series_temporais.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "ad834a72-91fd-48fa-b669-511d72a36787",
   "metadata": {
    "tags": []
   },
   "source": [
    "# Tratamento de Séries Temporais\n",
    "\n",
    "## Importação das bibliotecas e configurações iniciais"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "f96b3597-c3b3-48ea-8f37-82077d91f23f",
   "metadata": {},
   "outputs": [],
   "source": [
    "from IPython.core.interactiveshell import InteractiveShell\n",
    "InteractiveShell.ast_node_interactivity = \"all\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 406,
   "id": "1b864b98-d69b-4727-81a0-b3cbdda64127",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Data analysis in general\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "# Statistics\n",
    "import statsmodels.api as sm\n",
    "import statsmodels.tsa.stattools as ts\n",
    "from statsmodels.tsa.api import VAR\n",
    "import pmdarima as pm\n",
    "\n",
    "# For plotting\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "# For date manipulation\n",
    "from datetime import timedelta"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "94c81a86-cebe-4d90-8a4b-5ef59603ec19",
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "id": "c3cf46f3-390e-41f7-8174-5247c745b350",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PIB</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Data</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1985-01-01</th>\n",
       "      <td>1.819572e+12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1986-01-01</th>\n",
       "      <td>1.930043e+12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1987-01-01</th>\n",
       "      <td>2.039988e+12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1988-01-01</th>\n",
       "      <td>2.043433e+12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1989-01-01</th>\n",
       "      <td>2.076605e+12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-01-01</th>\n",
       "      <td>2.041445e+12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1991-01-01</th>\n",
       "      <td>2.078752e+12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1992-01-01</th>\n",
       "      <td>2.085247e+12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1993-01-01</th>\n",
       "      <td>2.189059e+12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1994-01-01</th>\n",
       "      <td>2.300632e+12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1995-01-01</th>\n",
       "      <td>2.393619e+12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1996-01-01</th>\n",
       "      <td>2.444186e+12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1997-01-01</th>\n",
       "      <td>2.526203e+12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1998-01-01</th>\n",
       "      <td>2.527164e+12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1999-01-01</th>\n",
       "      <td>2.534269e+12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-01</th>\n",
       "      <td>2.643750e+12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2001-01-01</th>\n",
       "      <td>2.678993e+12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002-01-01</th>\n",
       "      <td>2.413085e+12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2003-01-01</th>\n",
       "      <td>2.448912e+12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2004-01-01</th>\n",
       "      <td>2.568294e+12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2005-01-01</th>\n",
       "      <td>2.650760e+12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006-01-01</th>\n",
       "      <td>2.760735e+12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007-01-01</th>\n",
       "      <td>2.935756e+12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008-01-01</th>\n",
       "      <td>3.055982e+12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009-01-01</th>\n",
       "      <td>3.089808e+12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-01</th>\n",
       "      <td>3.302840e+12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-01-01</th>\n",
       "      <td>3.434739e+12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-01</th>\n",
       "      <td>3.501687e+12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-01</th>\n",
       "      <td>3.622807e+12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-01</th>\n",
       "      <td>3.668288e+12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-01</th>\n",
       "      <td>3.535670e+12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-01</th>\n",
       "      <td>3.438249e+12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-01</th>\n",
       "      <td>3.471894e+12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-01</th>\n",
       "      <td>3.562496e+12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-01</th>\n",
       "      <td>3.617015e+12</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     PIB\n",
       "Data                    \n",
       "1985-01-01  1.819572e+12\n",
       "1986-01-01  1.930043e+12\n",
       "1987-01-01  2.039988e+12\n",
       "1988-01-01  2.043433e+12\n",
       "1989-01-01  2.076605e+12\n",
       "1990-01-01  2.041445e+12\n",
       "1991-01-01  2.078752e+12\n",
       "1992-01-01  2.085247e+12\n",
       "1993-01-01  2.189059e+12\n",
       "1994-01-01  2.300632e+12\n",
       "1995-01-01  2.393619e+12\n",
       "1996-01-01  2.444186e+12\n",
       "1997-01-01  2.526203e+12\n",
       "1998-01-01  2.527164e+12\n",
       "1999-01-01  2.534269e+12\n",
       "2000-01-01  2.643750e+12\n",
       "2001-01-01  2.678993e+12\n",
       "2002-01-01  2.413085e+12\n",
       "2003-01-01  2.448912e+12\n",
       "2004-01-01  2.568294e+12\n",
       "2005-01-01  2.650760e+12\n",
       "2006-01-01  2.760735e+12\n",
       "2007-01-01  2.935756e+12\n",
       "2008-01-01  3.055982e+12\n",
       "2009-01-01  3.089808e+12\n",
       "2010-01-01  3.302840e+12\n",
       "2011-01-01  3.434739e+12\n",
       "2012-01-01  3.501687e+12\n",
       "2013-01-01  3.622807e+12\n",
       "2014-01-01  3.668288e+12\n",
       "2015-01-01  3.535670e+12\n",
       "2016-01-01  3.438249e+12\n",
       "2017-01-01  3.471894e+12\n",
       "2018-01-01  3.562496e+12\n",
       "2019-01-01  3.617015e+12"
      ]
     },
     "execution_count": 152,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pib = pd.read_excel('dados/PIB.xls')\n",
    "\n",
    "pib = pib.iloc[:, 2:].T\n",
    "pib.columns = ['PIB']\n",
    "\n",
    "pib['Data'] = pib.index.astype('str')\n",
    "pib['Data'] = pib['Data'].str.slice(0, 4) + '-' + pib['Data'].str.slice(4, 6) + '-' + pib['Data'].str.slice(6)\n",
    "pib['Data'] = pd.to_datetime(pib['Data'])\n",
    "pib = pib.set_index('Data')\n",
    "pib['PIB'] = pib['PIB'] * 1000\n",
    "# Os dados só são anuais a partir de 1985\n",
    "pib = pib.loc['1985-01-01':] \n",
    "\n",
    "pib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "id": "8d5daa2d-3e9b-4cc5-8a67-22a6af5c71ec",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Data'>"
      ]
     },
     "execution_count": 153,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pib.plot(figsize=((20, 10)))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "29c7c3b5-1743-496c-affa-db389b19075c",
   "metadata": {
    "tags": []
   },
   "source": [
    "## Verificação de estacionariedade\n",
    "\n",
    "Para uma série ser estacionária, sua média e variância devem ser constantes no tempo:\n",
    "\n",
    "$$\\bar{\\mu} = f(tempo)$$\n",
    "$$\\bar{\\sigma}^2 = f(tempo)$$\n",
    "\n",
    "É interessante trabalhar com séries estacionárias pois a distribuição dos valores em uma série não-estacionária não é igual no tempo, o que pode gerar cálculos imprecisos.\n",
    "\n",
    "Será utilizado o teste de Dickey-Fuller aumentado (ADF - _Augmented Dickey-Fuller_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "id": "b7f0bc4f-2b9b-47b0-90a6-200d4726f9f8",
   "metadata": {},
   "outputs": [],
   "source": [
    "from statsmodels.tsa.stattools import adfuller\n",
    "\n",
    "def adf_test(timeseries):\n",
    "    print(\"Results of Dickey-Fuller Test:\")\n",
    "    dftest = adfuller(timeseries, autolag=\"AIC\")\n",
    "    dfoutput = pd.Series(\n",
    "        dftest[0:4],\n",
    "        index=[\n",
    "            \"Test Statistic\",\n",
    "            \"p-value\",\n",
    "            \"#Lags Used\",\n",
    "            \"Number of Observations Used\",\n",
    "        ],\n",
    "    )\n",
    "    for key, value in dftest[4].items():\n",
    "        dfoutput[\"Critical Value (%s)\" % key] = value\n",
    "    print(dfoutput)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "id": "9418e752-590e-494f-8437-d20cfe782a68",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Results of Dickey-Fuller Test:\n",
      "Test Statistic                 -0.452411\n",
      "p-value                         0.900973\n",
      "#Lags Used                      1.000000\n",
      "Number of Observations Used    33.000000\n",
      "Critical Value (1%)            -3.646135\n",
      "Critical Value (5%)            -2.954127\n",
      "Critical Value (10%)           -2.615968\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "adf_test(pib)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "82c63216-8aca-4598-9cc4-b1eb7724b0d7",
   "metadata": {
    "tags": []
   },
   "source": [
    "Com este p-valor, confirma-se a hipótese nula de não-estacionaridade.\n",
    "\n",
    "Então, testa-se se a série, com apenas a diferença de um grau ($Y_t - Y_{t-1}$) leva-a à estacionariedade.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "id": "017f5497-a103-4515-877c-da173ed6fac5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Data'>"
      ]
     },
     "execution_count": 156,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pib_diff = pib.diff(1).dropna()\n",
    "pib_diff.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "id": "5b5dcbe5-1595-41b8-93bf-c30b5108f85d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Results of Dickey-Fuller Test:\n",
      "Test Statistic                 -4.169681\n",
      "p-value                         0.000741\n",
      "#Lags Used                      0.000000\n",
      "Number of Observations Used    33.000000\n",
      "Critical Value (1%)            -3.646135\n",
      "Critical Value (5%)            -2.954127\n",
      "Critical Value (10%)           -2.615968\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "adf_test(pib_diff)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b88d3b2d-2ae4-4e87-9b72-cf6efe187165",
   "metadata": {},
   "source": [
    "Desta forma, é possível considerar que a nova série já está estacionada."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4f2453ef-d78c-49cc-8592-e6ce0b93bd86",
   "metadata": {},
   "source": [
    "## Autocorrelação e autocorrelação parcial\n",
    "\n",
    "A autocorrelação mostra o quanto o termo $t$ têm de influência dos termos $t-1, t-2, \\dots, t-n$. Geralmente os termos mais próximos são mais fortes na autocorrelação. \n",
    "\n",
    "Na autocorrelação parcial estimamos um coeficiente direto $\\beta$ de modo que $Y_t = \\beta Y_{t-n}$, de modo generalizado. Assim, esta é mais sensível ao termo defasado do que a correlação entre termos.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "id": "4876810a-236b-4c85-adbf-59f0b1803846",
   "metadata": {},
   "outputs": [],
   "source": [
    "from statsmodels.graphics.tsaplots import plot_acf, plot_pacf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "id": "38a49454-e321-42d3-944c-6c5828f7916d",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Usuario\\anaconda3\\lib\\site-packages\\statsmodels\\graphics\\tsaplots.py:348: FutureWarning: The default method 'yw' can produce PACF values outside of the [-1,1] interval. After 0.13, the default will change tounadjusted Yule-Walker ('ywm'). You can use this method now by setting method='ywm'.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1440x1152 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(nrows=2, ncols=1, \n",
    "                       figsize=(20, 16))\n",
    "plot_acf(pib, ax=ax[0]);\n",
    "plot_pacf(pib, ax=ax[1]);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "id": "3833d724-6acb-4ffe-9d37-34d0a4eda45f",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Usuario\\anaconda3\\lib\\site-packages\\statsmodels\\graphics\\tsaplots.py:348: FutureWarning: The default method 'yw' can produce PACF values outside of the [-1,1] interval. After 0.13, the default will change tounadjusted Yule-Walker ('ywm'). You can use this method now by setting method='ywm'.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x1152 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(nrows=2, ncols=1, \n",
    "                       figsize=(20, 16))\n",
    "plot_acf(pib_diff, ax=ax[0]);\n",
    "plot_pacf(pib_diff, ax=ax[1]);"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "62fc98a6-a1ae-4751-a04b-8e4eb563bb83",
   "metadata": {},
   "source": [
    "## Modelos ARIMA\n",
    "\n",
    "ARIMA (_AutoRegressive Integrated Moving Average_) é um modelo que junta os modelos autorregressivos e de média móvel (ARMA) com o grau de diferenciação (em quantas diferenças a série fica estacionária). Com este modelo, pode-se considerar que o grau é zero - $I(0)$. \n",
    "\n",
    "### Modelo autorregressivo\n",
    "\n",
    "É um modelo em que o valor presente depende em algum grau do valor passado (ou valores passados). Genericamente tem-se, para o grau $p$, a fórmula\n",
    "$$Y_t = c + \\phi_1Y_{t-1} + \\phi_2Y_{t-2} + \\dots + \\phi_pY_{t-p} + \\epsilon_t$$\n",
    "\n",
    "### Modelo de média móvel\n",
    "\n",
    "É um modelo que o componente atual depende do erro aleatório do período passado. \n",
    "\n",
    "A fórmula para MA de grau $q$ é\n",
    "$$Y_t = \\mu + \\epsilon_t + \\theta_1\\epsilon_{t-1} + \\theta_2\\epsilon_{t-2} + \\dots + \\theta_q\\epsilon_{t-q}$$\n",
    "\n",
    "### Modelo ARMA\n",
    "\n",
    "É a junção dos dois modelos.\n",
    "\n",
    "$$Y_t = c + \\phi_1Y_{t-1} + \\phi_2Y_{t-2} + \\dots + \\phi_pY_{t-p} + \\theta_1\\epsilon_{t-1} + \\theta_2\\epsilon_{t-2} + \\dots + \\theta_q\\epsilon_{t-q} + \\epsilon_t $$\n",
    "\n",
    "Que pode ser reescrito para:\n",
    "\n",
    "$$Y_t = c + \\sum_{i=1}^p \\phi_iY_{t-i} + \\sum_{j=1}^q \\theta_j\\epsilon_{t-j} + \\epsilon_t$$\n",
    "\n",
    "Os modelos ARIMA são testados caso a caso."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 222,
   "id": "6e2145d9-4226-431a-9fea-d9714fc2ae51",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                               SARIMAX Results                                \n",
      "==============================================================================\n",
      "Dep. Variable:                    PIB   No. Observations:                   35\n",
      "Model:                 ARIMA(1, 1, 1)   Log Likelihood                -905.430\n",
      "Date:                Sat, 13 Aug 2022   AIC                           1816.861\n",
      "Time:                        21:00:04   BIC                           1821.440\n",
      "Sample:                    01-01-1985   HQIC                          1818.422\n",
      "                         - 01-01-2019                                         \n",
      "Covariance Type:                  opg                                         \n",
      "==============================================================================\n",
      "                 coef    std err          z      P>|z|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "ar.L1          0.9985      0.043     23.232      0.000       0.914       1.083\n",
      "ma.L1         -0.9905      0.141     -7.002      0.000      -1.268      -0.713\n",
      "sigma2      8.635e+21   7.87e-25    1.1e+46      0.000    8.64e+21    8.64e+21\n",
      "===================================================================================\n",
      "Ljung-Box (L1) (Q):                   3.08   Jarque-Bera (JB):                28.47\n",
      "Prob(Q):                              0.08   Prob(JB):                         0.00\n",
      "Heteroskedasticity (H):               3.51   Skew:                            -1.48\n",
      "Prob(H) (two-sided):                  0.05   Kurtosis:                         6.36\n",
      "===================================================================================\n",
      "\n",
      "Warnings:\n",
      "[1] Covariance matrix calculated using the outer product of gradients (complex-step).\n",
      "[2] Covariance matrix is singular or near-singular, with condition number 3.09e+63. Standard errors may be unstable.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Usuario\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency AS-JAN will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\Usuario\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency AS-JAN will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\Usuario\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency AS-JAN will be used.\n",
      "  self._init_dates(dates, freq)\n"
     ]
    }
   ],
   "source": [
    "from statsmodels.tsa.arima.model import ARIMA\n",
    "\n",
    "arima = ARIMA(pib, order=(1, 1, 1))\n",
    "modelo = arima.fit()\n",
    "print(modelo.summary())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "43d8d95e-a769-431d-8adc-f0bc772c05e7",
   "metadata": {},
   "source": [
    "### Como escolher o melhor modelo\n",
    "\n",
    "O melhor modelo seria aquele com menores índices de informação AIC e BIC. Quanto maiores estes valores, disponíveis no sumário do modelo, pior ajustado ele está em relação aos dados. **Menos é mais.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 223,
   "id": "f3903b89-679f-4fe7-8477-71a06cb264bf",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Usuario\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency AS-JAN will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\Usuario\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency AS-JAN will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\Usuario\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency AS-JAN will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\Usuario\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency AS-JAN will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\Usuario\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency AS-JAN will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\Usuario\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency AS-JAN will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\Usuario\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\statespace\\sarimax.py:966: UserWarning: Non-stationary starting autoregressive parameters found. Using zeros as starting parameters.\n",
      "  warn('Non-stationary starting autoregressive parameters'\n",
      "C:\\Users\\Usuario\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\statespace\\sarimax.py:978: UserWarning: Non-invertible starting MA parameters found. Using zeros as starting parameters.\n",
      "  warn('Non-invertible starting MA parameters found.'\n",
      "C:\\Users\\Usuario\\anaconda3\\lib\\site-packages\\statsmodels\\base\\model.py:604: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "C:\\Users\\Usuario\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency AS-JAN will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\Usuario\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency AS-JAN will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\Usuario\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency AS-JAN will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\Usuario\\anaconda3\\lib\\site-packages\\statsmodels\\base\\model.py:604: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "C:\\Users\\Usuario\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency AS-JAN will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\Usuario\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency AS-JAN will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\Usuario\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency AS-JAN will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\Usuario\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency AS-JAN will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\Usuario\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency AS-JAN will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\Usuario\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency AS-JAN will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\Usuario\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency AS-JAN will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\Usuario\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency AS-JAN will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\Usuario\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency AS-JAN will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\Usuario\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency AS-JAN will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\Usuario\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency AS-JAN will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\Usuario\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency AS-JAN will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\Usuario\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency AS-JAN will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\Usuario\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency AS-JAN will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\Usuario\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency AS-JAN will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\Usuario\\anaconda3\\lib\\site-packages\\statsmodels\\base\\model.py:604: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "C:\\Users\\Usuario\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency AS-JAN will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\Usuario\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency AS-JAN will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\Usuario\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency AS-JAN will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\Usuario\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency AS-JAN will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\Usuario\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency AS-JAN will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\Usuario\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency AS-JAN will be used.\n",
      "  self._init_dates(dates, freq)\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>aic</th>\n",
       "      <th>bic</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>order</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>(1, 1, 1)</th>\n",
       "      <td>1816.860506</td>\n",
       "      <td>1821.439588</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(2, 1, 1)</th>\n",
       "      <td>1817.394911</td>\n",
       "      <td>1823.500353</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(1, 1, 2)</th>\n",
       "      <td>1817.952903</td>\n",
       "      <td>1824.058345</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(3, 1, 1)</th>\n",
       "      <td>1819.197602</td>\n",
       "      <td>1826.829405</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(1, 1, 3)</th>\n",
       "      <td>1820.619331</td>\n",
       "      <td>1828.251134</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(2, 1, 2)</th>\n",
       "      <td>1820.690806</td>\n",
       "      <td>1828.322608</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(1, 1, 0)</th>\n",
       "      <td>1821.235180</td>\n",
       "      <td>1824.287901</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(0, 1, 1)</th>\n",
       "      <td>1821.377283</td>\n",
       "      <td>1824.430004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(2, 1, 3)</th>\n",
       "      <td>1821.958699</td>\n",
       "      <td>1831.116862</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(3, 1, 2)</th>\n",
       "      <td>1823.317002</td>\n",
       "      <td>1832.475165</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   aic          bic\n",
       "order                              \n",
       "(1, 1, 1)  1816.860506  1821.439588\n",
       "(2, 1, 1)  1817.394911  1823.500353\n",
       "(1, 1, 2)  1817.952903  1824.058345\n",
       "(3, 1, 1)  1819.197602  1826.829405\n",
       "(1, 1, 3)  1820.619331  1828.251134\n",
       "(2, 1, 2)  1820.690806  1828.322608\n",
       "(1, 1, 0)  1821.235180  1824.287901\n",
       "(0, 1, 1)  1821.377283  1824.430004\n",
       "(2, 1, 3)  1821.958699  1831.116862\n",
       "(3, 1, 2)  1823.317002  1832.475165"
      ]
     },
     "execution_count": 223,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "orders = (\n",
    "    (1, 1, 1),\n",
    "    (2, 1, 1),\n",
    "    (3, 1, 1),\n",
    "    (1, 1, 2),\n",
    "    (1, 1, 3),\n",
    "    (2, 1, 2),\n",
    "    (2, 1, 3),\n",
    "    (3, 1, 2),\n",
    "    (1, 1, 0),\n",
    "    (0, 1, 1)\n",
    ")\n",
    "\n",
    "def compare_models(y, orders):\n",
    "    models = []\n",
    "    for order in orders:\n",
    "        model = ARIMA(y, order=order)\n",
    "        resul = model.fit()\n",
    "        models.append([order, resul.aic, resul.bic])\n",
    "    df = pd.DataFrame(models, columns=[\"order\", \"aic\", \"bic\"])\n",
    "    return df.set_index(\"order\").sort_values(by=\"aic\")\n",
    "\n",
    "models = compare_models(pib['PIB'], orders)\n",
    "\n",
    "models"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "76242b62-a2db-4aba-a486-57e818a857ce",
   "metadata": {},
   "source": [
    "Dessa forma pode-se ver que o modelo simples de order P,Q(1,1) é o que possui melhor resultado. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "294844e6-0dae-4dd4-bc13-e09b60646fa1",
   "metadata": {},
   "source": [
    "Porém, é possível utilizar a função `auto_arima` do pacote pmdarima para identificar qual seria o melhor modelo também."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 217,
   "id": "5c13768a-1a92-4a6d-a0a2-0b65786cfdc4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Performing stepwise search to minimize aic\n",
      " ARIMA(1,2,1)(0,0,0)[0] intercept   : AIC=1772.822, Time=0.06 sec\n",
      " ARIMA(0,2,0)(0,0,0)[0] intercept   : AIC=1773.107, Time=0.01 sec\n",
      " ARIMA(1,2,0)(0,0,0)[0] intercept   : AIC=1774.906, Time=0.01 sec\n",
      " ARIMA(0,2,1)(0,0,0)[0] intercept   : AIC=1774.930, Time=0.02 sec\n",
      " ARIMA(0,2,0)(0,0,0)[0]             : AIC=1771.174, Time=0.01 sec\n",
      "\n",
      "Best model:  ARIMA(0,2,0)(0,0,0)[0]          \n",
      "Total fit time: 0.131 seconds\n"
     ]
    }
   ],
   "source": [
    "modelo_auto = pm.auto_arima(pib.PIB, \n",
    "                            start_p=1, start_q=1,\n",
    "                            test='adf',\n",
    "                            max_p=5, max_q=5,\n",
    "                            m=1,\n",
    "                            d=None,\n",
    "                            seasonal=False,\n",
    "                            start_P=0,\n",
    "                            D=0,\n",
    "                            trace=True,\n",
    "                            error_action='ignore', \n",
    "                            suppress_warnings=True, \n",
    "                            stepwise=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 241,
   "id": "5537aadf-86e3-42b8-b123-ff1976496e40",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "modelo_auto.plot_diagnostics(figsize=(10, 10));"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 413,
   "id": "1779dba2-0a37-4bdd-b5df-1639225e01f0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 1080x720 with 0 Axes>"
      ]
     },
     "execution_count": 413,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x2418fafb340>]"
      ]
     },
     "execution_count": 413,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x2418fd2d040>]"
      ]
     },
     "execution_count": 413,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PolyCollection at 0x2418fd1fd60>"
      ]
     },
     "execution_count": 413,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Previsão do PIB')"
      ]
     },
     "execution_count": 413,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "n_periods = 5\n",
    "fc, confint = modelo_auto.predict(n_periods=n_periods, return_conf_int=True)\n",
    "\n",
    "pib_with_fc = pib.copy()\n",
    "for n in range(1, n_periods+1):\n",
    "    new_date = pib.iloc[[-1]].index + timedelta(days=365*n)\n",
    "    pib_with_fc = pd.concat([pib_with_fc,\n",
    "                             pd.DataFrame(data=fc[n-1], \n",
    "                                          index=[new_date[0]], \n",
    "                                          columns=['PIB'])])\n",
    "\n",
    "lower_series = pd.Series(confint[:, 0], index=pib_with_fc.index[-5:])\n",
    "upper_series = pd.Series(confint[:, 1], index=pib_with_fc.index[-5:])\n",
    "\n",
    "\n",
    "# Plot\n",
    "plt.figure(figsize=(15, 10))\n",
    "plt.plot(pib.PIB)\n",
    "plt.plot(pib_with_fc.iloc[-6:, :], color='darkgreen')\n",
    "plt.fill_between(lower_series.index, \n",
    "                 lower_series, \n",
    "                 upper_series, \n",
    "                 color='k', alpha=.15)\n",
    "\n",
    "plt.title(\"Previsão do PIB\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 340,
   "id": "e3c0a33c-2093-439c-b317-6427be6319bc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>empregados_a_cada_1000_pessoas</th>\n",
       "      <th>produtividade_do_trabalho</th>\n",
       "      <th>salario_real</th>\n",
       "      <th>taxa_de_desemprego</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>tempo</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1980-01-01</th>\n",
       "      <td>929.610514</td>\n",
       "      <td>405.366466</td>\n",
       "      <td>386.136109</td>\n",
       "      <td>7.53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1980-04-01</th>\n",
       "      <td>929.803985</td>\n",
       "      <td>404.639834</td>\n",
       "      <td>388.135759</td>\n",
       "      <td>7.70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1980-07-01</th>\n",
       "      <td>930.318388</td>\n",
       "      <td>403.814883</td>\n",
       "      <td>390.540113</td>\n",
       "      <td>7.47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1980-10-01</th>\n",
       "      <td>931.427687</td>\n",
       "      <td>404.215773</td>\n",
       "      <td>393.963817</td>\n",
       "      <td>7.27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1981-01-01</th>\n",
       "      <td>932.662006</td>\n",
       "      <td>405.046714</td>\n",
       "      <td>396.764691</td>\n",
       "      <td>7.37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1999-10-01</th>\n",
       "      <td>959.488142</td>\n",
       "      <td>415.701580</td>\n",
       "      <td>467.702562</td>\n",
       "      <td>6.93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-01</th>\n",
       "      <td>960.362493</td>\n",
       "      <td>416.867407</td>\n",
       "      <td>469.134788</td>\n",
       "      <td>6.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-04-01</th>\n",
       "      <td>960.783379</td>\n",
       "      <td>417.610399</td>\n",
       "      <td>469.336420</td>\n",
       "      <td>6.70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-07-01</th>\n",
       "      <td>961.029030</td>\n",
       "      <td>418.002980</td>\n",
       "      <td>470.011666</td>\n",
       "      <td>6.93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-10-01</th>\n",
       "      <td>961.765710</td>\n",
       "      <td>417.266680</td>\n",
       "      <td>469.647234</td>\n",
       "      <td>6.87</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>84 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            empregados_a_cada_1000_pessoas  produtividade_do_trabalho  \\\n",
       "tempo                                                                   \n",
       "1980-01-01                      929.610514                 405.366466   \n",
       "1980-04-01                      929.803985                 404.639834   \n",
       "1980-07-01                      930.318388                 403.814883   \n",
       "1980-10-01                      931.427687                 404.215773   \n",
       "1981-01-01                      932.662006                 405.046714   \n",
       "...                                    ...                        ...   \n",
       "1999-10-01                      959.488142                 415.701580   \n",
       "2000-01-01                      960.362493                 416.867407   \n",
       "2000-04-01                      960.783379                 417.610399   \n",
       "2000-07-01                      961.029030                 418.002980   \n",
       "2000-10-01                      961.765710                 417.266680   \n",
       "\n",
       "            salario_real  taxa_de_desemprego  \n",
       "tempo                                         \n",
       "1980-01-01    386.136109                7.53  \n",
       "1980-04-01    388.135759                7.70  \n",
       "1980-07-01    390.540113                7.47  \n",
       "1980-10-01    393.963817                7.27  \n",
       "1981-01-01    396.764691                7.37  \n",
       "...                  ...                 ...  \n",
       "1999-10-01    467.702562                6.93  \n",
       "2000-01-01    469.134788                6.80  \n",
       "2000-04-01    469.336420                6.70  \n",
       "2000-07-01    470.011666                6.93  \n",
       "2000-10-01    469.647234                6.87  \n",
       "\n",
       "[84 rows x 4 columns]"
      ]
     },
     "execution_count": 340,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "canada = pd.read_csv('dados/dataset_canada.csv')\n",
    "canada['tempo'] = pd.to_datetime(canada['tempo'])\n",
    "canada.set_index('tempo', inplace=True)\n",
    "canada"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 366,
   "id": "dd0b2781-3c92-4234-8bda-e1be7403bc0c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "canada.plot(subplots=True, figsize=(15, 10));"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.9.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}