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": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1b. Load the data\n",
    "Call the variable `cigData`. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "cigData = pd.read_csv('CowboyCigsData.csv')"
   ]
  },
  {
   "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": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>Time</th>\n",
       "      <th>#CigSales</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>1949-01</td>\n",
       "      <td>1000112</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1949-02</td>\n",
       "      <td>1000118</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>1949-03</td>\n",
       "      <td>1000132</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>1949-04</td>\n",
       "      <td>1000129</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>1949-05</td>\n",
       "      <td>1000121</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0     Time  #CigSales\n",
       "0           0  1949-01    1000112\n",
       "1           1  1949-02    1000118\n",
       "2           2  1949-03    1000132\n",
       "3           3  1949-04    1000129\n",
       "4           4  1949-05    1000121"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cigData.head()"
   ]
  },
  {
   "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": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(144, 3)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cigData.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's check out the data types of our columns."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Unnamed: 0     int64\n",
       "Time          object\n",
       "#CigSales      int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 5,
     "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": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Unnamed: 0    0\n",
       "Time          0\n",
       "#CigSales     0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cigData.isnull().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": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "cigData.drop('Unnamed: 0',inplace=True,axis=1)"
   ]
  },
  {
   "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": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Month', '#CigSales'], dtype='object')"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cigData.rename({'Time':'Month'},axis=1,inplace=True)\n",
    "cigData.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Call a head() to check this has worked. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "    }\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": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cigData.head()"
   ]
  },
  {
   "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": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "cigData['Month'] = pd.to_datetime(cigData['Month'])\n",
    "cigData.set_index('Month',inplace=True)"
   ]
  },
  {
   "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": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "y = cigData['#CigSales']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Check the type of our new variable. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.series.Series"
      ]
     },
     "execution_count": 12,
     "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": 13,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <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": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cigData.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": 14,
   "metadata": {},
   "outputs": [
    {
     "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",
    "**Answer: Multiplicative because the maginitude of the seasonality changes with time.**"
   ]
  },
  {
   "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": 29,
   "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": [
    "from statsmodels.tsa.seasonal import seasonal_decompose\n",
    "\n",
    "# Plot the original data, the trend, the seasonality, and the residuals \n",
    "decomposition = seasonal_decompose(y)\n",
    "trend = decomposition.trend\n",
    "seasonal = decomposition.seasonal\n",
    "residual = decomposition.resid\n",
    "\n",
    "plt.subplot(411)\n",
    "plt.plot(y, label = 'original')\n",
    "plt.legend(loc = 'best')\n",
    "\n",
    "plt.subplot(412)\n",
    "plt.plot(trend, label = 'Trend')\n",
    "plt.legend(loc = 'best')\n",
    "\n",
    "plt.subplot(413)\n",
    "plt.plot(seasonal, label = 'seasonality')\n",
    "plt.legend(loc = 'best')\n",
    "\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": 30,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\jmlam\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\stattools.py:1875: FutureWarning: The behavior of using nlags=None will change in release 0.13.Currently nlags=None is the same as nlags=\"legacy\", and so a sample-size lag length is used. After the next release, the default will change to be the same as nlags=\"auto\" which uses an automatic lag length selection method. To silence this warning, either use \"auto\" or \"legacy\"\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "C:\\Users\\jmlam\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\stattools.py:1906: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
      "look-up table. The actual p-value is smaller than the p-value returned.\n",
      "\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(1.0521750110138661,\n",
       " 0.01,\n",
       " 14,\n",
       " {'10%': 0.347, '5%': 0.463, '2.5%': 0.574, '1%': 0.739})"
      ]
     },
     "execution_count": 30,
     "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": 32,
   "metadata": {},
   "outputs": [
    {
     "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 often 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": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "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": 35,
   "metadata": {},
   "outputs": [
    {
     "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": "code",
   "execution_count": 34,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "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": 36,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\jmlam\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\stattools.py:1875: FutureWarning: The behavior of using nlags=None will change in release 0.13.Currently nlags=None is the same as nlags=\"legacy\", and so a sample-size lag length is used. After the next release, the default will change to be the same as nlags=\"auto\" which uses an automatic lag length selection method. To silence this warning, either use \"auto\" or \"legacy\"\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "C:\\Users\\jmlam\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\stattools.py:1910: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
      "look-up table. The actual p-value is greater than the p-value returned.\n",
      "\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(0.05301079859857681,\n",
       " 0.1,\n",
       " 14,\n",
       " {'10%': 0.347, '5%': 0.463, '2.5%': 0.574, '1%': 0.739})"
      ]
     },
     "execution_count": 36,
     "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": 37,
   "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": 38,
   "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",
    "    \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",
    "    \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": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Make a function to evaluate different ARIMA models with several different p, d, and q values.\n",
    "\n",
    "# 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": 40,
   "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": 41,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ARIMA(0, 0, 0) MSE=0.000\n",
      "ARIMA(0, 0, 1) MSE=0.000\n",
      "ARIMA(0, 1, 0) MSE=0.000\n",
      "ARIMA(0, 1, 1) MSE=0.000\n",
      "ARIMA(0, 1, 2) MSE=0.000\n",
      "ARIMA(0, 2, 0) MSE=0.000\n",
      "ARIMA(0, 2, 1) MSE=0.000\n",
      "ARIMA(0, 2, 2) MSE=0.000\n",
      "ARIMA(1, 0, 0) MSE=0.000\n",
      "ARIMA(1, 0, 1) MSE=0.000\n",
      "ARIMA(1, 0, 2) MSE=0.000\n",
      "ARIMA(1, 1, 0) MSE=0.000\n",
      "ARIMA(1, 1, 1) MSE=0.000\n",
      "ARIMA(1, 2, 0) MSE=0.000\n",
      "ARIMA(2, 0, 0) MSE=0.000\n",
      "ARIMA(2, 0, 1) MSE=0.000\n",
      "ARIMA(2, 1, 0) MSE=0.000\n",
      "ARIMA(2, 1, 1) MSE=0.000\n",
      "ARIMA(2, 1, 2) MSE=0.000\n",
      "ARIMA(2, 2, 0) MSE=0.000\n",
      "ARIMA(2, 2, 2) MSE=0.000\n",
      "Best ARIMA(2, 1, 1) MSE=0.000\n"
     ]
    }
   ],
   "source": [
    "# Finally, we can find the optimum ARIMA model for our data.\n",
    "# Nb. this can take a while...!\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")\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": 42,
   "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": 43,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>ARIMA Model Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>    <td>D.#CigSales</td>   <th>  No. Observations:  </th>    <td>143</td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>          <td>ARIMA(2, 1, 1)</td>  <th>  Log Likelihood     </th> <td>1263.116</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>css-mle</td>     <th>  S.D. of innovations</th>   <td>0.000</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>          <td>Wed, 08 Dec 2021</td> <th>  AIC                </th> <td>-2516.233</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>              <td>13:23:31</td>     <th>  BIC                </th> <td>-2501.418</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Sample:</th>           <td>02-01-1949</td>    <th>  HQIC               </th> <td>-2510.213</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th></th>                 <td>- 12-01-1960</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>const</th>             <td> 2.624e-06</td> <td> 5.06e-07</td> <td>    5.184</td> <td> 0.000</td> <td> 1.63e-06</td> <td> 3.62e-06</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ar.L1.D.#CigSales</th> <td>    0.4681</td> <td>    0.156</td> <td>    3.003</td> <td> 0.003</td> <td>    0.163</td> <td>    0.774</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ar.L2.D.#CigSales</th> <td>   -0.2640</td> <td>    0.109</td> <td>   -2.413</td> <td> 0.016</td> <td>   -0.478</td> <td>   -0.050</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ma.L1.D.#CigSales</th> <td>   -0.8693</td> <td>      nan</td> <td>      nan</td> <td>   nan</td> <td>      nan</td> <td>      nan</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<caption>Roots</caption>\n",
       "<tr>\n",
       "    <td></td>   <th>            Real</th>  <th>         Imaginary</th> <th>         Modulus</th>  <th>        Frequency</th>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>AR.1</th> <td>           0.8866</td> <td>          -1.7326j</td> <td>           1.9463</td> <td>          -0.1747</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>AR.2</th> <td>           0.8866</td> <td>          +1.7326j</td> <td>           1.9463</td> <td>           0.1747</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>MA.1</th> <td>           1.1504</td> <td>          +0.0000j</td> <td>           1.1504</td> <td>           0.0000</td>\n",
       "</tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                             ARIMA Model Results                              \n",
       "==============================================================================\n",
       "Dep. Variable:            D.#CigSales   No. Observations:                  143\n",
       "Model:                 ARIMA(2, 1, 1)   Log Likelihood                1263.116\n",
       "Method:                       css-mle   S.D. of innovations              0.000\n",
       "Date:                Wed, 08 Dec 2021   AIC                          -2516.233\n",
       "Time:                        13:23:31   BIC                          -2501.418\n",
       "Sample:                    02-01-1949   HQIC                         -2510.213\n",
       "                         - 12-01-1960                                         \n",
       "=====================================================================================\n",
       "                        coef    std err          z      P>|z|      [0.025      0.975]\n",
       "-------------------------------------------------------------------------------------\n",
       "const              2.624e-06   5.06e-07      5.184      0.000    1.63e-06    3.62e-06\n",
       "ar.L1.D.#CigSales     0.4681      0.156      3.003      0.003       0.163       0.774\n",
       "ar.L2.D.#CigSales    -0.2640      0.109     -2.413      0.016      -0.478      -0.050\n",
       "ma.L1.D.#CigSales    -0.8693        nan        nan        nan         nan         nan\n",
       "                                    Roots                                    \n",
       "=============================================================================\n",
       "                  Real          Imaginary           Modulus         Frequency\n",
       "-----------------------------------------------------------------------------\n",
       "AR.1            0.8866           -1.7326j            1.9463           -0.1747\n",
       "AR.2            0.8866           +1.7326j            1.9463            0.1747\n",
       "MA.1            1.1504           +0.0000j            1.1504            0.0000\n",
       "-----------------------------------------------------------------------------\n",
       "\"\"\""
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Call summary() on model_fit\n",
    "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": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x2c8368e3dc0>]"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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",
    "plt.plot(y_log.diff())\n",
    "plt.plot(model_fit.predict(), color = 'red')"
   ]
  },
  {
   "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": 47,
   "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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