{
 "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": [
    {
     "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 = 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": [
    {
     "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": {
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       "    .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>258.882588</td>\n",
       "      <td>260.341540</td>\n",
       "      <td>257.354831</td>\n",
       "      <td>258.952022</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>$b_0$</th>\n",
       "      <td>NaN</td>\n",
       "      <td>5.010773</td>\n",
       "      <td>1.013780</td>\n",
       "      <td>6.644374</td>\n",
       "      <td>1.038143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SSE</th>\n",
       "      <td>6761.350218</td>\n",
       "      <td>6004.138200</td>\n",
       "      <td>6104.194746</td>\n",
       "      <td>6036.555017</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   258.882588   260.341540   257.354831      258.952022\n",
       "$b_0$             NaN     5.010773     1.013780     6.644374        1.038143\n",
       "SSE       6761.350218  6004.138200  6104.194746  6036.555017     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": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/lib/python3/dist-packages/pandas/plotting/_matplotlib/tools.py:307: MatplotlibDeprecationWarning: \n",
      "The rowNum attribute was deprecated in Matplotlib 3.2 and will be removed two minor releases later. Use ax.get_subplotspec().rowspan.start instead.\n",
      "  layout[ax.rowNum, ax.colNum] = ax.get_visible()\n",
      "/usr/lib/python3/dist-packages/pandas/plotting/_matplotlib/tools.py:307: MatplotlibDeprecationWarning: \n",
      "The colNum attribute was deprecated in Matplotlib 3.2 and will be removed two minor releases later. Use ax.get_subplotspec().colspan.start instead.\n",
      "  layout[ax.rowNum, ax.colNum] = ax.get_visible()\n",
      "/usr/lib/python3/dist-packages/pandas/plotting/_matplotlib/tools.py:313: MatplotlibDeprecationWarning: \n",
      "The rowNum attribute was deprecated in Matplotlib 3.2 and will be removed two minor releases later. Use ax.get_subplotspec().rowspan.start instead.\n",
      "  if not layout[ax.rowNum + 1, ax.colNum]:\n",
      "/usr/lib/python3/dist-packages/pandas/plotting/_matplotlib/tools.py:313: MatplotlibDeprecationWarning: \n",
      "The colNum attribute was deprecated in Matplotlib 3.2 and will be removed two minor releases later. Use ax.get_subplotspec().colspan.start instead.\n",
      "  if not layout[ax.rowNum + 1, ax.colNum]:\n",
      "/usr/lib/python3/dist-packages/pandas/plotting/_matplotlib/tools.py:307: MatplotlibDeprecationWarning: \n",
      "The rowNum attribute was deprecated in Matplotlib 3.2 and will be removed two minor releases later. Use ax.get_subplotspec().rowspan.start instead.\n",
      "  layout[ax.rowNum, ax.colNum] = ax.get_visible()\n",
      "/usr/lib/python3/dist-packages/pandas/plotting/_matplotlib/tools.py:307: MatplotlibDeprecationWarning: \n",
      "The colNum attribute was deprecated in Matplotlib 3.2 and will be removed two minor releases later. Use ax.get_subplotspec().colspan.start instead.\n",
      "  layout[ax.rowNum, ax.colNum] = ax.get_visible()\n",
      "/usr/lib/python3/dist-packages/pandas/plotting/_matplotlib/tools.py:313: MatplotlibDeprecationWarning: \n",
      "The rowNum attribute was deprecated in Matplotlib 3.2 and will be removed two minor releases later. Use ax.get_subplotspec().rowspan.start instead.\n",
      "  if not layout[ax.rowNum + 1, ax.colNum]:\n",
      "/usr/lib/python3/dist-packages/pandas/plotting/_matplotlib/tools.py:313: MatplotlibDeprecationWarning: \n",
      "The colNum attribute was deprecated in Matplotlib 3.2 and will be removed two minor releases later. Use ax.get_subplotspec().colspan.start instead.\n",
      "  if not layout[ax.rowNum + 1, ax.colNum]:\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/lib/python3/dist-packages/pandas/plotting/_matplotlib/tools.py:307: MatplotlibDeprecationWarning: \n",
      "The rowNum attribute was deprecated in Matplotlib 3.2 and will be removed two minor releases later. Use ax.get_subplotspec().rowspan.start instead.\n",
      "  layout[ax.rowNum, ax.colNum] = ax.get_visible()\n",
      "/usr/lib/python3/dist-packages/pandas/plotting/_matplotlib/tools.py:307: MatplotlibDeprecationWarning: \n",
      "The colNum attribute was deprecated in Matplotlib 3.2 and will be removed two minor releases later. Use ax.get_subplotspec().colspan.start instead.\n",
      "  layout[ax.rowNum, ax.colNum] = ax.get_visible()\n",
      "/usr/lib/python3/dist-packages/pandas/plotting/_matplotlib/tools.py:313: MatplotlibDeprecationWarning: \n",
      "The rowNum attribute was deprecated in Matplotlib 3.2 and will be removed two minor releases later. Use ax.get_subplotspec().rowspan.start instead.\n",
      "  if not layout[ax.rowNum + 1, ax.colNum]:\n",
      "/usr/lib/python3/dist-packages/pandas/plotting/_matplotlib/tools.py:313: MatplotlibDeprecationWarning: \n",
      "The colNum attribute was deprecated in Matplotlib 3.2 and will be removed two minor releases later. Use ax.get_subplotspec().colspan.start instead.\n",
      "  if not layout[ax.rowNum + 1, ax.colNum]:\n"
     ]
    },
    {
     "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": [
    {
     "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"
    },
    {
     "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",
      "/usr/lib/python3/dist-packages/statsmodels/tsa/holtwinters.py:731: RuntimeWarning: invalid value encountered in greater_equal\n",
      "  loc = initial_p >= ub\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>4.546196e-01</td>\n",
       "      <td>3.659017e-01</td>\n",
       "      <td>6.347043e-09</td>\n",
       "      <td>0.000184</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>$\\beta$</th>\n",
       "      <td>8.220629e-09</td>\n",
       "      <td>4.478251e-16</td>\n",
       "      <td>1.528369e-10</td>\n",
       "      <td>0.000184</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>$\\phi$</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.430768e-01</td>\n",
       "      <td>0.912999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>$\\gamma$</th>\n",
       "      <td>5.244087e-01</td>\n",
       "      <td>6.504759e-10</td>\n",
       "      <td>1.116856e-08</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>$l_0$</th>\n",
       "      <td>1.421752e+01</td>\n",
       "      <td>1.454895e+01</td>\n",
       "      <td>1.415804e+01</td>\n",
       "      <td>14.534950</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>$b_0$</th>\n",
       "      <td>1.307770e-01</td>\n",
       "      <td>1.661336e-01</td>\n",
       "      <td>2.455663e-01</td>\n",
       "      <td>0.486077</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SSE</th>\n",
       "      <td>5.001658e+01</td>\n",
       "      <td>4.306896e+01</td>\n",
       "      <td>3.527380e+01</td>\n",
       "      <td>39.825771</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Additive  Multiplicative  Additive Dam  Multiplica Dam\n",
       "$\\alpha$  4.546196e-01    3.659017e-01  6.347043e-09        0.000184\n",
       "$\\beta$   8.220629e-09    4.478251e-16  1.528369e-10        0.000184\n",
       "$\\phi$             NaN             NaN  9.430768e-01        0.912999\n",
       "$\\gamma$  5.244087e-01    6.504759e-10  1.116856e-08        0.000000\n",
       "$l_0$     1.421752e+01    1.454895e+01  1.415804e+01       14.534950\n",
       "$b_0$     1.307770e-01    1.661336e-01  2.455663e-01        0.486077\n",
       "SSE       5.001658e+01    4.306896e+01  3.527380e+01       39.825771"
      ]
     },
     "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>41.721220</td>\n",
       "      <td>-34.969407</td>\n",
       "      <td>49.317706</td>\n",
       "      <td>-7.593417</td>\n",
       "      <td>41.7275</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2005-04-01</th>\n",
       "      <td>24.190267</td>\n",
       "      <td>-35.452835</td>\n",
       "      <td>49.932480</td>\n",
       "      <td>-25.834695</td>\n",
       "      <td>24.0418</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2005-07-01</th>\n",
       "      <td>31.460552</td>\n",
       "      <td>-36.532800</td>\n",
       "      <td>51.126166</td>\n",
       "      <td>-19.182966</td>\n",
       "      <td>32.3281</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2005-10-01</th>\n",
       "      <td>36.634774</td>\n",
       "      <td>-37.397635</td>\n",
       "      <td>52.210097</td>\n",
       "      <td>-15.209698</td>\n",
       "      <td>37.3287</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006-01-01</th>\n",
       "      <td>45.097634</td>\n",
       "      <td>-38.467261</td>\n",
       "      <td>53.476766</td>\n",
       "      <td>-7.837286</td>\n",
       "      <td>46.2132</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006-04-01</th>\n",
       "      <td>27.191738</td>\n",
       "      <td>-40.276294</td>\n",
       "      <td>55.513834</td>\n",
       "      <td>-27.017370</td>\n",
       "      <td>29.3463</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006-07-01</th>\n",
       "      <td>36.544201</td>\n",
       "      <td>-40.625041</td>\n",
       "      <td>56.224470</td>\n",
       "      <td>-19.713858</td>\n",
       "      <td>36.4829</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006-10-01</th>\n",
       "      <td>41.449473</td>\n",
       "      <td>-42.041747</td>\n",
       "      <td>57.766067</td>\n",
       "      <td>-15.523310</td>\n",
       "      <td>42.9777</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007-01-01</th>\n",
       "      <td>50.934455</td>\n",
       "      <td>-41.543817</td>\n",
       "      <td>57.536743</td>\n",
       "      <td>-7.588691</td>\n",
       "      <td>48.9015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007-04-01</th>\n",
       "      <td>31.418215</td>\n",
       "      <td>-42.197861</td>\n",
       "      <td>58.151028</td>\n",
       "      <td>-26.874311</td>\n",
       "      <td>31.1802</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007-07-01</th>\n",
       "      <td>38.718346</td>\n",
       "      <td>-42.303500</td>\n",
       "      <td>58.363015</td>\n",
       "      <td>-20.191929</td>\n",
       "      <td>37.7179</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007-10-01</th>\n",
       "      <td>44.140644</td>\n",
       "      <td>-41.085811</td>\n",
       "      <td>57.181914</td>\n",
       "      <td>-14.982809</td>\n",
       "      <td>40.4202</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008-01-01</th>\n",
       "      <td>49.315827</td>\n",
       "      <td>-42.961495</td>\n",
       "      <td>58.853007</td>\n",
       "      <td>-8.619859</td>\n",
       "      <td>51.2069</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008-04-01</th>\n",
       "      <td>32.306991</td>\n",
       "      <td>-43.186558</td>\n",
       "      <td>59.367011</td>\n",
       "      <td>-27.309197</td>\n",
       "      <td>31.8872</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008-07-01</th>\n",
       "      <td>39.207465</td>\n",
       "      <td>-44.827003</td>\n",
       "      <td>61.095615</td>\n",
       "      <td>-20.925590</td>\n",
       "      <td>40.9783</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008-10-01</th>\n",
       "      <td>44.551290</td>\n",
       "      <td>-44.899471</td>\n",
       "      <td>61.462169</td>\n",
       "      <td>-17.319896</td>\n",
       "      <td>43.7725</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009-01-01</th>\n",
       "      <td>54.358065</td>\n",
       "      <td>-46.192241</td>\n",
       "      <td>62.816837</td>\n",
       "      <td>-7.881675</td>\n",
       "      <td>55.5586</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009-04-01</th>\n",
       "      <td>35.153854</td>\n",
       "      <td>-45.980189</td>\n",
       "      <td>62.832166</td>\n",
       "      <td>-28.447889</td>\n",
       "      <td>33.8509</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009-07-01</th>\n",
       "      <td>43.066474</td>\n",
       "      <td>-46.228139</td>\n",
       "      <td>63.082677</td>\n",
       "      <td>-20.550681</td>\n",
       "      <td>42.0764</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009-10-01</th>\n",
       "      <td>45.871213</td>\n",
       "      <td>-46.852509</td>\n",
       "      <td>63.748857</td>\n",
       "      <td>-17.997799</td>\n",
       "      <td>45.6423</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-01</th>\n",
       "      <td>57.166602</td>\n",
       "      <td>-48.770401</td>\n",
       "      <td>65.777589</td>\n",
       "      <td>-7.372156</td>\n",
       "      <td>59.7668</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-04-01</th>\n",
       "      <td>36.761396</td>\n",
       "      <td>-48.308330</td>\n",
       "      <td>65.650012</td>\n",
       "      <td>-29.816824</td>\n",
       "      <td>35.1919</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-07-01</th>\n",
       "      <td>44.932490</td>\n",
       "      <td>-48.798655</td>\n",
       "      <td>66.119434</td>\n",
       "      <td>-21.517484</td>\n",
       "      <td>44.3197</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-10-01</th>\n",
       "      <td>48.399583</td>\n",
       "      <td>-49.269809</td>\n",
       "      <td>66.667413</td>\n",
       "      <td>-18.522272</td>\n",
       "      <td>47.9137</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-01-01</th>\n",
       "      <td>61.337944</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>37.242903</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>46.842662</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>51.005276</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>64.470826</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>39.776976</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>49.635918</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>53.901514</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    41.721220 -34.969407  49.317706  -7.593417  41.7275\n",
       "2005-04-01    24.190267 -35.452835  49.932480 -25.834695  24.0418\n",
       "2005-07-01    31.460552 -36.532800  51.126166 -19.182966  32.3281\n",
       "2005-10-01    36.634774 -37.397635  52.210097 -15.209698  37.3287\n",
       "2006-01-01    45.097634 -38.467261  53.476766  -7.837286  46.2132\n",
       "2006-04-01    27.191738 -40.276294  55.513834 -27.017370  29.3463\n",
       "2006-07-01    36.544201 -40.625041  56.224470 -19.713858  36.4829\n",
       "2006-10-01    41.449473 -42.041747  57.766067 -15.523310  42.9777\n",
       "2007-01-01    50.934455 -41.543817  57.536743  -7.588691  48.9015\n",
       "2007-04-01    31.418215 -42.197861  58.151028 -26.874311  31.1802\n",
       "2007-07-01    38.718346 -42.303500  58.363015 -20.191929  37.7179\n",
       "2007-10-01    44.140644 -41.085811  57.181914 -14.982809  40.4202\n",
       "2008-01-01    49.315827 -42.961495  58.853007  -8.619859  51.2069\n",
       "2008-04-01    32.306991 -43.186558  59.367011 -27.309197  31.8872\n",
       "2008-07-01    39.207465 -44.827003  61.095615 -20.925590  40.9783\n",
       "2008-10-01    44.551290 -44.899471  61.462169 -17.319896  43.7725\n",
       "2009-01-01    54.358065 -46.192241  62.816837  -7.881675  55.5586\n",
       "2009-04-01    35.153854 -45.980189  62.832166 -28.447889  33.8509\n",
       "2009-07-01    43.066474 -46.228139  63.082677 -20.550681  42.0764\n",
       "2009-10-01    45.871213 -46.852509  63.748857 -17.997799  45.6423\n",
       "2010-01-01    57.166602 -48.770401  65.777589  -7.372156  59.7668\n",
       "2010-04-01    36.761396 -48.308330  65.650012 -29.816824  35.1919\n",
       "2010-07-01    44.932490 -48.798655  66.119434 -21.517484  44.3197\n",
       "2010-10-01    48.399583 -49.269809  66.667413 -18.522272  47.9137\n",
       "2011-01-01    61.337944        NaN        NaN        NaN      NaN\n",
       "2011-04-01    37.242903        NaN        NaN        NaN      NaN\n",
       "2011-07-01    46.842662        NaN        NaN        NaN      NaN\n",
       "2011-10-01    51.005276        NaN        NaN        NaN      NaN\n",
       "2012-01-01    64.470826        NaN        NaN        NaN      NaN\n",
       "2012-04-01    39.776976        NaN        NaN        NaN      NaN\n",
       "2012-07-01    49.635918        NaN        NaN        NaN      NaN\n",
       "2012-10-01    53.901514        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>41.861504</td>\n",
       "      <td>-36.533095</td>\n",
       "      <td>51.248179</td>\n",
       "      <td>0.815867</td>\n",
       "      <td>41.7275</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2005-04-01</th>\n",
       "      <td>25.839364</td>\n",
       "      <td>-35.869251</td>\n",
       "      <td>50.739339</td>\n",
       "      <td>0.495364</td>\n",
       "      <td>24.0418</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2005-07-01</th>\n",
       "      <td>31.659683</td>\n",
       "      <td>-37.286227</td>\n",
       "      <td>52.063290</td>\n",
       "      <td>0.612970</td>\n",
       "      <td>32.3281</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2005-10-01</th>\n",
       "      <td>35.189700</td>\n",
       "      <td>-39.171487</td>\n",
       "      <td>54.189765</td>\n",
       "      <td>0.664161</td>\n",
       "      <td>37.3287</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006-01-01</th>\n",
       "      <td>44.929289</td>\n",
       "      <td>-40.308330</td>\n",
       "      <td>55.708559</td>\n",
       "      <td>0.815027</td>\n",
       "      <td>46.2132</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006-04-01</th>\n",
       "      <td>27.934034</td>\n",
       "      <td>-42.089539</td>\n",
       "      <td>57.758615</td>\n",
       "      <td>0.493045</td>\n",
       "      <td>29.3463</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006-07-01</th>\n",
       "      <td>35.824367</td>\n",
       "      <td>-43.102604</td>\n",
       "      <td>59.129595</td>\n",
       "      <td>0.610077</td>\n",
       "      <td>36.4829</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006-10-01</th>\n",
       "      <td>39.768425</td>\n",
       "      <td>-45.646365</td>\n",
       "      <td>61.909655</td>\n",
       "      <td>0.661684</td>\n",
       "      <td>42.9777</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007-01-01</th>\n",
       "      <td>51.174461</td>\n",
       "      <td>-45.123756</td>\n",
       "      <td>61.859056</td>\n",
       "      <td>0.813568</td>\n",
       "      <td>48.9015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007-04-01</th>\n",
       "      <td>30.814845</td>\n",
       "      <td>-46.410853</td>\n",
       "      <td>63.137646</td>\n",
       "      <td>0.490289</td>\n",
       "      <td>31.1802</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007-07-01</th>\n",
       "      <td>39.009139</td>\n",
       "      <td>-46.389083</td>\n",
       "      <td>63.329952</td>\n",
       "      <td>0.608209</td>\n",
       "      <td>37.7179</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007-10-01</th>\n",
       "      <td>42.485786</td>\n",
       "      <td>-46.173619</td>\n",
       "      <td>63.146524</td>\n",
       "      <td>0.660422</td>\n",
       "      <td>40.4202</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008-01-01</th>\n",
       "      <td>52.174326</td>\n",
       "      <td>-46.762223</td>\n",
       "      <td>63.704572</td>\n",
       "      <td>0.813361</td>\n",
       "      <td>51.2069</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008-04-01</th>\n",
       "      <td>31.677731</td>\n",
       "      <td>-47.838216</td>\n",
       "      <td>64.873407</td>\n",
       "      <td>0.489546</td>\n",
       "      <td>31.8872</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008-07-01</th>\n",
       "      <td>40.035737</td>\n",
       "      <td>-49.242003</td>\n",
       "      <td>66.470520</td>\n",
       "      <td>0.607659</td>\n",
       "      <td>40.9783</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008-10-01</th>\n",
       "      <td>44.515671</td>\n",
       "      <td>-49.578044</td>\n",
       "      <td>67.068308</td>\n",
       "      <td>0.659561</td>\n",
       "      <td>43.7725</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009-01-01</th>\n",
       "      <td>55.343444</td>\n",
       "      <td>-50.605169</td>\n",
       "      <td>68.192701</td>\n",
       "      <td>0.812743</td>\n",
       "      <td>55.5586</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009-04-01</th>\n",
       "      <td>33.773539</td>\n",
       "      <td>-51.517992</td>\n",
       "      <td>69.287471</td>\n",
       "      <td>0.487871</td>\n",
       "      <td>33.8509</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009-07-01</th>\n",
       "      <td>42.644197</td>\n",
       "      <td>-52.028229</td>\n",
       "      <td>69.973619</td>\n",
       "      <td>0.606359</td>\n",
       "      <td>42.0764</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009-10-01</th>\n",
       "      <td>46.778157</td>\n",
       "      <td>-52.313804</td>\n",
       "      <td>70.368834</td>\n",
       "      <td>0.658672</td>\n",
       "      <td>45.6423</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-01</th>\n",
       "      <td>58.009227</td>\n",
       "      <td>-54.096900</td>\n",
       "      <td>72.214871</td>\n",
       "      <td>0.812265</td>\n",
       "      <td>59.7668</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-04-01</th>\n",
       "      <td>35.648813</td>\n",
       "      <td>-54.502721</td>\n",
       "      <td>72.912686</td>\n",
       "      <td>0.486511</td>\n",
       "      <td>35.1919</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-07-01</th>\n",
       "      <td>44.784308</td>\n",
       "      <td>-55.166806</td>\n",
       "      <td>73.686305</td>\n",
       "      <td>0.605385</td>\n",
       "      <td>44.3197</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-10-01</th>\n",
       "      <td>49.174177</td>\n",
       "      <td>-55.392771</td>\n",
       "      <td>74.033173</td>\n",
       "      <td>0.657804</td>\n",
       "      <td>47.9137</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-01-01</th>\n",
       "      <td>60.967564</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>36.994298</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>46.712580</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>51.482656</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>64.456489</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>39.017757</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>49.291976</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>54.320013</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    41.861504 -36.533095  51.248179  0.815867  41.7275\n",
       "2005-04-01    25.839364 -35.869251  50.739339  0.495364  24.0418\n",
       "2005-07-01    31.659683 -37.286227  52.063290  0.612970  32.3281\n",
       "2005-10-01    35.189700 -39.171487  54.189765  0.664161  37.3287\n",
       "2006-01-01    44.929289 -40.308330  55.708559  0.815027  46.2132\n",
       "2006-04-01    27.934034 -42.089539  57.758615  0.493045  29.3463\n",
       "2006-07-01    35.824367 -43.102604  59.129595  0.610077  36.4829\n",
       "2006-10-01    39.768425 -45.646365  61.909655  0.661684  42.9777\n",
       "2007-01-01    51.174461 -45.123756  61.859056  0.813568  48.9015\n",
       "2007-04-01    30.814845 -46.410853  63.137646  0.490289  31.1802\n",
       "2007-07-01    39.009139 -46.389083  63.329952  0.608209  37.7179\n",
       "2007-10-01    42.485786 -46.173619  63.146524  0.660422  40.4202\n",
       "2008-01-01    52.174326 -46.762223  63.704572  0.813361  51.2069\n",
       "2008-04-01    31.677731 -47.838216  64.873407  0.489546  31.8872\n",
       "2008-07-01    40.035737 -49.242003  66.470520  0.607659  40.9783\n",
       "2008-10-01    44.515671 -49.578044  67.068308  0.659561  43.7725\n",
       "2009-01-01    55.343444 -50.605169  68.192701  0.812743  55.5586\n",
       "2009-04-01    33.773539 -51.517992  69.287471  0.487871  33.8509\n",
       "2009-07-01    42.644197 -52.028229  69.973619  0.606359  42.0764\n",
       "2009-10-01    46.778157 -52.313804  70.368834  0.658672  45.6423\n",
       "2010-01-01    58.009227 -54.096900  72.214871  0.812265  59.7668\n",
       "2010-04-01    35.648813 -54.502721  72.912686  0.486511  35.1919\n",
       "2010-07-01    44.784308 -55.166806  73.686305  0.605385  44.3197\n",
       "2010-10-01    49.174177 -55.392771  74.033173  0.657804  47.9137\n",
       "2011-01-01    60.967564        NaN        NaN       NaN      NaN\n",
       "2011-04-01    36.994298        NaN        NaN       NaN      NaN\n",
       "2011-07-01    46.712580        NaN        NaN       NaN      NaN\n",
       "2011-10-01    51.482656        NaN        NaN       NaN      NaN\n",
       "2012-01-01    64.456489        NaN        NaN       NaN      NaN\n",
       "2012-04-01    39.017757        NaN        NaN       NaN      NaN\n",
       "2012-07-01    49.291976        NaN        NaN       NaN      NaN\n",
       "2012-10-01    54.320013        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": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/lib/python3/dist-packages/pandas/plotting/_matplotlib/tools.py:307: MatplotlibDeprecationWarning: \n",
      "The rowNum attribute was deprecated in Matplotlib 3.2 and will be removed two minor releases later. Use ax.get_subplotspec().rowspan.start instead.\n",
      "  layout[ax.rowNum, ax.colNum] = ax.get_visible()\n",
      "/usr/lib/python3/dist-packages/pandas/plotting/_matplotlib/tools.py:307: MatplotlibDeprecationWarning: \n",
      "The colNum attribute was deprecated in Matplotlib 3.2 and will be removed two minor releases later. Use ax.get_subplotspec().colspan.start instead.\n",
      "  layout[ax.rowNum, ax.colNum] = ax.get_visible()\n",
      "/usr/lib/python3/dist-packages/pandas/plotting/_matplotlib/tools.py:313: MatplotlibDeprecationWarning: \n",
      "The rowNum attribute was deprecated in Matplotlib 3.2 and will be removed two minor releases later. Use ax.get_subplotspec().rowspan.start instead.\n",
      "  if not layout[ax.rowNum + 1, ax.colNum]:\n",
      "/usr/lib/python3/dist-packages/pandas/plotting/_matplotlib/tools.py:313: MatplotlibDeprecationWarning: \n",
      "The colNum attribute was deprecated in Matplotlib 3.2 and will be removed two minor releases later. Use ax.get_subplotspec().colspan.start instead.\n",
      "  if not layout[ax.rowNum + 1, ax.colNum]:\n",
      "/usr/lib/python3/dist-packages/pandas/plotting/_matplotlib/tools.py:307: MatplotlibDeprecationWarning: \n",
      "The rowNum attribute was deprecated in Matplotlib 3.2 and will be removed two minor releases later. Use ax.get_subplotspec().rowspan.start instead.\n",
      "  layout[ax.rowNum, ax.colNum] = ax.get_visible()\n",
      "/usr/lib/python3/dist-packages/pandas/plotting/_matplotlib/tools.py:307: MatplotlibDeprecationWarning: \n",
      "The colNum attribute was deprecated in Matplotlib 3.2 and will be removed two minor releases later. Use ax.get_subplotspec().colspan.start instead.\n",
      "  layout[ax.rowNum, ax.colNum] = ax.get_visible()\n",
      "/usr/lib/python3/dist-packages/pandas/plotting/_matplotlib/tools.py:313: MatplotlibDeprecationWarning: \n",
      "The rowNum attribute was deprecated in Matplotlib 3.2 and will be removed two minor releases later. Use ax.get_subplotspec().rowspan.start instead.\n",
      "  if not layout[ax.rowNum + 1, ax.colNum]:\n",
      "/usr/lib/python3/dist-packages/pandas/plotting/_matplotlib/tools.py:313: MatplotlibDeprecationWarning: \n",
      "The colNum attribute was deprecated in Matplotlib 3.2 and will be removed two minor releases later. Use ax.get_subplotspec().colspan.start instead.\n",
      "  if not layout[ax.rowNum + 1, ax.colNum]:\n",
      "/usr/lib/python3/dist-packages/pandas/plotting/_matplotlib/tools.py:307: MatplotlibDeprecationWarning: \n",
      "The rowNum attribute was deprecated in Matplotlib 3.2 and will be removed two minor releases later. Use ax.get_subplotspec().rowspan.start instead.\n",
      "  layout[ax.rowNum, ax.colNum] = ax.get_visible()\n",
      "/usr/lib/python3/dist-packages/pandas/plotting/_matplotlib/tools.py:307: MatplotlibDeprecationWarning: \n",
      "The colNum attribute was deprecated in Matplotlib 3.2 and will be removed two minor releases later. Use ax.get_subplotspec().colspan.start instead.\n",
      "  layout[ax.rowNum, ax.colNum] = ax.get_visible()\n",
      "/usr/lib/python3/dist-packages/pandas/plotting/_matplotlib/tools.py:313: MatplotlibDeprecationWarning: \n",
      "The rowNum attribute was deprecated in Matplotlib 3.2 and will be removed two minor releases later. Use ax.get_subplotspec().rowspan.start instead.\n",
      "  if not layout[ax.rowNum + 1, ax.colNum]:\n",
      "/usr/lib/python3/dist-packages/pandas/plotting/_matplotlib/tools.py:313: MatplotlibDeprecationWarning: \n",
      "The colNum attribute was deprecated in Matplotlib 3.2 and will be removed two minor releases later. Use ax.get_subplotspec().colspan.start instead.\n",
      "  if not layout[ax.rowNum + 1, ax.colNum]:\n",
      "/usr/lib/python3/dist-packages/pandas/plotting/_matplotlib/tools.py:307: MatplotlibDeprecationWarning: \n",
      "The rowNum attribute was deprecated in Matplotlib 3.2 and will be removed two minor releases later. Use ax.get_subplotspec().rowspan.start instead.\n",
      "  layout[ax.rowNum, ax.colNum] = ax.get_visible()\n",
      "/usr/lib/python3/dist-packages/pandas/plotting/_matplotlib/tools.py:307: MatplotlibDeprecationWarning: \n",
      "The colNum attribute was deprecated in Matplotlib 3.2 and will be removed two minor releases later. Use ax.get_subplotspec().colspan.start instead.\n",
      "  layout[ax.rowNum, ax.colNum] = ax.get_visible()\n",
      "/usr/lib/python3/dist-packages/pandas/plotting/_matplotlib/tools.py:313: MatplotlibDeprecationWarning: \n",
      "The rowNum attribute was deprecated in Matplotlib 3.2 and will be removed two minor releases later. Use ax.get_subplotspec().rowspan.start instead.\n",
      "  if not layout[ax.rowNum + 1, ax.colNum]:\n",
      "/usr/lib/python3/dist-packages/pandas/plotting/_matplotlib/tools.py:313: MatplotlibDeprecationWarning: \n",
      "The colNum attribute was deprecated in Matplotlib 3.2 and will be removed two minor releases later. Use ax.get_subplotspec().colspan.start instead.\n",
      "  if not layout[ax.rowNum + 1, ax.colNum]:\n",
      "/usr/lib/python3/dist-packages/pandas/plotting/_matplotlib/tools.py:307: MatplotlibDeprecationWarning: \n",
      "The rowNum attribute was deprecated in Matplotlib 3.2 and will be removed two minor releases later. Use ax.get_subplotspec().rowspan.start instead.\n",
      "  layout[ax.rowNum, ax.colNum] = ax.get_visible()\n",
      "/usr/lib/python3/dist-packages/pandas/plotting/_matplotlib/tools.py:307: MatplotlibDeprecationWarning: \n",
      "The colNum attribute was deprecated in Matplotlib 3.2 and will be removed two minor releases later. Use ax.get_subplotspec().colspan.start instead.\n",
      "  layout[ax.rowNum, ax.colNum] = ax.get_visible()\n",
      "/usr/lib/python3/dist-packages/pandas/plotting/_matplotlib/tools.py:313: MatplotlibDeprecationWarning: \n",
      "The rowNum attribute was deprecated in Matplotlib 3.2 and will be removed two minor releases later. Use ax.get_subplotspec().rowspan.start instead.\n",
      "  if not layout[ax.rowNum + 1, ax.colNum]:\n",
      "/usr/lib/python3/dist-packages/pandas/plotting/_matplotlib/tools.py:313: MatplotlibDeprecationWarning: \n",
      "The colNum attribute was deprecated in Matplotlib 3.2 and will be removed two minor releases later. Use ax.get_subplotspec().colspan.start instead.\n",
      "  if not layout[ax.rowNum + 1, ax.colNum]:\n",
      "/usr/lib/python3/dist-packages/pandas/plotting/_matplotlib/tools.py:307: MatplotlibDeprecationWarning: \n",
      "The rowNum attribute was deprecated in Matplotlib 3.2 and will be removed two minor releases later. Use ax.get_subplotspec().rowspan.start instead.\n",
      "  layout[ax.rowNum, ax.colNum] = ax.get_visible()\n",
      "/usr/lib/python3/dist-packages/pandas/plotting/_matplotlib/tools.py:307: MatplotlibDeprecationWarning: \n",
      "The colNum attribute was deprecated in Matplotlib 3.2 and will be removed two minor releases later. Use ax.get_subplotspec().colspan.start instead.\n",
      "  layout[ax.rowNum, ax.colNum] = ax.get_visible()\n",
      "/usr/lib/python3/dist-packages/pandas/plotting/_matplotlib/tools.py:313: MatplotlibDeprecationWarning: \n",
      "The rowNum attribute was deprecated in Matplotlib 3.2 and will be removed two minor releases later. Use ax.get_subplotspec().rowspan.start instead.\n",
      "  if not layout[ax.rowNum + 1, ax.colNum]:\n",
      "/usr/lib/python3/dist-packages/pandas/plotting/_matplotlib/tools.py:313: MatplotlibDeprecationWarning: \n",
      "The colNum attribute was deprecated in Matplotlib 3.2 and will be removed two minor releases later. Use ax.get_subplotspec().colspan.start instead.\n",
      "  if not layout[ax.rowNum + 1, ax.colNum]:\n"
     ]
    },
    {
     "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": "3.8.5"
  },
  "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"
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   "navigate_menu": true,
   "number_sections": false,
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   "threshold": 4,
   "toc_cell": false,
   "toc_section_display": "block",
   "toc_window_display": true,
   "widenNotebook": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}
