Lista Pratica 2.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Pablo Corrêa Fonseca"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import statsmodels.api as sm\n",
    "import pandas as pd\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "import seaborn as sns\n",
    "sns.set_style('whitegrid')\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**1. Importe para o R os dados do arquivo “dados_ 01.csv” e converta as variáveis acima\n",
    "para séries de tempo usando o comando “ts”**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SELIC</th>\n",
       "      <th>UTCAP</th>\n",
       "      <th>INF</th>\n",
       "      <th>EXPINF</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2003-01</th>\n",
       "      <td>25.0</td>\n",
       "      <td>80.4</td>\n",
       "      <td>2.25</td>\n",
       "      <td>11.72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2003-02</th>\n",
       "      <td>25.5</td>\n",
       "      <td>80.2</td>\n",
       "      <td>1.57</td>\n",
       "      <td>11.07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2003-03</th>\n",
       "      <td>26.5</td>\n",
       "      <td>79.7</td>\n",
       "      <td>1.23</td>\n",
       "      <td>9.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2003-04</th>\n",
       "      <td>26.5</td>\n",
       "      <td>79.7</td>\n",
       "      <td>0.97</td>\n",
       "      <td>9.18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2003-05</th>\n",
       "      <td>26.5</td>\n",
       "      <td>79.9</td>\n",
       "      <td>0.61</td>\n",
       "      <td>8.23</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         SELIC  UTCAP   INF  EXPINF\n",
       "2003-01   25.0   80.4  2.25   11.72\n",
       "2003-02   25.5   80.2  1.57   11.07\n",
       "2003-03   26.5   79.7  1.23    9.90\n",
       "2003-04   26.5   79.7  0.97    9.18\n",
       "2003-05   26.5   79.9  0.61    8.23"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#carrega os dados e ajusta o index para períodos mensais\n",
    "data = pd.read_csv('dados_01.csv',sep=';', decimal=',', index_col=0)\n",
    "data.index = pd.to_datetime(dict(year=data.index.map(int), month=((data.index*100).map(int) % 100), day=1))\n",
    "data = data.to_period('M')\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Selecione a ordem de defasagens ótima de um modelo VAR para estas variáveis, com\n",
    "base nos Critérios de Informação de Akaike, Schwarz e Hannan-Quinn, bem como no\n",
    "teste de Razão de Verossimilhança (RV), considerando um máximo possível de 12\n",
    "defasagens. As rotinas VARselect, do pacote vars, e VARorder, do pacote MTS,\n",
    "fornecem os CIs, mas somente a segunda rotina realiza o teste de RV. [Para este\n",
    "comando, supondo que a matriz de dados do VAR seja y, a sintaxe é:\n",
    "VARorder(y,lag.max=12) ].**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                 VAR Order Selection                  \n",
      "======================================================\n",
      "            aic          bic          fpe         hqic\n",
      "------------------------------------------------------\n",
      "0        0.3740       0.4509        1.454       0.4052\n",
      "1        -9.933      -9.548*    4.856e-05      -9.777*\n",
      "2        -10.02       -9.331    4.439e-05       -9.742\n",
      "3        -10.02       -9.016    4.476e-05       -9.610\n",
      "4        -10.24       -8.931    3.589e-05       -9.708\n",
      "5       -10.24*       -8.629   3.581e-05*       -9.588\n",
      "6        -10.20       -8.274    3.770e-05       -9.416\n",
      "7        -10.20       -7.973    3.767e-05       -9.298\n",
      "8        -10.22       -7.686    3.720e-05       -9.193\n",
      "9        -10.19       -7.341    3.903e-05       -9.030\n",
      "10       -10.22       -7.069    3.813e-05       -8.941\n",
      "11       -10.14       -6.679    4.209e-05       -8.733\n",
      "12       -10.05       -6.281    4.691e-05       -8.519\n",
      "13       -9.966       -5.892    5.209e-05       -8.312\n",
      "14       -9.899       -5.517    5.722e-05       -8.120\n",
      "======================================================\n",
      "* Minimum\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'aic': 5, 'bic': 1, 'fpe': 5, 'hqic': 1}"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#cria o modelo VAR\n",
    "model = sm.tsa.VAR(data)\n",
    "model.select_order()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Verifique a presença de autocorrelação residual com base nos testes Portmanteau (6 e\n",
    "12 defasagens) e LM de Breusch-Godfrey (até 4 defasagens).**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SELIC [ 0.56320587  0.80680051  0.66572639  0.73851806  0.80107481  0.30428484\n",
      "  0.27170086  0.35812398  0.42931385  0.51820299  0.24941299  0.27612047] \n",
      "\n",
      "UTCAP [ 0.8488243   0.90053504  0.93180308  0.90809154  0.81941048  0.86101208\n",
      "  0.9186195   0.95380236  0.87479008  0.75917631  0.80716363  0.63251075] \n",
      "\n",
      "INF [ 0.86089954  0.73830308  0.8948515   0.86953052  0.86764958  0.76847227\n",
      "  0.51459747  0.44979975  0.54285487  0.50139496  0.54621717  0.63080747] \n",
      "\n",
      "EXPINF [ 0.79188065  0.96528168  0.97748512  0.96155851  0.94961337  0.9660936\n",
      "  0.98549924  0.97206745  0.96079981  0.7039296   0.51185726  0.52814901] \n",
      "\n"
     ]
    }
   ],
   "source": [
    "#diferente do R,o pacote statsmodels não possui o teste Portmanteau multivariavel\n",
    "#o codigo abaixo reporta o p-valor do teste ljungbox para cada variável em separado, até 12 lags.\n",
    "fit6 = model.fit(maxlags=5)\n",
    "for col in fit6.resid.columns:\n",
    "    print(col,sm.stats.diagnostic.acorr_ljungbox(fit6.resid[col], lags=12, boxpierce=False)[1],'\\n')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 2.07492304,  2.00993869,  1.95786267,  1.97159805])"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#No entanto, o teste mutlivariável presente no pacote statmodels é o Durbin Watson\n",
    "from statsmodels.stats.stattools import durbin_watson\n",
    "durbin_watson(fit6.resid)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Durbin-Watson statistics\n",
    ">The null hypothesis of the test is that there is no serial correlation. The Durbin-Watson test statistics is defined as:\n",
    ">\n",
    ">$$\\frac{\\sum_{t=2}^T((e_t - e_{t-1})^2)}{\\sum_{t=1}^Te_t^2}$$\n",
    ">\n",
    ">The test statistic is approximately equal to 2*(1-r) where r is the sample autocorrelation of the residuals. Thus, for r == 0, >indicating no serial correlation, the test statistic equals 2. This statistic will always be between 0 and 4. The closer to 0 the statistic, the more evidence for positive serial correlation. The closer to 4, the more evidence for negative serial >correlation."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Com base nos resultados anteriores, escolha o melhor modelo VAR para as variáveis analisadas."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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cQ7CvK2NCffh6WxKV5pqBXHRiNutjM1qsZooQQghxIbN1sLUd6K2U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\n2UPj3GqYub6u6xddgM2P1JhQPt+VZwV3T63K5FhpNZ/vym0R+L6yLotX1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rOnnhCi75Bp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\nAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x17d69ad5390>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#escolemos o modelo com 5 defasagens\n",
    "fit5 = model.fit(maxlags=5)\n",
    "fit5.plotsim()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "### Parte 2\n",
    "**Importe para o R os dados do arquivo “dados_02.csv”. As séries estão agrupadas de 4 em 4, e cada aluno deverá analisar um grupo específico (p.ex., V1 a V4, V5 a V8 etc.), conforme definido em aula.**\n",
    "\n",
    "**Deverão ser considerados os seguintes possíveis modelos: (i) ARIMA para X1 (identificado pela metodologia Box-Jenkins), (ii) VAR para X1 e X2; (iii) VAR para X1, X2, X3 e X4; (iv) ARMADL para X1 e X2. Em cada caso, o aluno deve buscar a melhor especificação possível para a amostra restrita às primeiras 200 observações.**\n",
    "\n",
    "**Em seguida, o aluno deve avaliar o desempenho preditivo de cada modelo por meio do\n",
    "seguinte procedimento: (i) estimar cada modelo usando apenas as primeiras 200\n",
    "observações; (ii) realizar previsões a partir do modelo estimado para as próximas 20\n",
    "observações, mantendo a amostra de estimação fixa em T=200; (iii) calcular o EQM de\n",
    "previsão; (iv) selecionar o modelo com menor EQM. [obs. No caso do modelo\n",
    "ARMADL, é necessário gerar também previsões de X2, o que pode ser feito a partir de\n",
    "um modelo ARIMA]**\n",
    "\n",
    "Selecionei o segundo grupo V5 - V8"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>V5</th>\n",
       "      <th>V6</th>\n",
       "      <th>V7</th>\n",
       "      <th>V8</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2015-01-01</th>\n",
       "      <td>9.155180</td>\n",
       "      <td>10.002280</td>\n",
       "      <td>1.809067</td>\n",
       "      <td>0.951514</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-02</th>\n",
       "      <td>9.127209</td>\n",
       "      <td>9.800345</td>\n",
       "      <td>1.811543</td>\n",
       "      <td>1.164562</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-03</th>\n",
       "      <td>9.264625</td>\n",
       "      <td>9.907205</td>\n",
       "      <td>1.866863</td>\n",
       "      <td>1.326710</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-04</th>\n",
       "      <td>9.280383</td>\n",
       "      <td>9.991321</td>\n",
       "      <td>1.974905</td>\n",
       "      <td>1.458685</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-05</th>\n",
       "      <td>9.173986</td>\n",
       "      <td>9.977733</td>\n",
       "      <td>1.842954</td>\n",
       "      <td>1.431095</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  V5         V6        V7        V8\n",
       "2015-01-01  9.155180  10.002280  1.809067  0.951514\n",
       "2015-01-02  9.127209   9.800345  1.811543  1.164562\n",
       "2015-01-03  9.264625   9.907205  1.866863  1.326710\n",
       "2015-01-04  9.280383   9.991321  1.974905  1.458685\n",
       "2015-01-05  9.173986   9.977733  1.842954  1.431095"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#a biblioteca statsmodels espera que as series temporais sejam indexadas por datas, como os dados não possuiam data\n",
    "#considerei dados diários desde jan/15\n",
    "data2 = pd.read_csv('dados_02.csv',sep=';', decimal=',', index_col=0, usecols=range(4,9))\n",
    "data2.index = pd.date_range(start='2015-01-01', periods=len(data2)) \n",
    "data2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'aic':             0           1           2\n",
       " 0 -273.420214 -408.329651 -406.698828\n",
       " 1 -344.703709 -406.699667 -404.700591\n",
       " 2 -383.086187 -404.706068 -403.448436\n",
       " 3 -387.579785 -405.544332 -403.632912\n",
       " 4 -388.339787 -403.736966 -404.601272,\n",
       " 'aic_min_order': (0, 1),\n",
       " 'bic':             0           1           2\n",
       " 0 -266.458936 -397.887734 -392.776272\n",
       " 1 -334.261792 -392.777111 -387.297396\n",
       " 2 -369.163631 -387.302874 -382.564602\n",
       " 3 -370.176591 -384.660498 -379.268440\n",
       " 4 -367.455954 -379.372493 -376.756161,\n",
       " 'bic_min_order': (0, 1),\n",
       " 'hqic':             0           1           2\n",
       " 0 -270.615327 -404.122321 -401.089054\n",
       " 1 -340.496379 -401.089893 -397.688374\n",
       " 2 -377.476413 -397.693851 -395.033776\n",
       " 3 -380.567569 -397.129672 -393.815809\n",
       " 4 -379.925127 -393.919862 -393.381725,\n",
       " 'hqic_min_order': (0, 1)}"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#seleciona ordens p e q para o modelo arima AR são linhas e MA são colunas\n",
    "sm.tsa.arma_order_select_ic(data2['V5'].values, ic=['aic', 'bic', 'hqic'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style  type=\"text/css\" >\n",
       "</style>  \n",
       "<table id=\"T_6c426818_b85e_11e7_843e_848f69d24bc6\" ><caption>Resultado das previsões 20 passos a frente.</caption> \n",
       "<thead>    <tr> \n",
       "        <th class=\"blank level0\" ></th> \n",
       "        <th class=\"col_heading level0 col0\" >ARIMA</th> \n",
       "        <th class=\"col_heading level0 col1\" >ARMADL</th> \n",
       "        <th class=\"col_heading level0 col2\" colspan=4>REAL</th> \n",
       "        <th class=\"col_heading level0 col6\" colspan=2>VAR_X12</th> \n",
       "        <th class=\"col_heading level0 col8\" colspan=4>VAR_X1234</th> \n",
       "    </tr>    <tr> \n",
       "        <th class=\"blank level1\" ></th> \n",
       "        <th class=\"col_heading level1 col0\" >V5</th> \n",
       "        <th class=\"col_heading level1 col1\" >V5</th> \n",
       "        <th class=\"col_heading level1 col2\" >V5</th> \n",
       "        <th class=\"col_heading level1 col3\" >V6</th> \n",
       "        <th class=\"col_heading level1 col4\" >V7</th> \n",
       "        <th class=\"col_heading level1 col5\" >V8</th> \n",
       "        <th class=\"col_heading level1 col6\" >V5</th> \n",
       "        <th class=\"col_heading level1 col7\" >V6</th> \n",
       "        <th class=\"col_heading level1 col8\" >V5</th> \n",
       "        <th class=\"col_heading level1 col9\" >V6</th> \n",
       "        <th class=\"col_heading level1 col10\" >V7</th> \n",
       "        <th class=\"col_heading level1 col11\" >V8</th> \n",
       "    </tr></thead> \n",
       "<tbody>    <tr> \n",
       "        <th id=\"T_6c426818_b85e_11e7_843e_848f69d24bc6level0_row0\" class=\"row_heading level0 row0\" >2015-07-20 00:00:00</th> \n",
       "        <td id=\"T_6c426818_b85e_11e7_843e_848f69d24bc6row0_col0\" class=\"data row0 col0\" >9.17916</td> \n",
       "        <td id=\"T_6c426818_b85e_11e7_843e_848f69d24bc6row0_col1\" class=\"data row0 col1\" >9.16574</td> \n",
       "        <td id=\"T_6c426818_b85e_11e7_843e_848f69d24bc6row0_col2\" class=\"data row0 col2\" >9.25974</td> \n",
       "        <td id=\"T_6c426818_b85e_11e7_843e_848f69d24bc6row0_col3\" class=\"data row0 col3\" >10.101</td> \n",
       "        <td id=\"T_6c426818_b85e_11e7_843e_848f69d24bc6row0_col4\" class=\"data row0 col4\" >1.92505</td> \n",
       "        <td id=\"T_6c426818_b85e_11e7_843e_848f69d24bc6row0_col5\" class=\"data row0 col5\" >1.45829</td> \n",
       "        <td id=\"T_6c426818_b85e_11e7_843e_848f69d24bc6row0_col6\" class=\"data row0 col6\" >8.99335</td> \n",
       "        <td id=\"T_6c426818_b85e_11e7_843e_848f69d24bc6row0_col7\" class=\"data row0 col7\" >9.57762</td> \n",
       "        <td id=\"T_6c426818_b85e_11e7_843e_848f69d24bc6row0_col8\" class=\"data row0 col8\" >8.98096</td> \n",
       "        <td id=\"T_6c426818_b85e_11e7_843e_848f69d24bc6row0_col9\" class=\"data row0 col9\" >9.57014</td> \n",
       "        <td id=\"T_6c426818_b85e_11e7_843e_848f69d24bc6row0_col10\" class=\"data row0 col10\" >1.72729</td> \n",
       "        <td id=\"T_6c426818_b85e_11e7_843e_848f69d24bc6row0_col11\" class=\"data row0 col11\" >1.18928</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_6c426818_b85e_11e7_843e_848f69d24bc6level0_row1\" class=\"row_heading level0 row1\" >2015-07-21 00:00:00</th> \n",
       "        <td id=\"T_6c426818_b85e_11e7_843e_848f69d24bc6row1_col0\" class=\"data row1 col0\" >9.17917</td> \n",
       "        <td id=\"T_6c426818_b85e_11e7_843e_848f69d24bc6row1_col1\" class=\"data row1 col1\" >9.18698</td> \n",
       "        <td id=\"T_6c426818_b85e_11e7_843e_848f69d24bc6row1_col2\" class=\"data row1 col2\" >9.20284</td> \n",
       "        <td id=\"T_6c426818_b85e_11e7_843e_848f69d24bc6row1_col3\" class=\"data row1 col3\" >10.0492</td> \n",
       "        <td id=\"T_6c426818_b85e_11e7_843e_848f69d24bc6row1_col4\" class=\"data row1 col4\" >2.02786</td> \n",
       "        <td id=\"T_6c426818_b85e_11e7_843e_848f69d24bc6row1_col5\" class=\"data row1 col5\" >1.42793</td> \n",
       "        <td id=\"T_6c426818_b85e_11e7_843e_848f69d24bc6row1_col6\" class=\"data row1 col6\" >8.94171</td> \n",
       "        <td id=\"T_6c426818_b85e_11e7_843e_848f69d24bc6row1_col7\" class=\"data row1 col7\" >9.55615</td> \n",
       "        <td id=\"T_6c426818_b85e_11e7_843e_848f69d24bc6row1_col8\" class=\"data row1 col8\" >8.91997</td> \n",
       "        <td id=\"T_6c426818_b85e_11e7_843e_848f69d24bc6row1_col9\" class=\"data row1 col9\" >9.53103</td> \n",
       "        <td id=\"T_6c426818_b85e_11e7_843e_848f69d24bc6row1_col10\" class=\"data row1 col10\" >1.4965</td> \n",
       "        <td id=\"T_6c426818_b85e_11e7_843e_848f69d24bc6row1_col11\" class=\"data row1 col11\" >1.00136</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_6c426818_b85e_11e7_843e_848f69d24bc6level0_row2\" class=\"row_heading level0 row2\" >2015-07-22 00:00:00</th> \n",
       "        <td id=\"T_6c426818_b85e_11e7_843e_848f69d24bc6row2_col0\" class=\"data row2 col0\" >9.17919</td> \n",
       "        <td id=\"T_6c426818_b85e_11e7_843e_848f69d24bc6row2_col1\" class=\"data row2 col1\" >9.19371</td> \n",
       "        <td id=\"T_6c426818_b85e_11e7_843e_848f69d24bc6row2_col2\" class=\"data row2 col2\" >9.18954</td> \n",
       "        <td id=\"T_6c426818_b85e_11e7_843e_848f69d24bc6row2_col3\" class=\"data row2 col3\" >10.1405</td> \n",
       "        <td id=\"T_6c426818_b85e_11e7_843e_848f69d24bc6row2_col4\" class=\"data row2 col4\" >2.18814</td> \n",
       "        <td id=\"T_6c426818_b85e_11e7_843e_848f69d24bc6row2_col5\" class=\"data row2 col5\" >1.57194</td> \n",
       "        <td id=\"T_6c426818_b85e_11e7_843e_848f69d24bc6row2_col6\" class=\"data row2 col6\" >8.90897</td> \n",
       "        <td id=\"T_6c426818_b85e_11e7_843e_848f69d24bc6row2_col7\" class=\"data row2 col7\" >9.54075</td> \n",
       "        <td id=\"T_6c426818_b85e_11e7_843e_848f69d24bc6row2_col8\" class=\"data row2 col8\" >8.8864</td> \n",
       "        <td id=\"T_6c426818_b85e_11e7_843e_848f69d24bc6row2_col9\" class=\"data row2 col9\" >9.52044</td> \n",
       "        <td id=\"T_6c426818_b85e_11e7_843e_848f69d24bc6row2_col10\" class=\"data row2 col10\" >1.37925</td> \n",
       "        <td id=\"T_6c426818_b85e_11e7_843e_848f69d24bc6row2_col11\" class=\"data row2 col11\" >0.89928</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_6c426818_b85e_11e7_843e_848f69d24bc6level0_row3\" class=\"row_heading level0 row3\" >2015-07-23 00:00:00</th> \n",
       "        <td id=\"T_6c426818_b85e_11e7_843e_848f69d24bc6row3_col0\" class=\"data row3 col0\" >9.1792</td> \n",
       "        <td id=\"T_6c426818_b85e_11e7_843e_848f69d24bc6row3_col1\" class=\"data row3 col1\" >9.18649</td> \n",
       "        <td id=\"T_6c426818_b85e_11e7_843e_848f69d24bc6row3_col2\" class=\"data row3 col2\" >9.18811</td> \n",
       "        <td id=\"T_6c426818_b85e_11e7_843e_848f69d24bc6row3_col3\" class=\"data row3 col3\" >10.0322</td> \n",
       "        <td id=\"T_6c426818_b85e_11e7_843e_848f69d24bc6row3_col4\" class=\"data row3 col4\" >2.23413</td> \n",
       "        <td id=\"T_6c426818_b85e_11e7_843e_848f69d24bc6row3_col5\" class=\"data row3 col5\" >1.60712</td> \n",
       "        <td id=\"T_6c426818_b85e_11e7_843e_848f69d24bc6row3_col6\" class=\"data row3 col6\" >8.88484</td> \n",
       "        <td id=\"T_6c426818_b85e_11e7_843e_848f69d24bc6row3_col7\" class=\"data row3 col7\" >9.52735</td> \n",
       "        <td id=\"T_6c426818_b85e_11e7_843e_848f69d24bc6row3_col8\" class=\"data row3 col8\" >8.86918</td> \n",
       "        <td id=\"T_6c426818_b85e_11e7_843e_848f69d24bc6row3_col9\" class=\"data row3 col9\" >9.52092</td> \n",
       "        <td id=\"T_6c426818_b85e_11e7_843e_848f69d24bc6row3_col10\" class=\"data row3 col10\" >1.34092</td> \n",
       "        <td id=\"T_6c426818_b85e_11e7_843e_848f69d24bc6row3_col11\" class=\"data row3 col11\" >0.854579</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_6c426818_b85e_11e7_843e_848f69d24bc6level0_row4\" class=\"row_heading level0 row4\" >2015-07-24 00:00:00</th> \n",
       "        <td id=\"T_6c426818_b85e_11e7_843e_848f69d24bc6row4_col0\" class=\"data row4 col0\" >9.17922</td> \n",
       "        <td id=\"T_6c426818_b85e_11e7_843e_848f69d24bc6row4_col1\" class=\"data row4 col1\" >9.17931</td> \n",
       "        <td id=\"T_6c426818_b85e_11e7_843e_848f69d24bc6row4_col2\" class=\"data row4 col2\" >9.13563</td> \n",
       "        <td id=\"T_6c426818_b85e_11e7_843e_848f69d24bc6row4_col3\" class=\"data row4 col3\" >9.97192</td> \n",
       "        <td id=\"T_6c426818_b85e_11e7_843e_848f69d24bc6row4_col4\" class=\"data row4 col4\" >2.1959</td> \n",
       "        <td id=\"T_6c426818_b85e_11e7_843e_848f69d24bc6row4_col5\" class=\"data row4 col5\" >1.51364</td> \n",
       "        <td id=\"T_6c426818_b85e_11e7_843e_848f69d24bc6row4_col6\" class=\"data row4 col6\" >8.87981</td> \n",
       "        <td id=\"T_6c426818_b85e_11e7_843e_848f69d24bc6row4_col7\" class=\"data row4 col7\" >9.52845</td> \n",
       "        <td id=\"T_6c426818_b85e_11e7_843e_848f69d24bc6row4_col8\" class=\"data row4 col8\" >8.87256</td> \n",
       "        <td id=\"T_6c426818_b85e_11e7_843e_848f69d24bc6row4_col9\" class=\"data row4 col9\" >9.53397</td> \n",
       "        <td id=\"T_6c426818_b85e_11e7_843e_848f69d24bc6row4_col10\" class=\"data row4 col10\" >1.34183</td> \n",
       "        <td id=\"T_6c426818_b85e_11e7_843e_848f69d24bc6row4_col11\" class=\"data row4 col11\" >0.842038</td> \n",
       "    </tr></tbody> \n",
       "</table> "
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x17d7309c4a8>"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Cria os modelos e faz previsões 20 passos a frente\n",
    "\n",
    "modelo_arima = sm.tsa.ARIMA(data2.iloc[:200]['V5'],order=(0,1,1), exog=None).fit()\n",
    "forecast_arima = pd.DataFrame(modelo_arima.forecast(20)[0], columns=[data2.columns[0]], index=pd.date_range(start=data2.index[200], periods=20))\n",
    "\n",
    "var_12 = sm.tsa.VAR(data2.iloc[:200,[0,1]])\n",
    "order = 2\n",
    "\n",
    "var_12_results = var_12.fit(order)\n",
    "forecasts_var_12 = pd.DataFrame(var_12_results.forecast_interval(var_12_results.y[-order:],20)[1], columns=data2.columns[0:2], index=pd.date_range(start=data2.index[200], periods=20))\n",
    "\n",
    "var_1234 = sm.tsa.VAR(data2.iloc[:200])\n",
    "order = 2\n",
    "\n",
    "var_1234_results = var_1234.fit(order)\n",
    "forecast_var_1234 = pd.DataFrame(var_1234_results.forecast_interval(var_1234_results.y[-order:],20)[1], columns=data2.columns, index=pd.date_range(start=data2.index[200], periods=20))\n",
    "\n",
    "modelo_armadl = sm.tsa.ARMA(data2.iloc[:200]['V5'],order=(0,1), exog=data2.iloc[:200]['V6']).fit()\n",
    "\n",
    "min_order = sm.tsa.arma_order_select_ic(data2.iloc[:200]['V6'].values, ic='bic')['bic_min_order']\n",
    "exog_model = sm.tsa.ARMA(data2.iloc[:200]['V6'],order=min_order, exog=None).fit()\n",
    "exog= exog_model.forecast(20)[0]\n",
    "forecast_exog = pd.DataFrame(exog, columns=[data2.columns[0]], index=pd.date_range(start=data2.index[200], periods=20))\n",
    "\n",
    "forecast_armadl = pd.DataFrame(modelo_armadl.forecast(20, exog=forecast_exog)[0], columns=[data2.columns[0]], index=pd.date_range(start=data2.index[200], periods=20))\n",
    "\n",
    "forecasts = pd.concat([data2.iloc[200:220],forecast_arima, forecast_armadl, forecasts_var_12, forecast_var_1234], axis=1, keys=['REAL','ARIMA','ARMADL','VAR_X12','VAR_X1234'])\n",
    "\n",
    "forecasts.sort_index(axis=1).head().style.set_caption('Resultado das previsões 20 passos a frente.')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style  type=\"text/css\" >\n",
       "</style>  \n",
       "<table id=\"T_6bdacb06_b85c_11e7_afcf_848f69d24bc6\" > \n",
       "<thead>    <tr> \n",
       "        <th class=\"blank\" ></th> \n",
       "        <th class=\"blank level0\" ></th> \n",
       "        <th class=\"col_heading level0 col0\" >Erro Quadrático Médio - 20 passos</th> \n",
       "    </tr>    <tr> \n",
       "        <th class=\"index_name level0\" >Modelo</th> \n",
       "        <th class=\"index_name level1\" >Parâmetro</th> \n",
       "        <th class=\"blank\" ></th> \n",
       "    </tr></thead> \n",
       "<tbody>    <tr> \n",
       "        <th id=\"T_6bdacb06_b85c_11e7_afcf_848f69d24bc6level0_row0\" class=\"row_heading level0 row0\" >ARIMA</th> \n",
       "        <th id=\"T_6bdacb06_b85c_11e7_afcf_848f69d24bc6level1_row0\" class=\"row_heading level1 row0\" >V5</th> \n",
       "        <td id=\"T_6bdacb06_b85c_11e7_afcf_848f69d24bc6row0_col0\" class=\"data row0 col0\" >0.165623</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_6bdacb06_b85c_11e7_afcf_848f69d24bc6level0_row1\" class=\"row_heading level0 row1\" >ARMADL</th> \n",
       "        <th id=\"T_6bdacb06_b85c_11e7_afcf_848f69d24bc6level1_row1\" class=\"row_heading level1 row1\" >V5</th> \n",
       "        <td id=\"T_6bdacb06_b85c_11e7_afcf_848f69d24bc6row1_col0\" class=\"data row1 col0\" >0.1648</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_6bdacb06_b85c_11e7_afcf_848f69d24bc6level0_row2\" class=\"row_heading level0 row2\" rowspan=2>VAR_X12</th> \n",
       "        <th id=\"T_6bdacb06_b85c_11e7_afcf_848f69d24bc6level1_row2\" class=\"row_heading level1 row2\" >V5</th> \n",
       "        <td id=\"T_6bdacb06_b85c_11e7_afcf_848f69d24bc6row2_col0\" class=\"data row2 col0\" >1.51696</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_6bdacb06_b85c_11e7_afcf_848f69d24bc6level1_row3\" class=\"row_heading level1 row3\" >V6</th> \n",
       "        <td id=\"T_6bdacb06_b85c_11e7_afcf_848f69d24bc6row3_col0\" class=\"data row3 col0\" >3.64851</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_6bdacb06_b85c_11e7_afcf_848f69d24bc6level0_row4\" class=\"row_heading level0 row4\" rowspan=4>VAR_X1234</th> \n",
       "        <th id=\"T_6bdacb06_b85c_11e7_afcf_848f69d24bc6level1_row4\" class=\"row_heading level1 row4\" >V5</th> \n",
       "        <td id=\"T_6bdacb06_b85c_11e7_afcf_848f69d24bc6row4_col0\" class=\"data row4 col0\" >1.58596</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_6bdacb06_b85c_11e7_afcf_848f69d24bc6level1_row5\" class=\"row_heading level1 row5\" >V6</th> \n",
       "        <td id=\"T_6bdacb06_b85c_11e7_afcf_848f69d24bc6row5_col0\" class=\"data row5 col0\" >3.71607</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_6bdacb06_b85c_11e7_afcf_848f69d24bc6level1_row6\" class=\"row_heading level1 row6\" >V7</th> \n",
       "        <td id=\"T_6bdacb06_b85c_11e7_afcf_848f69d24bc6row6_col0\" class=\"data row6 col0\" >6.39654</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_6bdacb06_b85c_11e7_afcf_848f69d24bc6level1_row7\" class=\"row_heading level1 row7\" >V8</th> \n",
       "        <td id=\"T_6bdacb06_b85c_11e7_afcf_848f69d24bc6row7_col0\" class=\"data row7 col0\" >5.31025</td> \n",
       "    </tr></tbody> \n",
       "</table> "
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x17d6a007860>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#calcula o Erro quadrático médio das previsões 20 passos a frente\n",
    "mqe_arima = ((forecasts['REAL'] - forecasts['ARIMA'])**2)\n",
    "mqe_armadl = ((forecasts['REAL'] - forecasts['ARMADL'])**2)\n",
    "mqe_varx12 = ((forecasts['REAL'] - forecasts['VAR_X12'])**2)\n",
    "mqe_varx1234 = ((forecasts['REAL'] - forecasts['VAR_X1234'])**2)\n",
    "mqe = pd.concat([mqe_arima, mqe_armadl, mqe_varx12, mqe_varx1234], axis=1, names=['Modelo', 'Parâmetro'], keys=['ARIMA','ARMADL','VAR_X12','VAR_X1234']).dropna(axis=1).sum()\n",
    "mqe.to_frame(name='Erro Quadrático Médio - 20 passos').style"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style  type=\"text/css\" >\n",
       "</style>  \n",
       "<table id=\"T_54c21c86_b85e_11e7_acbd_848f69d24bc6\" ><caption>Primeiras 5 previsões realizadas 1 passo de cada vez.</caption> \n",
       "<thead>    <tr> \n",
       "        <th class=\"blank level0\" ></th> \n",
       "        <th class=\"col_heading level0 col0\" colspan=4>REAL</th> \n",
       "        <th class=\"col_heading level0 col4\" >ARIMA</th> \n",
       "        <th class=\"col_heading level0 col5\" >ARMADL</th> \n",
       "        <th class=\"col_heading level0 col6\" colspan=2>VAR_X12</th> \n",
       "        <th class=\"col_heading level0 col8\" colspan=4>VAR_X1234</th> \n",
       "    </tr>    <tr> \n",
       "        <th class=\"blank level1\" ></th> \n",
       "        <th class=\"col_heading level1 col0\" >V5</th> \n",
       "        <th class=\"col_heading level1 col1\" >V6</th> \n",
       "        <th class=\"col_heading level1 col2\" >V7</th> \n",
       "        <th class=\"col_heading level1 col3\" >V8</th> \n",
       "        <th class=\"col_heading level1 col4\" >V5</th> \n",
       "        <th class=\"col_heading level1 col5\" >V5</th> \n",
       "        <th class=\"col_heading level1 col6\" >V5</th> \n",
       "        <th class=\"col_heading level1 col7\" >V6</th> \n",
       "        <th class=\"col_heading level1 col8\" >V5</th> \n",
       "        <th class=\"col_heading level1 col9\" >V6</th> \n",
       "        <th class=\"col_heading level1 col10\" >V7</th> \n",
       "        <th class=\"col_heading level1 col11\" >V8</th> \n",
       "    </tr></thead> \n",
       "<tbody>    <tr> \n",
       "        <th id=\"T_54c21c86_b85e_11e7_acbd_848f69d24bc6level0_row0\" class=\"row_heading level0 row0\" >2015-07-20 00:00:00</th> \n",
       "        <td id=\"T_54c21c86_b85e_11e7_acbd_848f69d24bc6row0_col0\" class=\"data row0 col0\" >9.25974</td> \n",
       "        <td id=\"T_54c21c86_b85e_11e7_acbd_848f69d24bc6row0_col1\" class=\"data row0 col1\" >10.101</td> \n",
       "        <td id=\"T_54c21c86_b85e_11e7_acbd_848f69d24bc6row0_col2\" class=\"data row0 col2\" >1.92505</td> \n",
       "        <td id=\"T_54c21c86_b85e_11e7_acbd_848f69d24bc6row0_col3\" class=\"data row0 col3\" >1.45829</td> \n",
       "        <td id=\"T_54c21c86_b85e_11e7_acbd_848f69d24bc6row0_col4\" class=\"data row0 col4\" >9.17916</td> \n",
       "        <td id=\"T_54c21c86_b85e_11e7_acbd_848f69d24bc6row0_col5\" class=\"data row0 col5\" >9.19823</td> \n",
       "        <td id=\"T_54c21c86_b85e_11e7_acbd_848f69d24bc6row0_col6\" class=\"data row0 col6\" >8.99335</td> \n",
       "        <td id=\"T_54c21c86_b85e_11e7_acbd_848f69d24bc6row0_col7\" class=\"data row0 col7\" >9.57762</td> \n",
       "        <td id=\"T_54c21c86_b85e_11e7_acbd_848f69d24bc6row0_col8\" class=\"data row0 col8\" >8.98096</td> \n",
       "        <td id=\"T_54c21c86_b85e_11e7_acbd_848f69d24bc6row0_col9\" class=\"data row0 col9\" >9.57014</td> \n",
       "        <td id=\"T_54c21c86_b85e_11e7_acbd_848f69d24bc6row0_col10\" class=\"data row0 col10\" >1.72729</td> \n",
       "        <td id=\"T_54c21c86_b85e_11e7_acbd_848f69d24bc6row0_col11\" class=\"data row0 col11\" >1.18928</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_54c21c86_b85e_11e7_acbd_848f69d24bc6level0_row1\" class=\"row_heading level0 row1\" >2015-07-21 00:00:00</th> \n",
       "        <td id=\"T_54c21c86_b85e_11e7_acbd_848f69d24bc6row1_col0\" class=\"data row1 col0\" >9.20284</td> \n",
       "        <td id=\"T_54c21c86_b85e_11e7_acbd_848f69d24bc6row1_col1\" class=\"data row1 col1\" >10.0492</td> \n",
       "        <td id=\"T_54c21c86_b85e_11e7_acbd_848f69d24bc6row1_col2\" class=\"data row1 col2\" >2.02786</td> \n",
       "        <td id=\"T_54c21c86_b85e_11e7_acbd_848f69d24bc6row1_col3\" class=\"data row1 col3\" >1.42793</td> \n",
       "        <td id=\"T_54c21c86_b85e_11e7_acbd_848f69d24bc6row1_col4\" class=\"data row1 col4\" >9.17917</td> \n",
       "        <td id=\"T_54c21c86_b85e_11e7_acbd_848f69d24bc6row1_col5\" class=\"data row1 col5\" >9.17675</td> \n",
       "        <td id=\"T_54c21c86_b85e_11e7_acbd_848f69d24bc6row1_col6\" class=\"data row1 col6\" >8.82594</td> \n",
       "        <td id=\"T_54c21c86_b85e_11e7_acbd_848f69d24bc6row1_col7\" class=\"data row1 col7\" >9.38723</td> \n",
       "        <td id=\"T_54c21c86_b85e_11e7_acbd_848f69d24bc6row1_col8\" class=\"data row1 col8\" >8.78624</td> \n",
       "        <td id=\"T_54c21c86_b85e_11e7_acbd_848f69d24bc6row1_col9\" class=\"data row1 col9\" >9.3488</td> \n",
       "        <td id=\"T_54c21c86_b85e_11e7_acbd_848f69d24bc6row1_col10\" class=\"data row1 col10\" >1.3167</td> \n",
       "        <td id=\"T_54c21c86_b85e_11e7_acbd_848f69d24bc6row1_col11\" class=\"data row1 col11\" >0.82746</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_54c21c86_b85e_11e7_acbd_848f69d24bc6level0_row2\" class=\"row_heading level0 row2\" >2015-07-22 00:00:00</th> \n",
       "        <td id=\"T_54c21c86_b85e_11e7_acbd_848f69d24bc6row2_col0\" class=\"data row2 col0\" >9.18954</td> \n",
       "        <td id=\"T_54c21c86_b85e_11e7_acbd_848f69d24bc6row2_col1\" class=\"data row2 col1\" >10.1405</td> \n",
       "        <td id=\"T_54c21c86_b85e_11e7_acbd_848f69d24bc6row2_col2\" class=\"data row2 col2\" >2.18814</td> \n",
       "        <td id=\"T_54c21c86_b85e_11e7_acbd_848f69d24bc6row2_col3\" class=\"data row2 col3\" >1.57194</td> \n",
       "        <td id=\"T_54c21c86_b85e_11e7_acbd_848f69d24bc6row2_col4\" class=\"data row2 col4\" >9.17919</td> \n",
       "        <td id=\"T_54c21c86_b85e_11e7_acbd_848f69d24bc6row2_col5\" class=\"data row2 col5\" >9.17675</td> \n",
       "        <td id=\"T_54c21c86_b85e_11e7_acbd_848f69d24bc6row2_col6\" class=\"data row2 col6\" >8.74724</td> \n",
       "        <td id=\"T_54c21c86_b85e_11e7_acbd_848f69d24bc6row2_col7\" class=\"data row2 col7\" >9.28759</td> \n",
       "        <td id=\"T_54c21c86_b85e_11e7_acbd_848f69d24bc6row2_col8\" class=\"data row2 col8\" >8.69375</td> \n",
       "        <td id=\"T_54c21c86_b85e_11e7_acbd_848f69d24bc6row2_col9\" class=\"data row2 col9\" >9.2423</td> \n",
       "        <td id=\"T_54c21c86_b85e_11e7_acbd_848f69d24bc6row2_col10\" class=\"data row2 col10\" >0.988084</td> \n",
       "        <td id=\"T_54c21c86_b85e_11e7_acbd_848f69d24bc6row2_col11\" class=\"data row2 col11\" >0.520456</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_54c21c86_b85e_11e7_acbd_848f69d24bc6level0_row3\" class=\"row_heading level0 row3\" >2015-07-23 00:00:00</th> \n",
       "        <td id=\"T_54c21c86_b85e_11e7_acbd_848f69d24bc6row3_col0\" class=\"data row3 col0\" >9.18811</td> \n",
       "        <td id=\"T_54c21c86_b85e_11e7_acbd_848f69d24bc6row3_col1\" class=\"data row3 col1\" >10.0322</td> \n",
       "        <td id=\"T_54c21c86_b85e_11e7_acbd_848f69d24bc6row3_col2\" class=\"data row3 col2\" >2.23413</td> \n",
       "        <td id=\"T_54c21c86_b85e_11e7_acbd_848f69d24bc6row3_col3\" class=\"data row3 col3\" >1.60712</td> \n",
       "        <td id=\"T_54c21c86_b85e_11e7_acbd_848f69d24bc6row3_col4\" class=\"data row3 col4\" >9.1792</td> \n",
       "        <td id=\"T_54c21c86_b85e_11e7_acbd_848f69d24bc6row3_col5\" class=\"data row3 col5\" >9.17675</td> \n",
       "        <td id=\"T_54c21c86_b85e_11e7_acbd_848f69d24bc6row3_col6\" class=\"data row3 col6\" >8.74079</td> \n",
       "        <td id=\"T_54c21c86_b85e_11e7_acbd_848f69d24bc6row3_col7\" class=\"data row3 col7\" >9.26526</td> \n",
       "        <td id=\"T_54c21c86_b85e_11e7_acbd_848f69d24bc6row3_col8\" class=\"data row3 col8\" >8.69369</td> \n",
       "        <td id=\"T_54c21c86_b85e_11e7_acbd_848f69d24bc6row3_col9\" class=\"data row3 col9\" >9.24282</td> \n",
       "        <td id=\"T_54c21c86_b85e_11e7_acbd_848f69d24bc6row3_col10\" class=\"data row3 col10\" >0.766558</td> \n",
       "        <td id=\"T_54c21c86_b85e_11e7_acbd_848f69d24bc6row3_col11\" class=\"data row3 col11\" >0.277283</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_54c21c86_b85e_11e7_acbd_848f69d24bc6level0_row4\" class=\"row_heading level0 row4\" >2015-07-24 00:00:00</th> \n",
       "        <td id=\"T_54c21c86_b85e_11e7_acbd_848f69d24bc6row4_col0\" class=\"data row4 col0\" >9.13563</td> \n",
       "        <td id=\"T_54c21c86_b85e_11e7_acbd_848f69d24bc6row4_col1\" class=\"data row4 col1\" >9.97192</td> \n",
       "        <td id=\"T_54c21c86_b85e_11e7_acbd_848f69d24bc6row4_col2\" class=\"data row4 col2\" >2.1959</td> \n",
       "        <td id=\"T_54c21c86_b85e_11e7_acbd_848f69d24bc6row4_col3\" class=\"data row4 col3\" >1.51364</td> \n",
       "        <td id=\"T_54c21c86_b85e_11e7_acbd_848f69d24bc6row4_col4\" class=\"data row4 col4\" >9.17921</td> \n",
       "        <td id=\"T_54c21c86_b85e_11e7_acbd_848f69d24bc6row4_col5\" class=\"data row4 col5\" >9.17675</td> \n",
       "        <td id=\"T_54c21c86_b85e_11e7_acbd_848f69d24bc6row4_col6\" class=\"data row4 col6\" >8.74913</td> \n",
       "        <td id=\"T_54c21c86_b85e_11e7_acbd_848f69d24bc6row4_col7\" class=\"data row4 col7\" >9.25974</td> \n",
       "        <td id=\"T_54c21c86_b85e_11e7_acbd_848f69d24bc6row4_col8\" class=\"data row4 col8\" >8.71225</td> \n",
       "        <td id=\"T_54c21c86_b85e_11e7_acbd_848f69d24bc6row4_col9\" class=\"data row4 col9\" >9.26398</td> \n",
       "        <td id=\"T_54c21c86_b85e_11e7_acbd_848f69d24bc6row4_col10\" class=\"data row4 col10\" >0.629257</td> \n",
       "        <td id=\"T_54c21c86_b85e_11e7_acbd_848f69d24bc6row4_col11\" class=\"data row4 col11\" >0.0858155</td> \n",
       "    </tr></tbody> \n",
       "</table> "
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x17d7309c128>"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#calcula as previsões 1 passo a frente, 20 vezes\n",
    "\n",
    "sim_data = data2.iloc[:200].copy()\n",
    "\n",
    "arima_data = sim_data['V5'].copy()\n",
    "var12_data = sim_data[['V5','V6']].copy()\n",
    "var1234_data = sim_data.copy()\n",
    "armadl_data = sim_data['V5'].copy()\n",
    "exog_data = sim_data['V6'].copy()\n",
    "\n",
    "for i in range(20):\n",
    "    modelo_arima = sm.tsa.ARIMA(arima_data,order=(0,1,1), exog=None).fit()\n",
    "    forecast_arima = pd.Series(modelo_arima.forecast(1)[0], index=[arima_data.index[-1] + pd.Timedelta(days=1)], name='V5')\n",
    "    arima_data = arima_data.append(forecast_arima)\n",
    "    \n",
    "    var_12 = sm.tsa.VAR(var12_data)\n",
    "    var_12_results = var_12.fit(2)\n",
    "    forecast_var_12 = pd.DataFrame(var_12_results.forecast_interval(var_12_results.y[-2:],1)[1], columns=['V5','V6'], index=[var12_data.index[-1] + pd.Timedelta(days=1)])\n",
    "    var12_data = var12_data.append(forecast_var_12)\n",
    "    \n",
    "    var_1234 = sm.tsa.VAR(var1234_data)\n",
    "    var_1234_results = var_1234.fit(2)\n",
    "    forecast_var_1234 = pd.DataFrame(var_1234_results.forecast_interval(var_1234_results.y[-2:],1)[1], columns=var1234_data.columns, index=[var1234_data.index[-1] +  pd.Timedelta(days=1)])\n",
    "    var1234_data = var1234_data.append(forecast_var_1234)\n",
    "    \n",
    "    exog_model = sm.tsa.ARMA(exog_data.values,order=(0,1)).fit()\n",
    "    modelo_armadl = sm.tsa.ARMA(armadl_data,order=(0,1), exog=exog_data).fit()\n",
    "    \n",
    "    forecast_exog = exog_model.forecast(1)[0]\n",
    "    exog_data[exog_data.index[-1] + pd.Timedelta(days=1)] = forecast_exog[0]\n",
    "    \n",
    "    forecast_armadl = pd.Series(modelo_armadl.forecast(1, exog=forecast_exog)[0], name='V5', index=[armadl_data.index[-1] + pd.Timedelta(days=1)])\n",
    "    armadl_data = armadl_data.append(forecast_armadl)\n",
    "    \n",
    "forecasts1p20 = pd.concat([data2,arima_data, armadl_data, var12_data, var1234_data], axis=1, keys=['REAL','ARIMA','ARMADL','VAR_X12','VAR_X1234']).iloc[200:220]\n",
    "forecasts1p20.head().style.set_caption('Primeiras 5 previsões realizadas 1 passo de cada vez.')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style  type=\"text/css\" >\n",
       "</style>  \n",
       "<table id=\"T_6e9724e6_b85c_11e7_bb26_848f69d24bc6\" ><caption>Comparativo EQM</caption> \n",
       "<thead>    <tr> \n",
       "        <th class=\"blank\" ></th> \n",
       "        <th class=\"blank level0\" ></th> \n",
       "        <th class=\"col_heading level0 col0\" >EQM 20 passos 1 vez</th> \n",
       "        <th class=\"col_heading level0 col1\" >EQM 1 passo 20 vezes</th> \n",
       "    </tr>    <tr> \n",
       "        <th class=\"index_name level0\" >Modelo</th> \n",
       "        <th class=\"index_name level1\" >Parâmetro</th> \n",
       "        <th class=\"blank\" ></th> \n",
       "        <th class=\"blank\" ></th> \n",
       "    </tr></thead> \n",
       "<tbody>    <tr> \n",
       "        <th id=\"T_6e9724e6_b85c_11e7_bb26_848f69d24bc6level0_row0\" class=\"row_heading level0 row0\" >ARIMA</th> \n",
       "        <th id=\"T_6e9724e6_b85c_11e7_bb26_848f69d24bc6level1_row0\" class=\"row_heading level1 row0\" >V5</th> \n",
       "        <td id=\"T_6e9724e6_b85c_11e7_bb26_848f69d24bc6row0_col0\" class=\"data row0 col0\" >0.165623</td> \n",
       "        <td id=\"T_6e9724e6_b85c_11e7_bb26_848f69d24bc6row0_col1\" class=\"data row0 col1\" >0.165622</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_6e9724e6_b85c_11e7_bb26_848f69d24bc6level0_row1\" class=\"row_heading level0 row1\" >ARMADL</th> \n",
       "        <th id=\"T_6e9724e6_b85c_11e7_bb26_848f69d24bc6level1_row1\" class=\"row_heading level1 row1\" >V5</th> \n",
       "        <td id=\"T_6e9724e6_b85c_11e7_bb26_848f69d24bc6row1_col0\" class=\"data row1 col0\" >0.1648</td> \n",
       "        <td id=\"T_6e9724e6_b85c_11e7_bb26_848f69d24bc6row1_col1\" class=\"data row1 col1\" >0.160909</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_6e9724e6_b85c_11e7_bb26_848f69d24bc6level0_row2\" class=\"row_heading level0 row2\" rowspan=2>VAR_X12</th> \n",
       "        <th id=\"T_6e9724e6_b85c_11e7_bb26_848f69d24bc6level1_row2\" class=\"row_heading level1 row2\" >V5</th> \n",
       "        <td id=\"T_6e9724e6_b85c_11e7_bb26_848f69d24bc6row2_col0\" class=\"data row2 col0\" >1.51696</td> \n",
       "        <td id=\"T_6e9724e6_b85c_11e7_bb26_848f69d24bc6row2_col1\" class=\"data row2 col1\" >11.9225</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_6e9724e6_b85c_11e7_bb26_848f69d24bc6level1_row3\" class=\"row_heading level1 row3\" >V6</th> \n",
       "        <td id=\"T_6e9724e6_b85c_11e7_bb26_848f69d24bc6row3_col0\" class=\"data row3 col0\" >3.64851</td> \n",
       "        <td id=\"T_6e9724e6_b85c_11e7_bb26_848f69d24bc6row3_col1\" class=\"data row3 col1\" >32.2429</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_6e9724e6_b85c_11e7_bb26_848f69d24bc6level0_row4\" class=\"row_heading level0 row4\" rowspan=4>VAR_X1234</th> \n",
       "        <th id=\"T_6e9724e6_b85c_11e7_bb26_848f69d24bc6level1_row4\" class=\"row_heading level1 row4\" >V5</th> \n",
       "        <td id=\"T_6e9724e6_b85c_11e7_bb26_848f69d24bc6row4_col0\" class=\"data row4 col0\" >1.58596</td> \n",
       "        <td id=\"T_6e9724e6_b85c_11e7_bb26_848f69d24bc6row4_col1\" class=\"data row4 col1\" >133.17</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_6e9724e6_b85c_11e7_bb26_848f69d24bc6level1_row5\" class=\"row_heading level1 row5\" >V6</th> \n",
       "        <td id=\"T_6e9724e6_b85c_11e7_bb26_848f69d24bc6row5_col0\" class=\"data row5 col0\" >3.71607</td> \n",
       "        <td id=\"T_6e9724e6_b85c_11e7_bb26_848f69d24bc6row5_col1\" class=\"data row5 col1\" >309.703</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_6e9724e6_b85c_11e7_bb26_848f69d24bc6level1_row6\" class=\"row_heading level1 row6\" >V7</th> \n",
       "        <td id=\"T_6e9724e6_b85c_11e7_bb26_848f69d24bc6row6_col0\" class=\"data row6 col0\" >6.39654</td> \n",
       "        <td id=\"T_6e9724e6_b85c_11e7_bb26_848f69d24bc6row6_col1\" class=\"data row6 col1\" >601.3</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_6e9724e6_b85c_11e7_bb26_848f69d24bc6level1_row7\" class=\"row_heading level1 row7\" >V8</th> \n",
       "        <td id=\"T_6e9724e6_b85c_11e7_bb26_848f69d24bc6row7_col0\" class=\"data row7 col0\" >5.31025</td> \n",
       "        <td id=\"T_6e9724e6_b85c_11e7_bb26_848f69d24bc6row7_col1\" class=\"data row7 col1\" >711.789</td> \n",
       "    </tr></tbody> \n",
       "</table> "
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x17d6a09cb38>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#calcula o Erro quadrático médio das previsões 20 passos a frente e imprime uma tabela comparativa\n",
    "mqe_arima = ((forecasts1p20['REAL'] - forecasts1p20['ARIMA'])**2)\n",
    "mqe_armadl = ((forecasts1p20['REAL'] - forecasts1p20['ARMADL'])**2)\n",
    "mqe_varx12 = ((forecasts1p20['REAL'] - forecasts1p20['VAR_X12'])**2)\n",
    "mqe_varx1234 = ((forecasts1p20['REAL'] - forecasts1p20['VAR_X1234'])**2)\n",
    "mqe_1p20 = pd.concat([mqe_arima, mqe_armadl, mqe_varx12, mqe_varx1234], axis=1, names=['Modelo', 'Parâmetro'], keys=['ARIMA','ARMADL','VAR_X12','VAR_X1234']).dropna(axis=1).sum()\n",
    "pd.concat([mqe, mqe_1p20], axis=1, keys=['EQM 20 passos 1 vez', 'EQM 1 passo 20 vezes']).style.set_caption('Comparativo EQM')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = pd.concat([forecasts, forecasts1p20], axis=1, keys=['20 passos 1 vez', '1 passo 20 vezes']).swaplevel(i=-3, j=-2, axis=1).sort_index(axis=1)\n",
    "x.columns = pd.MultiIndex.from_tuples([(a,'Forecast {} {}'.format(c,b)) for a,b,c in x.columns])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x17d6d1564e0>"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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JALC0tKR79+7s3r0bgC1btvDUU08B0KpVK6ZMmcK6devw9PTk448/vucc+/bti0qluu81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\nAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x17d6d92a2b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = forecasts['REAL']['V5'].to_frame('V5').plot(figsize=(10,5), title='Resultados ARIMA', marker='o')\n",
    "x['ARIMA'].plot(ax=ax)\n",
    "plt.legend(bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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FhVy/fp1bt27h7u4OFNVaACQmJjJ37lysrKxISkrCx8eHFi1a8NxzzzFp0iQK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\nSBQsNoiIiIiISBQsNoiIiIiISBT/D9qpGNQJZBoyAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x17d72a24518>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = forecasts['REAL']['V5'].to_frame('REAL').plot(figsize=(10,5), title='Resultados ARMADL', marker='o')\n",
    "x['ARMADL'].plot(ax=ax)\n",
    "plt.legend(bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.1)\n",
    "sns.despine()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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0hAp14fDq3F9OWhto/akq0Ti8Gk78BNZloEFXaNQDnNxBoym0QxNCCPGI9Knwy6jcF6Rp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Dwfas6FV//GmZF/wXbxYmkPX0RK0G9bEvRXPD09SUtLo0ePHly9epX333+/6DGLxUKlSpWKvvby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\nYncqGiIiIiIiYncqGiIiIiIiYnf/F5hsMWgE/ymcAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x17d72c15e10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = forecasts['REAL'][['V5','V6']].plot(figsize=(10,5), title='Resultados VAR 2 Variáveis', marker='o')\n",
    "x['VAR_X12'].plot(ax=ax)\n",
    "plt.legend(bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.)\n",
    "sns.despine()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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GyjMuP5IthqSXWm4qL17vDKfwlG3p6hPJKCy8AJ67psjyyJ5wt0snN2fZZV/T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+eBBx5IWJ1WrlxJy5YtyczMjJTddtttPPDAA1x++eWRsn/84x80a9aML774gtzcXAD27t1Lq1at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AAAAAAKDC4ZAWAAAAAABQ4VB4AAAAAACACofCAwAAAAAAVDgUHgAAAAAAoMKh8AAA\nAAAAABUOhQcAAAAAAKhwKDwAAAAAAECFQ+EBAAAAAAAqnP8PPUHKg1qq35YAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x17d7314ef28>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = forecasts['REAL'].plot(figsize=(15,10), title='Resultados VAR 4 Variáveis', marker='o')\n",
    "x['VAR_X1234'].plot(ax=ax, colormap='Dark2')\n",
    "plt.legend(bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.)\n",
    "sns.despine()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}