{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Tutorial \n",
    "\n",
    "Let us consider chapter 7 of the excellent treatise on the subject of Exponential Smoothing By Hyndman and Athanasopoulos [1].\n",
    "We will work through all the examples in the chapter as they unfold.\n",
    "\n",
    "[1] [Hyndman, Rob J., and George Athanasopoulos. Forecasting: principles and practice. OTexts, 2014.](https://www.otexts.org/fpp/7)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Exponential smoothing\n",
    "\n",
    "First we load some data. We have included the R data in the notebook for expedience."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-12-07T12:39:15.020317Z",
     "start_time": "2017-12-07T12:39:14.263100Z"
    }
   },
   "outputs": [],
   "source": [
    "import os\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from statsmodels.tsa.api import ExponentialSmoothing, SimpleExpSmoothing, Holt\n",
    "\n",
    "data = [446.6565,  454.4733,  455.663 ,  423.6322,  456.2713,  440.5881, 425.3325,  485.1494,  506.0482,  526.792 ,  514.2689,  494.211 ]\n",
    "index= pd.date_range(start='1996', end='2008', freq='A')\n",
    "oildata = pd.Series(data, index)\n",
    "\n",
    "data = [17.5534,  21.86  ,  23.8866,  26.9293,  26.8885,  28.8314, 30.0751,  30.9535,  30.1857,  31.5797,  32.5776,  33.4774, 39.0216,  41.3864,  41.5966]\n",
    "index= pd.date_range(start='1990', end='2005', freq='A')\n",
    "air = pd.Series(data, index)\n",
    "\n",
    "data = [263.9177,  268.3072,  260.6626,  266.6394,  277.5158,  283.834 , 290.309 ,  292.4742,  300.8307,  309.2867,  318.3311,  329.3724, 338.884 ,  339.2441,  328.6006,  314.2554,  314.4597,  321.4138, 329.7893,  346.3852,  352.2979,  348.3705,  417.5629,  417.1236, 417.7495,  412.2339,  411.9468,  394.6971,  401.4993,  408.2705, 414.2428]\n",
    "index= pd.date_range(start='1970', end='2001', freq='A')\n",
    "livestock2 = pd.Series(data, index)\n",
    "\n",
    "data = [407.9979 ,  403.4608,  413.8249,  428.105 ,  445.3387,  452.9942, 455.7402]\n",
    "index= pd.date_range(start='2001', end='2008', freq='A')\n",
    "livestock3 = pd.Series(data, index)\n",
    "\n",
    "data = [41.7275,  24.0418,  32.3281,  37.3287,  46.2132,  29.3463, 36.4829,  42.9777,  48.9015,  31.1802,  37.7179,  40.4202, 51.2069,  31.8872,  40.9783,  43.7725,  55.5586,  33.8509, 42.0764,  45.6423,  59.7668,  35.1919,  44.3197,  47.9137]\n",
    "index= pd.date_range(start='2005', end='2010-Q4', freq='QS-OCT')\n",
    "aust = pd.Series(data, index)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Simple Exponential Smoothing\n",
    "Lets use Simple Exponential Smoothing to forecast the below oil data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-12-07T12:39:15.189907Z",
     "start_time": "2017-12-07T12:39:15.022229Z"
    }
   },
   "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"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Figure 7.1: Oil production in Saudi Arabia from 1996 to 2007.\n"
     ]
    }
   ],
   "source": [
    "ax=oildata.plot()\n",
    "ax.set_xlabel(\"Year\")\n",
    "ax.set_ylabel(\"Oil (millions of tonnes)\")\n",
    "plt.show()\n",
    "print(\"Figure 7.1: Oil production in Saudi Arabia from 1996 to 2007.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here we run three variants of simple exponential smoothing:\n",
    "1. In ```fit1``` we do not use the auto optimization but instead choose to explicitly provide the model with the $\\alpha=0.2$ parameter\n",
    "2. In ```fit2``` as above we choose an $\\alpha=0.6$\n",
    "3. In ```fit3``` we allow statsmodels to automatically find an optimized $\\alpha$ value for us. This is the recommended approach."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-12-07T12:39:15.785068Z",
     "start_time": "2017-12-07T12:39:15.191930Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/lib/python3/dist-packages/statsmodels/tsa/holtwinters.py:731: RuntimeWarning: invalid value encountered in greater_equal\n",
      "  loc = initial_p >= ub\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fit1 = SimpleExpSmoothing(oildata).fit(smoothing_level=0.2,optimized=False)\n",
    "fcast1 = fit1.forecast(3).rename(r'$\\alpha=0.2$')\n",
    "fit2 = SimpleExpSmoothing(oildata).fit(smoothing_level=0.6,optimized=False)\n",
    "fcast2 = fit2.forecast(3).rename(r'$\\alpha=0.6$')\n",
    "fit3 = SimpleExpSmoothing(oildata).fit()\n",
    "fcast3 = fit3.forecast(3).rename(r'$\\alpha=%s$'%fit3.model.params['smoothing_level'])\n",
    "\n",
    "ax = oildata.plot(marker='o', color='black', figsize=(12,8))\n",
    "fcast1.plot(marker='o', ax=ax, color='blue', legend=True)\n",
    "fit1.fittedvalues.plot(marker='o', ax=ax, color='blue')\n",
    "fcast2.plot(marker='o', ax=ax, color='red', legend=True)\n",
    "\n",
    "fit2.fittedvalues.plot(marker='o', ax=ax, color='red')\n",
    "fcast3.plot(marker='o', ax=ax, color='green', legend=True)\n",
    "fit3.fittedvalues.plot(marker='o', ax=ax, color='green')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Holt's Method\n",
    "\n",
    "Lets take a look at another example.\n",
    "This time we use air pollution data and the Holt's Method.\n",
    "We will fit three examples again.\n",
    "1. In ```fit1``` we again choose not to use the optimizer and provide explicit values for $\\alpha=0.8$ and $\\beta=0.2$\n",
    "2. In ```fit2``` we do the same as in ```fit1``` but choose to use an exponential model rather than a Holt's additive model.\n",
    "3. In ```fit3``` we used a damped versions of the Holt's additive model but allow the dampening parameter $\\phi$ to be optimized while fixing the values for $\\alpha=0.8$ and $\\beta=0.2$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-12-07T12:39:16.114361Z",
     "start_time": "2017-12-07T12:39:15.786542Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fit1 = Holt(air).fit(smoothing_level=0.8, smoothing_slope=0.2, optimized=False)\n",
    "fcast1 = fit1.forecast(5).rename(\"Holt's linear trend\")\n",
    "fit2 = Holt(air, exponential=True).fit(smoothing_level=0.8, smoothing_slope=0.2, optimized=False)\n",
    "fcast2 = fit2.forecast(5).rename(\"Exponential trend\")\n",
    "fit3 = Holt(air, damped=True).fit(smoothing_level=0.8, smoothing_slope=0.2)\n",
    "fcast3 = fit3.forecast(5).rename(\"Additive damped trend\")\n",
    "\n",
    "ax = air.plot(color=\"black\", marker=\"o\", figsize=(12,8))\n",
    "fit1.fittedvalues.plot(ax=ax, color='blue')\n",
    "fcast1.plot(ax=ax, color='blue', marker=\"o\", legend=True)\n",
    "fit2.fittedvalues.plot(ax=ax, color='red')\n",
    "fcast2.plot(ax=ax, color='red', marker=\"o\", legend=True)\n",
    "fit3.fittedvalues.plot(ax=ax, color='green')\n",
    "fcast3.plot(ax=ax, color='green', marker=\"o\", legend=True)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Seasonally adjusted data\n",
    "Lets look at some seasonally adjusted livestock data. We fit five Holt's models.\n",
    "The below table allows us to compare results when we use exponential versus additive and damped versus non-damped.\n",
    " \n",
    "Note: ```fit4``` does not allow the parameter $\\phi$ to be optimized by providing a fixed value of $\\phi=0.98$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-12-07T12:39:16.605618Z",
     "start_time": "2017-12-07T12:39:16.116424Z"
    }
   },
   "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>SES</th>\n",
       "      <th>Holt's</th>\n",
       "      <th>Exponential</th>\n",
       "      <th>Additive</th>\n",
       "      <th>Multiplicative</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>$\\alpha$</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.974306</td>\n",
       "      <td>0.977634</td>\n",
       "      <td>0.978825</td>\n",
       "      <td>0.974909</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>$\\beta$</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>$\\phi$</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.980000</td>\n",
       "      <td>0.981647</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>$l_0$</th>\n",
       "      <td>263.918414</td>\n",
       "      <td>258.882637</td>\n",
       "      <td>260.341678</td>\n",
       "      <td>257.354986</td>\n",
       "      <td>258.951988</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>$b_0$</th>\n",
       "      <td>NaN</td>\n",
       "      <td>5.010784</td>\n",
       "      <td>1.013780</td>\n",
       "      <td>6.644341</td>\n",
       "      <td>1.038144</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SSE</th>\n",
       "      <td>6761.350218</td>\n",
       "      <td>6004.138200</td>\n",
       "      <td>6104.194747</td>\n",
       "      <td>6036.555016</td>\n",
       "      <td>6081.995045</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  SES       Holt's  Exponential     Additive  Multiplicative\n",
       "$\\alpha$     1.000000     0.974306     0.977634     0.978825        0.974909\n",
       "$\\beta$           NaN     0.000000     0.000000     0.000000        0.000000\n",
       "$\\phi$            NaN          NaN          NaN     0.980000        0.981647\n",
       "$l_0$      263.918414   258.882637   260.341678   257.354986      258.951988\n",
       "$b_0$             NaN     5.010784     1.013780     6.644341        1.038144\n",
       "SSE       6761.350218  6004.138200  6104.194747  6036.555016     6081.995045"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fit1 = SimpleExpSmoothing(livestock2).fit()\n",
    "fit2 = Holt(livestock2).fit()\n",
    "fit3 = Holt(livestock2,exponential=True).fit()\n",
    "fit4 = Holt(livestock2,damped=True).fit(damping_slope=0.98)\n",
    "fit5 = Holt(livestock2,exponential=True,damped=True).fit()\n",
    "params = ['smoothing_level', 'smoothing_slope', 'damping_slope', 'initial_level', 'initial_slope']\n",
    "results=pd.DataFrame(index=[r\"$\\alpha$\",r\"$\\beta$\",r\"$\\phi$\",r\"$l_0$\",\"$b_0$\",\"SSE\"] ,columns=['SES', \"Holt's\",\"Exponential\", \"Additive\", \"Multiplicative\"])\n",
    "results[\"SES\"] =            [fit1.params[p] for p in params] + [fit1.sse]\n",
    "results[\"Holt's\"] =         [fit2.params[p] for p in params] + [fit2.sse]\n",
    "results[\"Exponential\"] =    [fit3.params[p] for p in params] + [fit3.sse]\n",
    "results[\"Additive\"] =       [fit4.params[p] for p in params] + [fit4.sse]\n",
    "results[\"Multiplicative\"] = [fit5.params[p] for p in params] + [fit5.sse]\n",
    "results"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plots of Seasonally Adjusted Data\n",
    "The following plots allow us to evaluate the level and slope/trend components of the above table's fits."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-12-07T12:39:17.105928Z",
     "start_time": "2017-12-07T12:39:16.607306Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Figure 7.4: Level and slope components for Holt’s linear trend method and the additive damped trend method.\n"
     ]
    }
   ],
   "source": [
    "for fit in [fit2,fit4]:\n",
    "    pd.DataFrame(np.c_[fit.level,fit.slope]).rename(\n",
    "        columns={0:'level',1:'slope'}).plot(subplots=True)\n",
    "plt.show()\n",
    "print('Figure 7.4: Level and slope components for Holt’s linear trend method and the additive damped trend method.')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Comparison\n",
    "Here we plot a comparison Simple Exponential Smoothing and Holt's Methods for various additive, exponential and damped combinations. All of the models parameters will be optimized by statsmodels."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-12-07T12:39:18.038995Z",
     "start_time": "2017-12-07T12:39:17.108323Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Figure 7.5: Forecasting livestock, sheep in Asia: comparing forecasting performance of non-seasonal methods.\n"
     ]
    }
   ],
   "source": [
    "fit1 = SimpleExpSmoothing(livestock2).fit()\n",
    "fcast1 = fit1.forecast(9).rename(\"SES\")\n",
    "fit2 = Holt(livestock2).fit()\n",
    "fcast2 = fit2.forecast(9).rename(\"Holt's\")\n",
    "fit3 = Holt(livestock2, exponential=True).fit()\n",
    "fcast3 = fit3.forecast(9).rename(\"Exponential\")\n",
    "fit4 = Holt(livestock2, damped=True).fit(damping_slope=0.98)\n",
    "fcast4 = fit4.forecast(9).rename(\"Additive Damped\")\n",
    "fit5 = Holt(livestock2, exponential=True, damped=True).fit()\n",
    "fcast5 = fit5.forecast(9).rename(\"Multiplicative Damped\")\n",
    "\n",
    "ax = livestock2.plot(color=\"black\", marker=\"o\", figsize=(12,8))\n",
    "livestock3.plot(ax=ax, color=\"black\", marker=\"o\", legend=False)\n",
    "fcast1.plot(ax=ax, color='red', legend=True)\n",
    "fcast2.plot(ax=ax, color='green', legend=True)\n",
    "fcast3.plot(ax=ax, color='blue', legend=True)\n",
    "fcast4.plot(ax=ax, color='cyan', legend=True)\n",
    "fcast5.plot(ax=ax, color='magenta', legend=True)\n",
    "ax.set_ylabel('Livestock, sheep in Asia (millions)')\n",
    "plt.show()\n",
    "print('Figure 7.5: Forecasting livestock, sheep in Asia: comparing forecasting performance of non-seasonal methods.')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-10-05T09:40:15.958575Z",
     "start_time": "2017-10-05T09:40:15.615Z"
    }
   },
   "source": [
    "## Holt's Winters Seasonal\n",
    "Finally we are able to run full Holt's Winters Seasonal Exponential Smoothing  including a trend component and a seasonal component.\n",
    "statsmodels allows for all the combinations including as shown in the examples below:\n",
    "1. ```fit1``` additive trend, additive seasonal of period ```season_length=4``` and the use of a Box-Cox transformation.\n",
    "1. ```fit2``` additive trend, multiplicative seasonal of period ```season_length=4``` and the use of a Box-Cox transformation..\n",
    "1. ```fit3``` additive damped trend, additive seasonal of period ```season_length=4``` and the use of a Box-Cox transformation.\n",
    "1. ```fit4``` additive damped trend, multiplicative seasonal of period ```season_length=4``` and the use of a Box-Cox transformation.\n",
    "\n",
    "The plot shows the results and forecast for ```fit1``` and ```fit2```.\n",
    "The table allows us to compare the results and parameterizations."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-12-07T12:39:28.375871Z",
     "start_time": "2017-12-07T12:39:18.040674Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/lib/python3/dist-packages/statsmodels/tsa/holtwinters.py:725: RuntimeWarning: invalid value encountered in less_equal\n",
      "  loc = initial_p <= lb\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/lib/python3/dist-packages/statsmodels/tsa/holtwinters.py:743: ConvergenceWarning: Optimization failed to converge. Check mle_retvals.\n",
      "  warn(\"Optimization failed to converge. Check mle_retvals.\",\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Figure 7.6: Forecasting international visitor nights in Australia using Holt-Winters method with both additive and multiplicative seasonality.\n"
     ]
    },
    {
     "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>Additive</th>\n",
       "      <th>Multiplicative</th>\n",
       "      <th>Additive Dam</th>\n",
       "      <th>Multiplica Dam</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>$\\alpha$</th>\n",
       "      <td>4.546459e-01</td>\n",
       "      <td>3.659223e-01</td>\n",
       "      <td>7.771267e-15</td>\n",
       "      <td>0.000189</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>$\\beta$</th>\n",
       "      <td>7.794133e-09</td>\n",
       "      <td>7.316443e-19</td>\n",
       "      <td>5.975782e-20</td>\n",
       "      <td>0.000189</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>$\\phi$</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.446193e-01</td>\n",
       "      <td>0.913789</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>$\\gamma$</th>\n",
       "      <td>5.243837e-01</td>\n",
       "      <td>1.060914e-12</td>\n",
       "      <td>4.955475e-14</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>$l_0$</th>\n",
       "      <td>1.421754e+01</td>\n",
       "      <td>1.454811e+01</td>\n",
       "      <td>1.417767e+01</td>\n",
       "      <td>14.534881</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>$b_0$</th>\n",
       "      <td>1.307698e-01</td>\n",
       "      <td>1.661090e-01</td>\n",
       "      <td>2.399716e-01</td>\n",
       "      <td>0.484079</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SSE</th>\n",
       "      <td>5.001687e+01</td>\n",
       "      <td>4.307029e+01</td>\n",
       "      <td>3.530344e+01</td>\n",
       "      <td>39.666447</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Additive  Multiplicative  Additive Dam  Multiplica Dam\n",
       "$\\alpha$  4.546459e-01    3.659223e-01  7.771267e-15        0.000189\n",
       "$\\beta$   7.794133e-09    7.316443e-19  5.975782e-20        0.000189\n",
       "$\\phi$             NaN             NaN  9.446193e-01        0.913789\n",
       "$\\gamma$  5.243837e-01    1.060914e-12  4.955475e-14        0.000000\n",
       "$l_0$     1.421754e+01    1.454811e+01  1.417767e+01       14.534881\n",
       "$b_0$     1.307698e-01    1.661090e-01  2.399716e-01        0.484079\n",
       "SSE       5.001687e+01    4.307029e+01  3.530344e+01       39.666447"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fit1 = ExponentialSmoothing(aust, seasonal_periods=4, trend='add', seasonal='add').fit(use_boxcox=True)\n",
    "fit2 = ExponentialSmoothing(aust, seasonal_periods=4, trend='add', seasonal='mul').fit(use_boxcox=True)\n",
    "fit3 = ExponentialSmoothing(aust, seasonal_periods=4, trend='add', seasonal='add', damped=True).fit(use_boxcox=True)\n",
    "fit4 = ExponentialSmoothing(aust, seasonal_periods=4, trend='add', seasonal='mul', damped=True).fit(use_boxcox=True)\n",
    "results=pd.DataFrame(index=[r\"$\\alpha$\",r\"$\\beta$\",r\"$\\phi$\",r\"$\\gamma$\",r\"$l_0$\",\"$b_0$\",\"SSE\"])\n",
    "params = ['smoothing_level', 'smoothing_slope', 'damping_slope', 'smoothing_seasonal', 'initial_level', 'initial_slope']\n",
    "results[\"Additive\"]       = [fit1.params[p] for p in params] + [fit1.sse]\n",
    "results[\"Multiplicative\"] = [fit2.params[p] for p in params] + [fit2.sse]\n",
    "results[\"Additive Dam\"]   = [fit3.params[p] for p in params] + [fit3.sse]\n",
    "results[\"Multiplica Dam\"] = [fit4.params[p] for p in params] + [fit4.sse]\n",
    "\n",
    "ax = aust.plot(figsize=(10,6), marker='o', color='black', title=\"Forecasts from Holt-Winters' multiplicative method\" )\n",
    "ax.set_ylabel(\"International visitor night in Australia (millions)\")\n",
    "ax.set_xlabel(\"Year\")\n",
    "fit1.fittedvalues.plot(ax=ax, style='--', color='red')\n",
    "fit2.fittedvalues.plot(ax=ax, style='--', color='green')\n",
    "\n",
    "fit1.forecast(8).rename('Holt-Winters (add-add-seasonal)').plot(ax=ax, style='--', marker='o', color='red', legend=True)\n",
    "fit2.forecast(8).rename('Holt-Winters (add-mul-seasonal)').plot(ax=ax, style='--', marker='o', color='green', legend=True)\n",
    "\n",
    "plt.show()\n",
    "print(\"Figure 7.6: Forecasting international visitor nights in Australia using Holt-Winters method with both additive and multiplicative seasonality.\")\n",
    "\n",
    "results"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### The Internals\n",
    "It is possible to get at the internals of the Exponential Smoothing models. \n",
    "\n",
    "Here we show some tables that allow you to view side by side the original values $y_t$, the level $l_t$, the trend $b_t$, the season $s_t$ and the fitted values $\\hat{y}_t$."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-12-07T12:39:28.399765Z",
     "start_time": "2017-12-07T12:39:28.377215Z"
    }
   },
   "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>$\\hat{y}_t$</th>\n",
       "      <th>$b_t$</th>\n",
       "      <th>$l_t$</th>\n",
       "      <th>$s_t$</th>\n",
       "      <th>$y_t$</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2005-01-01</th>\n",
       "      <td>41.721301</td>\n",
       "      <td>-34.969418</td>\n",
       "      <td>49.317727</td>\n",
       "      <td>-7.593396</td>\n",
       "      <td>41.7275</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2005-04-01</th>\n",
       "      <td>24.190405</td>\n",
       "      <td>-35.452731</td>\n",
       "      <td>49.932372</td>\n",
       "      <td>-25.834541</td>\n",
       "      <td>24.0418</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2005-07-01</th>\n",
       "      <td>31.460402</td>\n",
       "      <td>-36.532791</td>\n",
       "      <td>51.126130</td>\n",
       "      <td>-19.182969</td>\n",
       "      <td>32.3281</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2005-10-01</th>\n",
       "      <td>36.634633</td>\n",
       "      <td>-37.397668</td>\n",
       "      <td>52.210116</td>\n",
       "      <td>-15.209764</td>\n",
       "      <td>37.3287</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006-01-01</th>\n",
       "      <td>45.097596</td>\n",
       "      <td>-38.467294</td>\n",
       "      <td>53.476795</td>\n",
       "      <td>-7.837304</td>\n",
       "      <td>46.2132</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006-04-01</th>\n",
       "      <td>27.191816</td>\n",
       "      <td>-40.276313</td>\n",
       "      <td>55.513851</td>\n",
       "      <td>-27.017281</td>\n",
       "      <td>29.3463</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006-07-01</th>\n",
       "      <td>36.544210</td>\n",
       "      <td>-40.625014</td>\n",
       "      <td>56.224439</td>\n",
       "      <td>-19.713825</td>\n",
       "      <td>36.4829</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006-10-01</th>\n",
       "      <td>41.449390</td>\n",
       "      <td>-42.041777</td>\n",
       "      <td>57.766084</td>\n",
       "      <td>-15.523320</td>\n",
       "      <td>42.9777</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007-01-01</th>\n",
       "      <td>50.934403</td>\n",
       "      <td>-41.543765</td>\n",
       "      <td>57.536686</td>\n",
       "      <td>-7.588718</td>\n",
       "      <td>48.9015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007-04-01</th>\n",
       "      <td>31.418131</td>\n",
       "      <td>-42.197821</td>\n",
       "      <td>58.150971</td>\n",
       "      <td>-26.874297</td>\n",
       "      <td>31.1802</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007-07-01</th>\n",
       "      <td>38.718290</td>\n",
       "      <td>-42.303417</td>\n",
       "      <td>58.362916</td>\n",
       "      <td>-20.191886</td>\n",
       "      <td>37.7179</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007-10-01</th>\n",
       "      <td>44.140504</td>\n",
       "      <td>-41.085655</td>\n",
       "      <td>57.181734</td>\n",
       "      <td>-14.982807</td>\n",
       "      <td>40.4202</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008-01-01</th>\n",
       "      <td>49.315669</td>\n",
       "      <td>-42.961440</td>\n",
       "      <td>58.852914</td>\n",
       "      <td>-8.619795</td>\n",
       "      <td>51.2069</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008-04-01</th>\n",
       "      <td>32.306929</td>\n",
       "      <td>-43.186468</td>\n",
       "      <td>59.366898</td>\n",
       "      <td>-27.309123</td>\n",
       "      <td>31.8872</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008-07-01</th>\n",
       "      <td>39.207416</td>\n",
       "      <td>-44.826955</td>\n",
       "      <td>61.095541</td>\n",
       "      <td>-20.925483</td>\n",
       "      <td>40.9783</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008-10-01</th>\n",
       "      <td>44.551342</td>\n",
       "      <td>-44.899323</td>\n",
       "      <td>61.462001</td>\n",
       "      <td>-17.319727</td>\n",
       "      <td>43.7725</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009-01-01</th>\n",
       "      <td>54.357955</td>\n",
       "      <td>-46.192151</td>\n",
       "      <td>62.816712</td>\n",
       "      <td>-7.881574</td>\n",
       "      <td>55.5586</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009-04-01</th>\n",
       "      <td>35.153811</td>\n",
       "      <td>-45.980030</td>\n",
       "      <td>62.831978</td>\n",
       "      <td>-28.447763</td>\n",
       "      <td>33.8509</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009-07-01</th>\n",
       "      <td>43.066343</td>\n",
       "      <td>-46.227986</td>\n",
       "      <td>63.082485</td>\n",
       "      <td>-20.550579</td>\n",
       "      <td>42.0764</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009-10-01</th>\n",
       "      <td>45.871156</td>\n",
       "      <td>-46.852335</td>\n",
       "      <td>63.748644</td>\n",
       "      <td>-17.997619</td>\n",
       "      <td>45.6423</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-01</th>\n",
       "      <td>57.166477</td>\n",
       "      <td>-48.770316</td>\n",
       "      <td>65.777461</td>\n",
       "      <td>-7.372023</td>\n",
       "      <td>59.7668</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-04-01</th>\n",
       "      <td>36.761409</td>\n",
       "      <td>-48.308126</td>\n",
       "      <td>65.649780</td>\n",
       "      <td>-29.816638</td>\n",
       "      <td>35.1919</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-07-01</th>\n",
       "      <td>44.932417</td>\n",
       "      <td>-48.798443</td>\n",
       "      <td>66.119177</td>\n",
       "      <td>-21.517279</td>\n",
       "      <td>44.3197</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-10-01</th>\n",
       "      <td>48.399507</td>\n",
       "      <td>-49.269581</td>\n",
       "      <td>66.667135</td>\n",
       "      <td>-18.522045</td>\n",
       "      <td>47.9137</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-01-01</th>\n",
       "      <td>61.337759</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-04-01</th>\n",
       "      <td>37.242799</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-07-01</th>\n",
       "      <td>46.842530</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-10-01</th>\n",
       "      <td>51.005064</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-01</th>\n",
       "      <td>64.470463</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-04-01</th>\n",
       "      <td>39.776728</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-07-01</th>\n",
       "      <td>49.635627</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-10-01</th>\n",
       "      <td>53.901136</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            $\\hat{y}_t$      $b_t$      $l_t$      $s_t$    $y_t$\n",
       "2005-01-01    41.721301 -34.969418  49.317727  -7.593396  41.7275\n",
       "2005-04-01    24.190405 -35.452731  49.932372 -25.834541  24.0418\n",
       "2005-07-01    31.460402 -36.532791  51.126130 -19.182969  32.3281\n",
       "2005-10-01    36.634633 -37.397668  52.210116 -15.209764  37.3287\n",
       "2006-01-01    45.097596 -38.467294  53.476795  -7.837304  46.2132\n",
       "2006-04-01    27.191816 -40.276313  55.513851 -27.017281  29.3463\n",
       "2006-07-01    36.544210 -40.625014  56.224439 -19.713825  36.4829\n",
       "2006-10-01    41.449390 -42.041777  57.766084 -15.523320  42.9777\n",
       "2007-01-01    50.934403 -41.543765  57.536686  -7.588718  48.9015\n",
       "2007-04-01    31.418131 -42.197821  58.150971 -26.874297  31.1802\n",
       "2007-07-01    38.718290 -42.303417  58.362916 -20.191886  37.7179\n",
       "2007-10-01    44.140504 -41.085655  57.181734 -14.982807  40.4202\n",
       "2008-01-01    49.315669 -42.961440  58.852914  -8.619795  51.2069\n",
       "2008-04-01    32.306929 -43.186468  59.366898 -27.309123  31.8872\n",
       "2008-07-01    39.207416 -44.826955  61.095541 -20.925483  40.9783\n",
       "2008-10-01    44.551342 -44.899323  61.462001 -17.319727  43.7725\n",
       "2009-01-01    54.357955 -46.192151  62.816712  -7.881574  55.5586\n",
       "2009-04-01    35.153811 -45.980030  62.831978 -28.447763  33.8509\n",
       "2009-07-01    43.066343 -46.227986  63.082485 -20.550579  42.0764\n",
       "2009-10-01    45.871156 -46.852335  63.748644 -17.997619  45.6423\n",
       "2010-01-01    57.166477 -48.770316  65.777461  -7.372023  59.7668\n",
       "2010-04-01    36.761409 -48.308126  65.649780 -29.816638  35.1919\n",
       "2010-07-01    44.932417 -48.798443  66.119177 -21.517279  44.3197\n",
       "2010-10-01    48.399507 -49.269581  66.667135 -18.522045  47.9137\n",
       "2011-01-01    61.337759        NaN        NaN        NaN      NaN\n",
       "2011-04-01    37.242799        NaN        NaN        NaN      NaN\n",
       "2011-07-01    46.842530        NaN        NaN        NaN      NaN\n",
       "2011-10-01    51.005064        NaN        NaN        NaN      NaN\n",
       "2012-01-01    64.470463        NaN        NaN        NaN      NaN\n",
       "2012-04-01    39.776728        NaN        NaN        NaN      NaN\n",
       "2012-07-01    49.635627        NaN        NaN        NaN      NaN\n",
       "2012-10-01    53.901136        NaN        NaN        NaN      NaN"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame(np.c_[aust, fit1.level, fit1.slope, fit1.season, fit1.fittedvalues],\n",
    "                  columns=[r'$y_t$',r'$l_t$',r'$b_t$',r'$s_t$',r'$\\hat{y}_t$'],index=aust.index)\n",
    "df.append(fit1.forecast(8).rename(r'$\\hat{y}_t$').to_frame(), sort=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-12-07T12:39:28.574783Z",
     "start_time": "2017-12-07T12:39:28.401234Z"
    }
   },
   "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>$\\hat{y}_t$</th>\n",
       "      <th>$b_t$</th>\n",
       "      <th>$l_t$</th>\n",
       "      <th>$s_t$</th>\n",
       "      <th>$y_t$</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2005-01-01</th>\n",
       "      <td>41.860019</td>\n",
       "      <td>-36.529901</td>\n",
       "      <td>51.244121</td>\n",
       "      <td>0.815914</td>\n",
       "      <td>41.7275</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2005-04-01</th>\n",
       "      <td>25.838329</td>\n",
       "      <td>-35.866598</td>\n",
       "      <td>50.735921</td>\n",
       "      <td>0.495384</td>\n",
       "      <td>24.0418</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2005-07-01</th>\n",
       "      <td>31.659094</td>\n",
       "      <td>-37.283662</td>\n",
       "      <td>52.060074</td>\n",
       "      <td>0.613001</td>\n",
       "      <td>32.3281</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2005-10-01</th>\n",
       "      <td>35.189675</td>\n",
       "      <td>-39.168718</td>\n",
       "      <td>54.186389</td>\n",
       "      <td>0.664203</td>\n",
       "      <td>37.3287</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006-01-01</th>\n",
       "      <td>44.928949</td>\n",
       "      <td>-40.305567</td>\n",
       "      <td>55.705171</td>\n",
       "      <td>0.815073</td>\n",
       "      <td>46.2132</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006-04-01</th>\n",
       "      <td>27.933400</td>\n",
       "      <td>-42.087113</td>\n",
       "      <td>57.755569</td>\n",
       "      <td>0.493065</td>\n",
       "      <td>29.3463</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006-07-01</th>\n",
       "      <td>35.824218</td>\n",
       "      <td>-43.100038</td>\n",
       "      <td>59.126477</td>\n",
       "      <td>0.610108</td>\n",
       "      <td>36.4829</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006-10-01</th>\n",
       "      <td>39.768767</td>\n",
       "      <td>-45.643430</td>\n",
       "      <td>61.906160</td>\n",
       "      <td>0.661726</td>\n",
       "      <td>42.9777</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007-01-01</th>\n",
       "      <td>51.174397</td>\n",
       "      <td>-45.120736</td>\n",
       "      <td>61.855424</td>\n",
       "      <td>0.813614</td>\n",
       "      <td>48.9015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007-04-01</th>\n",
       "      <td>30.814215</td>\n",
       "      <td>-46.408183</td>\n",
       "      <td>63.134340</td>\n",
       "      <td>0.490309</td>\n",
       "      <td>31.1802</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007-07-01</th>\n",
       "      <td>39.009005</td>\n",
       "      <td>-46.386264</td>\n",
       "      <td>63.326558</td>\n",
       "      <td>0.608240</td>\n",
       "      <td>37.7179</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007-10-01</th>\n",
       "      <td>42.486130</td>\n",
       "      <td>-46.170456</td>\n",
       "      <td>63.142772</td>\n",
       "      <td>0.660464</td>\n",
       "      <td>40.4202</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008-01-01</th>\n",
       "      <td>52.174114</td>\n",
       "      <td>-46.759046</td>\n",
       "      <td>63.700745</td>\n",
       "      <td>0.813407</td>\n",
       "      <td>51.2069</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008-04-01</th>\n",
       "      <td>31.677045</td>\n",
       "      <td>-47.835416</td>\n",
       "      <td>64.869947</td>\n",
       "      <td>0.489565</td>\n",
       "      <td>31.8872</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008-07-01</th>\n",
       "      <td>40.035566</td>\n",
       "      <td>-49.239072</td>\n",
       "      <td>66.466994</td>\n",
       "      <td>0.607690</td>\n",
       "      <td>40.9783</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008-10-01</th>\n",
       "      <td>44.516066</td>\n",
       "      <td>-49.574721</td>\n",
       "      <td>67.064385</td>\n",
       "      <td>0.659603</td>\n",
       "      <td>43.7725</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009-01-01</th>\n",
       "      <td>55.343283</td>\n",
       "      <td>-50.601802</td>\n",
       "      <td>68.188673</td>\n",
       "      <td>0.812789</td>\n",
       "      <td>55.5586</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009-04-01</th>\n",
       "      <td>33.772845</td>\n",
       "      <td>-51.515006</td>\n",
       "      <td>69.283811</td>\n",
       "      <td>0.487890</td>\n",
       "      <td>33.8509</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009-07-01</th>\n",
       "      <td>42.644046</td>\n",
       "      <td>-52.025095</td>\n",
       "      <td>69.969874</td>\n",
       "      <td>0.606390</td>\n",
       "      <td>42.0764</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009-10-01</th>\n",
       "      <td>46.778562</td>\n",
       "      <td>-52.310277</td>\n",
       "      <td>70.364685</td>\n",
       "      <td>0.658714</td>\n",
       "      <td>45.6423</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-01</th>\n",
       "      <td>58.009042</td>\n",
       "      <td>-54.093333</td>\n",
       "      <td>72.210619</td>\n",
       "      <td>0.812312</td>\n",
       "      <td>59.7668</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-04-01</th>\n",
       "      <td>35.648092</td>\n",
       "      <td>-54.499545</td>\n",
       "      <td>72.908816</td>\n",
       "      <td>0.486531</td>\n",
       "      <td>35.1919</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-07-01</th>\n",
       "      <td>44.784147</td>\n",
       "      <td>-55.163485</td>\n",
       "      <td>73.682353</td>\n",
       "      <td>0.605416</td>\n",
       "      <td>44.3197</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-10-01</th>\n",
       "      <td>49.174608</td>\n",
       "      <td>-55.389037</td>\n",
       "      <td>74.028798</td>\n",
       "      <td>0.657846</td>\n",
       "      <td>47.9137</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-01-01</th>\n",
       "      <td>60.967374</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-04-01</th>\n",
       "      <td>36.993488</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-07-01</th>\n",
       "      <td>46.712003</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-10-01</th>\n",
       "      <td>51.482604</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-01</th>\n",
       "      <td>64.455898</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-04-01</th>\n",
       "      <td>39.016675</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-07-01</th>\n",
       "      <td>49.291081</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-10-01</th>\n",
       "      <td>54.319649</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            $\\hat{y}_t$      $b_t$      $l_t$     $s_t$    $y_t$\n",
       "2005-01-01    41.860019 -36.529901  51.244121  0.815914  41.7275\n",
       "2005-04-01    25.838329 -35.866598  50.735921  0.495384  24.0418\n",
       "2005-07-01    31.659094 -37.283662  52.060074  0.613001  32.3281\n",
       "2005-10-01    35.189675 -39.168718  54.186389  0.664203  37.3287\n",
       "2006-01-01    44.928949 -40.305567  55.705171  0.815073  46.2132\n",
       "2006-04-01    27.933400 -42.087113  57.755569  0.493065  29.3463\n",
       "2006-07-01    35.824218 -43.100038  59.126477  0.610108  36.4829\n",
       "2006-10-01    39.768767 -45.643430  61.906160  0.661726  42.9777\n",
       "2007-01-01    51.174397 -45.120736  61.855424  0.813614  48.9015\n",
       "2007-04-01    30.814215 -46.408183  63.134340  0.490309  31.1802\n",
       "2007-07-01    39.009005 -46.386264  63.326558  0.608240  37.7179\n",
       "2007-10-01    42.486130 -46.170456  63.142772  0.660464  40.4202\n",
       "2008-01-01    52.174114 -46.759046  63.700745  0.813407  51.2069\n",
       "2008-04-01    31.677045 -47.835416  64.869947  0.489565  31.8872\n",
       "2008-07-01    40.035566 -49.239072  66.466994  0.607690  40.9783\n",
       "2008-10-01    44.516066 -49.574721  67.064385  0.659603  43.7725\n",
       "2009-01-01    55.343283 -50.601802  68.188673  0.812789  55.5586\n",
       "2009-04-01    33.772845 -51.515006  69.283811  0.487890  33.8509\n",
       "2009-07-01    42.644046 -52.025095  69.969874  0.606390  42.0764\n",
       "2009-10-01    46.778562 -52.310277  70.364685  0.658714  45.6423\n",
       "2010-01-01    58.009042 -54.093333  72.210619  0.812312  59.7668\n",
       "2010-04-01    35.648092 -54.499545  72.908816  0.486531  35.1919\n",
       "2010-07-01    44.784147 -55.163485  73.682353  0.605416  44.3197\n",
       "2010-10-01    49.174608 -55.389037  74.028798  0.657846  47.9137\n",
       "2011-01-01    60.967374        NaN        NaN       NaN      NaN\n",
       "2011-04-01    36.993488        NaN        NaN       NaN      NaN\n",
       "2011-07-01    46.712003        NaN        NaN       NaN      NaN\n",
       "2011-10-01    51.482604        NaN        NaN       NaN      NaN\n",
       "2012-01-01    64.455898        NaN        NaN       NaN      NaN\n",
       "2012-04-01    39.016675        NaN        NaN       NaN      NaN\n",
       "2012-07-01    49.291081        NaN        NaN       NaN      NaN\n",
       "2012-10-01    54.319649        NaN        NaN       NaN      NaN"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame(np.c_[aust, fit2.level, fit2.slope, fit2.season, fit2.fittedvalues], \n",
    "                  columns=[r'$y_t$',r'$l_t$',r'$b_t$',r'$s_t$',r'$\\hat{y}_t$'],index=aust.index)\n",
    "df.append(fit2.forecast(8).rename(r'$\\hat{y}_t$').to_frame(), sort=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally lets look at the levels, slopes/trends and seasonal components of the models."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-12-07T12:39:29.636548Z",
     "start_time": "2017-12-07T12:39:28.576279Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x576 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "states1 = pd.DataFrame(np.c_[fit1.level, fit1.slope, fit1.season], columns=['level','slope','seasonal'], index=aust.index)\n",
    "states2 = pd.DataFrame(np.c_[fit2.level, fit2.slope, fit2.season], columns=['level','slope','seasonal'], index=aust.index)\n",
    "fig, [[ax1, ax4],[ax2, ax5], [ax3, ax6]] = plt.subplots(3, 2, figsize=(12,8))\n",
    "states1[['level']].plot(ax=ax1)\n",
    "states1[['slope']].plot(ax=ax2)\n",
    "states1[['seasonal']].plot(ax=ax3)\n",
    "states2[['level']].plot(ax=ax4)\n",
    "states2[['slope']].plot(ax=ax5)\n",
    "states2[['seasonal']].plot(ax=ax6)\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "hide_input": false,
  "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": "3.8.2rc2"
  },
  "latex_envs": {
   "LaTeX_envs_menu_present": true,
   "autocomplete": true,
   "bibliofile": "biblio.bib",
   "cite_by": "apalike",
   "current_citInitial": 1,
   "eqLabelWithNumbers": true,
   "eqNumInitial": 1,
   "hotkeys": {
    "equation": "Ctrl-E",
    "itemize": "Ctrl-I"
   },
   "labels_anchors": false,
   "latex_user_defs": false,
   "report_style_numbering": false,
   "user_envs_cfg": false
  },
  "toc": {
   "colors": {
    "hover_highlight": "#DAA520",
    "navigate_num": "#000000",
    "navigate_text": "#333333",
    "running_highlight": "#FF0000",
    "selected_highlight": "#FFD700",
    "sidebar_border": "#EEEEEE",
    "wrapper_background": "#FFFFFF"
   },
   "moveMenuLeft": true,
   "nav_menu": {
    "height": "98px",
    "width": "252px"
   },
   "navigate_menu": true,
   "number_sections": false,
   "sideBar": true,
   "threshold": 4,
   "toc_cell": false,
   "toc_section_display": "block",
   "toc_window_display": true,
   "widenNotebook": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}
