000\n",
       "2013-10-31    0.000000\n",
       "2013-11-30    0.000000\n",
       "2013-12-31    0.000000\n",
       "Freq: M, dtype: float64"
      ]
     },
     "execution_count": 176,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Predictions from the order model\n",
    "start_index = len(df.loc[:'2012-12-31', 'resids']) - 1\n",
    "end_index = start_index + 12\n",
    "# model prediction\n",
    "model_prediction = arima_remainder.predict(start=start_index, end=end_index)\n",
    "model_prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Make the forecast for the next year\n",
    "X = df.loc['2012-12-31':, ['Month_Cnt', 'Month_Cnt2', 'Jan', 'Feb', 'Mar', 'Apr', 'May', 'Jun', 'Jul', 'Aug', 'Sep', 'Oct', 'Nov', 'Dec']].values # .as_matrix() obsolete\n",
    "# and the predictions for the forecast\n",
    "df.loc['2012-12-31':, 'scores'] =  mod_fit.predict(X) - model_prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot predict value with 95% Confident Intervals\n",
    "plot_forecast(df, 'Icecream.Prod')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## - 4.1 Are the confidence intervals reasonably small compared to the forecast means?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Based on the plot above, the **95% confidence intervals** has pretty small band compared to the forecase means (scores)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## - 4.2 How do the confidence intervals behave as time moves to the future?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "execution_count": 180,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the autocorrelation of the forcast means\n",
    "splt.plot_acf(df['scores'], lags = 40)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Based on the plot above, the **95% confidence intervals** moving as a seasonal pattern."
   ]
  },
  {
   "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": "3.7.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}