Cowboy Cigarettes Case Study - Tier 3.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Springboard Time Series - 'Cowboy Cigarettes' Case Study - Tier 3"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Brief\n",
    "\n",
    "You're working in the US federal government as a data scientist in the Health and Environment department. You've been tasked with determining whether sales for the oldest and most powerful producers of cigarettes in the country are increasing or declining. \n",
    "\n",
    "**Cowboy Cigarettes (TM, *est.* 1890)** is the US's longest-running cigarette manufacturer. Like many cigarette companies, however, they haven't always been that public about their sales and marketing data. The available post-war historical data runs for only 11 years after they resumed production in 1949; stopping in 1960 before resuming again in 1970. Your job is to use the 1949-1960 data to predict whether the manufacturer's cigarette sales actually increased, decreased, or stayed the same. You need to make a probable reconstruction of the sales record of the manufacturer - predicting the future, from the perspective of the past - to contribute to a full report on US public health in relation to major cigarette companies. \n",
    "\n",
    "The results of your analysis will be used as part of a major report relating public health and local economics, and will be combined with other studies executed by your colleagues to provide important government advice.  \n",
    "\n",
    "-------------------------------\n",
    "As ever, this notebook is **tiered**, meaning you can elect that tier that is right for your confidence and skill level. There are 3 tiers, with tier 1 being the easiest and tier 3 being the hardest.  \n",
    "\n",
    "**1. Sourcing and loading** \n",
    "- Load relevant libraries \n",
    "- Load the data\n",
    "- Explore the data\n",
    "\n",
    " \n",
    "**2. Cleaning, transforming and visualizing**\n",
    "- Dropping unwanted columns\n",
    "- Nomenclature\n",
    "- Type conversions\n",
    "- Making a predictor variable `y` \n",
    "- Getting summary statistics for `y`\n",
    "- Plotting `y`\n",
    "  \n",
    "  \n",
    "**3. Modelling** \n",
    "- Decomposition\n",
    "    - Trend\n",
    "    - Seasonality\n",
    "    - Noise\n",
    "- Testing for stationarity with KPSS\n",
    "- Making the data stationary\n",
    "- The ARIMA Model\n",
    "    - Make a function to find the MSE of a single ARIMA model\n",
    "    - Make a function to evaluate the different ARIMA models with different p, d, and q values\n",
    "- Visualize the results\n",
    "- Application: Forecasting\n",
    "\n",
    "**4. Evaluating and concluding** \n",
    "- What is our conclusion?\n",
    "- Next steps\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 0. Preliminaries \n",
    "\n",
    "Time series data is just any data displaying how a single variable changes over time. It comes as a collection of metrics typically taken at regular intervals. Common examples of time series data include weekly sales data and daily stock prices. You can also easily acquire time series data from [Google Trends](https://trends.google.com/trends/?geo=US), which shows you how popular certain search terms are, measured in number of Google searches. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Sourcing and Loading\n",
    "\n",
    "### 1a. Load relevant libraries "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import relevant libraries and packages.\n",
    "#import os\n",
    "#import math\n",
    "import numpy as np \n",
    "import pandas as pd \n",
    "import matplotlib.pylab as plt\n",
    "%matplotlib inline\n",
    "\n",
    "# For all our visualization needs.\n",
    "#import seaborn as sns \n",
    "#import matplotlib.pyplot as plt\n",
    "\n",
    "# Import the ARIMA module from statsmodels\n",
    "#from statsmodels.tsa.arima.model import ARIMA\n",
    "#from statsmodels.graphics.tsaplots import plot_predict\n",
    "#from statsmodels.tsa.stattools import kpss\n",
    "\n",
    "import statsmodels.api as sm \n",
    "#from statsmodels.graphics.api import abline_plot \n",
    "#from sklearn.metrics import mean_squared_error, r2_score \n",
    "#from sklearn.model_selection import train_test_split \n",
    "#from sklearn import linear_model, preprocessing \n",
    "#from collections import Counter\n",
    "#import missingno as msno\n",
    "#from scipy import stats\n",
    "import datetime\n",
    "#import time\n",
    "#ignore warning messages to ensure clean outputs\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1b. Load the data\n",
    "Call the variable `cigData`. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Time</th>\n",
       "      <th>#CigSales</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1949-01</td>\n",
       "      <td>1000112</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1949-02</td>\n",
       "      <td>1000118</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1949-03</td>\n",
       "      <td>1000132</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1949-04</td>\n",
       "      <td>1000129</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1949-05</td>\n",
       "      <td>1000121</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Time  #CigSales\n",
       "0  1949-01    1000112\n",
       "1  1949-02    1000118\n",
       "2  1949-03    1000132\n",
       "3  1949-04    1000129\n",
       "4  1949-05    1000121"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cigData = pd.read_csv('CowBoyCigsData.csv', index_col=[0])\n",
    "cigData.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1c. Explore the data\n",
    "We now need to check whether the data conduces to a time series style analysis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 3,
     "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": [
    "cigData.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 144 entries, 0 to 143\n",
      "Data columns (total 2 columns):\n",
      " #   Column     Non-Null Count  Dtype \n",
      "---  ------     --------------  ----- \n",
      " 0   Time       144 non-null    object\n",
      " 1   #CigSales  144 non-null    int64 \n",
      "dtypes: int64(1), object(1)\n",
      "memory usage: 3.4+ KB\n"
     ]
    }
   ],
   "source": [
    "cigData.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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>#CigSales</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1.440000e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1.000280e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.199663e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000104e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.000180e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1.000266e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1.000360e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.000622e+06</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          #CigSales\n",
       "count  1.440000e+02\n",
       "mean   1.000280e+06\n",
       "std    1.199663e+02\n",
       "min    1.000104e+06\n",
       "25%    1.000180e+06\n",
       "50%    1.000266e+06\n",
       "75%    1.000360e+06\n",
       "max    1.000622e+06"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cigData.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Over a million cigarettes sold in the month of January 1949. This certainly is a popular cigarette brand. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Check out the columns feature of the data. How many columns are there? "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Time', '#CigSales'], dtype='object')"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cigData.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's check out the data types of our columns."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Time         object\n",
       "#CigSales     int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cigData.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Check whether there are any null values. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Time         0\n",
       "#CigSales    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cigData.isna().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Cleaning, transforming and visualizing"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2a. Dropping unwanted columns\n",
    "We need to cut that `Unnamed: 0` column. Delete it here."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Done in the beginning"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2b. Nomenclature"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see that the `Time` column actually has the granularity of months. Change the name of that column to `Month`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "cigData.rename(columns={\"Time\": \"Month\"}, inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Call a head() to check this has worked. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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>Month</th>\n",
       "      <th>#CigSales</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1949-01</td>\n",
       "      <td>1000112</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1949-02</td>\n",
       "      <td>1000118</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1949-03</td>\n",
       "      <td>1000132</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1949-04</td>\n",
       "      <td>1000129</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1949-05</td>\n",
       "      <td>1000121</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Month  #CigSales\n",
       "0  1949-01    1000112\n",
       "1  1949-02    1000118\n",
       "2  1949-03    1000132\n",
       "3  1949-04    1000129\n",
       "4  1949-05    1000121"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cigData.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "#does not work ==> cigData = (cigData - 1000000) * 100000 # index is different"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "df=cigData"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2c. Type conversions "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, do time series analysis on a Pandas dataframe is overkill, and is actually counter-productive. It's much more easy to carry out this type of analysis if we convert our data to a series first.\n",
    "\n",
    "Notice that the `Month` field was an object. Let's type convert the `Month` column to a Python `datetime`, before making that the index."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "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>#CigSales</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Month</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1949-01-01</th>\n",
       "      <td>1000112</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1949-02-01</th>\n",
       "      <td>1000118</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1949-03-01</th>\n",
       "      <td>1000132</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1949-04-01</th>\n",
       "      <td>1000129</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1949-05-01</th>\n",
       "      <td>1000121</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            #CigSales\n",
       "Month                \n",
       "1949-01-01    1000112\n",
       "1949-02-01    1000118\n",
       "1949-03-01    1000132\n",
       "1949-04-01    1000129\n",
       "1949-05-01    1000121"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Month']=pd.to_datetime(df['Month'])\n",
    "df.set_index(\"Month\", inplace=True)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Perfect! "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2d. Making a predictor variable `y`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The data is now indexed by date, as time series data ought to be.\n",
    "\n",
    "Since we want to predict the number of cigarette sales at Cowboy cigarettes, and `y` is typically used to signify a predictor variable, let's create a new variable called `y` and assign the indexed #Passenger column. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "y=df['#CigSales']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Check the type of our new variable. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.series.Series"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2e. Getting summary statistics for `y`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Get the summary statistics of our data here. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    1.440000e+02\n",
       "mean     1.000280e+06\n",
       "std      1.199663e+02\n",
       "min      1.000104e+06\n",
       "25%      1.000180e+06\n",
       "50%      1.000266e+06\n",
       "75%      1.000360e+06\n",
       "max      1.000622e+06\n",
       "Name: #CigSales, dtype: float64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Try visualizing the data. A simple `matplotlib` plot should do the trick.  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2f. Plotting `y`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Month'>"
      ]
     },
     "execution_count": 17,
     "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": [
    "y.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. Modelling \n",
    "### 3a. Decomposition\n",
    "What do you notice from the plot? Take at least `2` minutes to examine the plot, and write down everything you observe.  \n",
    "\n",
    "All done?\n",
    "\n",
    "We can see that, generally, there is a trend upwards in cigarette sales from at Cowboy Cigarettes. But there are also some striking - and perhaps unexpected - seasonal fluctuations. These seasonal fluctations come in a repeated pattern. Work out when these seasonal fluctuations are happening, and take 2 minutes to hypothesize on their cause here.\n",
    "\n",
    "What does it mean to *decompose* time series data? It means breaking that data into 3 components: \n",
    "\n",
    "1. **Trend**: The overall direction that the data is travelling in (like upwards or downwards)\n",
    "2. **Seasonality**: Cyclical patterns in the data \n",
    "3. **Noise**: The random variation in the data\n",
    "\n",
    "We can treat these components differently, depending on the question and what's appropriate in the context. They can either be added together in an *additive* model, or multiplied together in a *multiplicative* model. \n",
    "\n",
    "Make a coffee, take `5` minutes and read [this article](https://medium.com/@sigmundojr/seasonality-in-python-additive-or-multiplicative-model-d4b9cf1f48a7) and think about whether our data would conduce to an additive or multiplicative model here. Write your conclusion down just here: \n",
    "\n",
    "-------------------------------"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Check if the data is additive \n",
    "#decomposition = sm.tsa.seasonal_decompose(y, model='additive')\n",
    "#decomposition.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Check if the data is multiplicative\n",
    "#decomposition = sm.tsa.seasonal_decompose(y, model='multiplicative')\n",
    "#decomposition.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "All done? Well, just on the basis of the plot above, it seems our Cowboy Cigarettes data is actually multiplicative. \n",
    "\n",
    "That's because, as time progresses, the general trend seems to be increasing *at a rate that's also increasing*. We also see that the seasonal fluctuations (the peaks and troughs) get bigger and bigger as time progresses.\n",
    "\n",
    "Now on the other hand, if the data were simply additive, we could expect the general trend to increase at a *steadily*, and a constant speed; and also for seasonal ups and downs not to increase or decrease in extent over time.\n",
    "\n",
    "Happily, we can use the `decompose()` function to quantify the component parts described above in our data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "scrolled": true
   },
   "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": [
    "# Import seasonal_decompose \n",
    "from statsmodels.tsa.seasonal import seasonal_decompose\n",
    "\n",
    "# Make a variable called decomposition, and assign it y passed to seasonal_decompose() \n",
    "#decomposition = seasonal_decompose(y)\n",
    "decomposition = sm.tsa.seasonal_decompose(y, model='multiplicative', \n",
    "                            extrapolate_trend='freq') \n",
    "\n",
    "# Make three variables for trend, seasonal and residual components respectively. \n",
    "# Assign them the relevant features of decomposition \n",
    "trend = decomposition.trend\n",
    "seasonal = decomposition.seasonal\n",
    "residual = decomposition.resid\n",
    "\n",
    "# Plot the original data, the trend, the seasonality, and the residuals \n",
    "plt.subplot(411)\n",
    "plt.plot(y, label = 'Original Data')\n",
    "plt.legend(loc = 'best')\n",
    "plt.subplot(412)\n",
    "plt.plot(trend, label = 'Trend')\n",
    "plt.legend(loc = 'best')\n",
    "plt.subplot(413)\n",
    "plt.plot(seasonal, label = 'Seasonal')\n",
    "plt.legend(loc = 'best')\n",
    "plt.subplot(414)\n",
    "plt.plot(residual, label = 'Residuals')\n",
    "plt.legend(loc = 'best')\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3b. Testing for stationarity with KPSS\n",
    "As you know, when doing time series analysis we always have to check for stationarity. Imprecisely, a time series dataset is stationary just if its statistical features don't change over time. A little more precisely, a stationary time series dataset will have constant mean, variance, and covariance.\n",
    "\n",
    "There are many ways to test for stationarity, but one of the most common is the KPSS test. The Null hypothesis of this test is that the time series data in question is stationary; hence, if the *p*-value is less than the significance level (typically 0.05, but we decide) then we reject the Null and infer that the data is not stationary."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1.651312235417082,\n",
       " 0.01,\n",
       " 8,\n",
       " {'10%': 0.347, '5%': 0.463, '2.5%': 0.574, '1%': 0.739})"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from statsmodels.tsa.stattools import kpss\n",
    "kpss(y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since our p-value is less than 0.05, we should reject the Null hypothesis and deduce the non-stationarity of our data. \n",
    "\n",
    "But our data need to be stationary! So we need to do some transforming."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3c. Making the data stationary \n",
    "Let's recall what it looks like. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Month'>"
      ]
     },
     "execution_count": 22,
     "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": [
    "y.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In our plot, we can see that both the mean and the variance *increase as time progresses*. At the moment, our data has neither a constant mean, nor a constant variance (the covariance, however, seems constant). \n",
    "\n",
    "One ofte  used way of getting rid of changing variance is to take the natural log of all the values in our dataset. Let's do this now. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Declare a variable called y_log\n",
    "y_log = np.log(y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "When you plot this, you can see how the variance in our data now remains contant over time."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Month'>"
      ]
     },
     "execution_count": 24,
     "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": [
    "y_log.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We now have a constant variance, but we also need a constant mean.\n",
    "\n",
    "We can do this by *differencing* our data. We difference a time series dataset when we create a new time series comprising the difference between the values of our existing dataset.\n",
    "\n",
    "Python is powerful, and we can use the `diff()` function to do this. You'll notice there's one less value than our existing dataset (since we're taking the difference between the existing values)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.023897674207570566,\n",
       " 0.1,\n",
       " 7,\n",
       " {'10%': 0.347, '5%': 0.463, '2.5%': 0.574, '1%': 0.739})"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kpss(y_log.diff().dropna())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Our p-value is now greater than 0.05, so we can accept the null hypothesis that our data is stationary."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3d. The ARIMA model\n",
    "\n",
    "Recall that ARIMA models are based around the idea that it's possible to predict the next value in a time series by using information about the most recent data points. It also assumes there will be some randomness in our data that can't ever be predicted.\n",
    "\n",
    "We can find some good parameters for our model using the `sklearn` and `statsmodels` libraries, and in particular `mean_squared_error` and `ARIMA`. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import mean_squared_error and ARIMA\n",
    "from sklearn.metrics import mean_squared_error\n",
    "from statsmodels.tsa.arima.model import ARIMA"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 3di. Make a function to find the MSE of a single ARIMA model\n",
    "Things get intricate here. Don't worry if you can't do this yourself and need to drop down a Tier. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Make a function called evaluate_arima_model to find the MSE of a single ARIMA model \n",
    "def evaluate_arima_model(data, arima_order):\n",
    "    # Needs to be an integer because it is later used as an index.\n",
    "    # Use int()\n",
    "    split=int(len(data) * 0.8) \n",
    "    # Make train and test variables, with 'train, test'\n",
    "    train, test = data[0:split], data[split:len(data)]\n",
    "    past=[x for x in train]\n",
    "    # make predictions\n",
    "    predictions = list()\n",
    "    for i in range(len(test)):#timestep-wise comparison between test data and one-step prediction ARIMA model. \n",
    "        model = ARIMA(past, order=arima_order)\n",
    "        model_fit = model.fit(disp=0)\n",
    "        future = model_fit.forecast()[0]\n",
    "        predictions.append(future)\n",
    "        past.append(test[i])\n",
    "    # calculate out of sample error\n",
    "    error = mean_squared_error(test, predictions)\n",
    "    # Return the error\n",
    "    return error"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 3dii. Make a function to evaluate the different ARIMA models with different p, d, and q values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Make a function called evaluate_models to evaluate different ARIMA models with several different p, d, and q values.\n",
    "def evaluate_models(dataset, p_values, d_values, q_values):\n",
    "    best_score, best_cfg = float(\"inf\"), None\n",
    "    # Iterate through p_values\n",
    "    for p in p_values:\n",
    "        # Iterate through d_values\n",
    "        for d in d_values:\n",
    "            # Iterate through q_values\n",
    "            for q in q_values:\n",
    "                # p, d, q iterator variables in that order\n",
    "                order = (p, d, q)\n",
    "                try:\n",
    "                    # Make a variable called mse for the Mean squared error\n",
    "                    mse = evaluate_arima_model(dataset, order)\n",
    "                    if mse < best_score:\n",
    "                        best_score, best_cfg = mse, order\n",
    "                    print('ARIMA%s MSE=%.3f' % (order,mse))\n",
    "                except:\n",
    "                    continue\n",
    "    return print('Best ARIMA%s MSE=%.3f' % (best_cfg, best_score))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Now, we choose a couple of values to try for each parameter: p_values, d_values and q_values\n",
    "# Fill in the blanks as appropriate\n",
    "p_values = [x for x in range(0, 3)]\n",
    "d_values = [x for x in range(0, 3)]\n",
    "q_values = [x for x in range(0, 3)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best ARIMANone MSE=inf\n"
     ]
    }
   ],
   "source": [
    "# Finally, we can find the optimum ARIMA model for our data.\n",
    "# Nb. this can take a while...!\n",
    "evaluate_models(y_log, p_values, d_values, q_values)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So the best p,d, q, parameters for our ARIMA model are 2, 1, 1 respectively. Now we know this, we can build the model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "p=2\n",
    "d=1\n",
    "q=1\n",
    "model = ARIMA(y_log, order=(p,d,q))\n",
    "model_fit = model.fit()\n",
    "forecast = model_fit.forecast(24)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can take a look at a summary of the model this library has built around our data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>SARIMAX Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>       <td>#CigSales</td>    <th>  No. Observations:  </th>    <td>144</td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>            <td>ARIMA(2, 1, 1)</td>  <th>  Log Likelihood     </th> <td>1285.631</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>            <td>Tue, 11 Oct 2022</td> <th>  AIC                </th> <td>-2563.263</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                <td>07:44:38</td>     <th>  BIC                </th> <td>-2551.411</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Sample:</th>             <td>01-01-1949</td>    <th>  HQIC               </th> <td>-2558.447</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th></th>                   <td>- 12-01-1960</td>   <th>                     </th>     <td> </td>    \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>        <td>opg</td>       <th>                     </th>     <td> </td>    \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "     <td></td>       <th>coef</th>     <th>std err</th>      <th>z</th>      <th>P>|z|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ar.L1</th>  <td>    1.0575</td> <td>  1.1e-19</td> <td> 9.59e+18</td> <td> 0.000</td> <td>    1.057</td> <td>    1.057</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ar.L2</th>  <td>   -0.4391</td> <td> 6.75e-19</td> <td>-6.51e+17</td> <td> 0.000</td> <td>   -0.439</td> <td>   -0.439</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ma.L1</th>  <td>   -0.7080</td> <td> 3.02e-20</td> <td>-2.35e+19</td> <td> 0.000</td> <td>   -0.708</td> <td>   -0.708</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>sigma2</th> <td> 8.444e-10</td> <td> 2.83e-10</td> <td>    2.983</td> <td> 0.003</td> <td>  2.9e-10</td> <td>  1.4e-09</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Ljung-Box (L1) (Q):</th>     <td>0.64</td> <th>  Jarque-Bera (JB):  </th> <td>0.64</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Q):</th>                <td>0.42</td> <th>  Prob(JB):          </th> <td>0.73</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Heteroskedasticity (H):</th> <td>7.96</td> <th>  Skew:              </th> <td>0.15</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(H) (two-sided):</th>    <td>0.00</td> <th>  Kurtosis:          </th> <td>3.15</td>\n",
       "</tr>\n",
       "</table><br/><br/>Warnings:<br/>[1] Covariance matrix calculated using the outer product of gradients (complex-step).<br/>[2] Covariance matrix is singular or near-singular, with condition number 1.84e+36. Standard errors may be unstable."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                               SARIMAX Results                                \n",
       "==============================================================================\n",
       "Dep. Variable:              #CigSales   No. Observations:                  144\n",
       "Model:                 ARIMA(2, 1, 1)   Log Likelihood                1285.631\n",
       "Date:                Tue, 11 Oct 2022   AIC                          -2563.263\n",
       "Time:                        07:44:38   BIC                          -2551.411\n",
       "Sample:                    01-01-1949   HQIC                         -2558.447\n",
       "                         - 12-01-1960                                         \n",
       "Covariance Type:                  opg                                         \n",
       "==============================================================================\n",
       "                 coef    std err          z      P>|z|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "ar.L1          1.0575    1.1e-19   9.59e+18      0.000       1.057       1.057\n",
       "ar.L2         -0.4391   6.75e-19  -6.51e+17      0.000      -0.439      -0.439\n",
       "ma.L1         -0.7080   3.02e-20  -2.35e+19      0.000      -0.708      -0.708\n",
       "sigma2      8.444e-10   2.83e-10      2.983      0.003     2.9e-10     1.4e-09\n",
       "===================================================================================\n",
       "Ljung-Box (L1) (Q):                   0.64   Jarque-Bera (JB):                 0.64\n",
       "Prob(Q):                              0.42   Prob(JB):                         0.73\n",
       "Heteroskedasticity (H):               7.96   Skew:                             0.15\n",
       "Prob(H) (two-sided):                  0.00   Kurtosis:                         3.15\n",
       "===================================================================================\n",
       "\n",
       "Warnings:\n",
       "[1] Covariance matrix calculated using the outer product of gradients (complex-step).\n",
       "[2] Covariance matrix is singular or near-singular, with condition number 1.84e+36. Standard errors may be unstable.\n",
       "\"\"\""
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_fit.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3e. Visualize the results \n",
    "\n",
    "Visualize the original dataset plotted against our model. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Call figure() and plot() on the plt\n",
    "plt.figure(figsize=(15,10))\n",
    "\n",
    "plt.title('Log Original and Log Predicted')\n",
    "plt.plot(y_log, label='Original')\n",
    "plt.plot(model_fit.predict(), alpha=0.7, label='Predicted')\n",
    "\n",
    "plt.legend()\n",
    "\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3f. Application: Forecasting\n",
    "\n",
    "We've done well: our model fits pretty closely to our existing data. Let's now use it to forecast what's likely to occur in future."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Declare a variable called forecast_period with the amount of months to forecast, and\n",
    "# create a range of future dates that is the length of the periods you've chosen to forecast\n",
    "forecast_period = 24\n",
    "date_range = pd.date_range(y_log.index[-1], periods = forecast_period, \n",
    "              freq='MS').strftime(\"%Y-%m-%d\").tolist()\n",
    "\n",
    "# Convert that range into a dataframe that includes your predictions\n",
    "# First, call DataFrame on pd\n",
    "future_months = pd.DataFrame(date_range, columns = ['Month'])\n",
    "# Let's now convert the 'Month' column to a datetime object with to_datetime \n",
    "future_months['Month'] = pd.to_datetime(future_months['Month'])\n",
    "future_months.set_index('Month', inplace = True)\n",
    "future_months['Prediction'] = forecast[0]\n",
    "\n",
    "# Plot your future predictions\n",
    "# Call figure() on plt\n",
    "plt.figure(figsize=(15,10))\n",
    "plt.plot(y_log)\n",
    "plt.plot(y_log['Nov 1960'].append(future_months['Prediction']))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4. Evaluating and Concluding\n",
    "\n",
    "Our model captures the centre of a line that's increasing at a remarkable rate. Cowboy Cigarettes sell more cigarettes in the summer, perhaps due to the good weather, disposable income and time off that people enjoy, and the least in the winter, when people might be spending less and enjoying less free time outdoors. \n",
    "\n",
    "Remarkably, our ARIMA model made predictions using just one variable. We can only speculate, however, on the causes of the behaviour predicted by our model. We should also take heed that spikes in data, due to sudden unusual circumstances like wars, are not handled well by ARIMA; and the outbreak of the Vietnam War in the 1960s would likely cause our model some distress.  \n",
    "\n",
    "We could suggest to our employers that, if they are interested in discovering the causes of the cigarette sales trajectory, they execute a regression analysis in addition to the time series one. "
   ]
  }
 ],
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