Teste_de_Estacionaridade.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Teste de Estacionaridade.ipynb",
      "provenance": [],
      "collapsed_sections": [
        "j2wSVcDKu77o",
        "kam32IAju2Xa",
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        "coo8jJh704OX",
        "O66d6qLc1Ig-",
        "0AUwFC4H1Pky"
      ]
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rWgnpUqtu6LI"
      },
      "source": [
        "#Teste de Estacionaridade"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "j2wSVcDKu77o"
      },
      "source": [
        "##Importar Libs"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FdC6NB6oujAz"
      },
      "source": [
        "import numpy as np                    #Manipulações Matemáticas\n",
        "import pandas as pd                   #Manipulações de DataFrames e Series\n",
        "import warnings                       #Supressão de FutureWarnings\n",
        "import matplotlib.pyplot as plt       #Manipulações Gráficas\n",
        "import statsmodels.tsa.stattools      #Realizações de Testes Estátisticos\n",
        "from matplotlib.pylab import rcParams #Alteração de parametros de gráficos do matplotlib"
      ],
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bIFAnl9EuqlC"
      },
      "source": [
        "warnings.filterwarnings('ignore')\n",
        "rcParams['figure.figsize'] = 15, 6"
      ],
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kam32IAju2Xa"
      },
      "source": [
        "##Criação da Série"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qPMA46hku4th",
        "outputId": "c5e26ef4-79fe-4678-d9ca-372f6e68a382"
      },
      "source": [
        "np.random.seed(10)\n",
        "dados1 = np.random.normal(0, 1, 41)\n",
        "dados1"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([ 1.3315865 ,  0.71527897, -1.54540029, -0.00838385,  0.62133597,\n",
              "       -0.72008556,  0.26551159,  0.10854853,  0.00429143, -0.17460021,\n",
              "        0.43302619,  1.20303737, -0.96506567,  1.02827408,  0.22863013,\n",
              "        0.44513761, -1.13660221,  0.13513688,  1.484537  , -1.07980489,\n",
              "       -1.97772828, -1.7433723 ,  0.26607016,  2.38496733,  1.12369125,\n",
              "        1.67262221,  0.09914922,  1.39799638, -0.27124799,  0.61320418,\n",
              "       -0.26731719, -0.54930901,  0.1327083 , -0.47614201,  1.30847308,\n",
              "        0.19501328,  0.40020999, -0.33763234,  1.25647226, -0.7319695 ,\n",
              "        0.66023155])"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 203
        },
        "id": "zms2OVN-vSOc",
        "outputId": "35bd3a0b-f308-432f-cbd8-63ac3ce07c46"
      },
      "source": [
        "dados1 = pd.DataFrame(dados1, columns=['valores'])\n",
        "ddados1.head()"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>valores</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1.331587</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.715279</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>-1.545400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>-0.008384</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.621336</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "    valores\n",
              "0  1.331587\n",
              "1  0.715279\n",
              "2 -1.545400\n",
              "3 -0.008384\n",
              "4  0.621336"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TXGewRBOvbO2",
        "outputId": "3fa61d04-0d85-4d06-cb2d-b79b36ebbab9"
      },
      "source": [
        "dados1.shape"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(41, 1)"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hsIrq_ICvgye",
        "outputId": "32913c32-3ba7-49cc-ee31-53b0544cec98"
      },
      "source": [
        "indice = pd.date_range('1980', periods=len(dados1), freq='Y')\n",
        "indice"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DatetimeIndex(['1980-12-31', '1981-12-31', '1982-12-31', '1983-12-31',\n",
              "               '1984-12-31', '1985-12-31', '1986-12-31', '1987-12-31',\n",
              "               '1988-12-31', '1989-12-31', '1990-12-31', '1991-12-31',\n",
              "               '1992-12-31', '1993-12-31', '1994-12-31', '1995-12-31',\n",
              "               '1996-12-31', '1997-12-31', '1998-12-31', '1999-12-31',\n",
              "               '2000-12-31', '2001-12-31', '2002-12-31', '2003-12-31',\n",
              "               '2004-12-31', '2005-12-31', '2006-12-31', '2007-12-31',\n",
              "               '2008-12-31', '2009-12-31', '2010-12-31', '2011-12-31',\n",
              "               '2012-12-31', '2013-12-31', '2014-12-31', '2015-12-31',\n",
              "               '2016-12-31', '2017-12-31', '2018-12-31', '2019-12-31',\n",
              "               '2020-12-31'],\n",
              "              dtype='datetime64[ns]', freq='A-DEC')"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pPcZeIVTvodl"
      },
      "source": [
        "serie1 = pd.Series(dados1['valores'].values, index=indice)"
      ],
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 404
        },
        "id": "44j5WmOwvyOs",
        "outputId": "1b1c4cfd-ebde-4060-9c8d-406beff02020"
      },
      "source": [
        "serie1.plot()\n",
        "plt.title('Dados aleatórios de 1980 à 2020')\n",
        "plt.xlabel('Anos')\n",
        "plt.ylabel('Valores')\n",
        "plt.show()"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1080x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GrXr5bssu5Wf"
      },
      "source": [
        "##Testes de Estacionaridade"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "E44z8MjuwDPK"
      },
      "source": [
        "###Teste KPSS (Kwiatkowski-Phillips-Schmidt-Shin)\n",
        "\n",
        "* H0 = Não é estacionário: Estátistica do teste > valor crítico\n",
        "* H1 = É estacionário: Estátistica do teste < valor crítico"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JHFCz1ddwsLl",
        "outputId": "cc3f9ba8-25a0-42ed-e89e-545a453da547"
      },
      "source": [
        "kpss = statsmodels.tsa.stattools.kpss(serie1);\n",
        "\n",
        "print('Estátistica do Teste: {:.4f}'.format(kpss[0]))\n",
        "print('p-value: {:.4f}'.format(kpss[1]))\n",
        "print('Número de Lags: {}'.format(kpss[2]))\n",
        "print('\\n Valores Críticos:')\n",
        "\n",
        "for chave, valor in kpss[3].items():\n",
        "  if kpss[0] < valor:\n",
        "    print('Estacionário - {}: {:.4f}'.format(chave, valor))\n",
        "  else:\n",
        "    print('Não Estacionário - {}: {:.4f}'.format(chave, valor))"
      ],
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Estátistica do Teste: 0.1918\n",
            "p-value: 0.1000\n",
            "Número de Lags: 10\n",
            "\n",
            " Valores Críticos:\n",
            "Estacionário - 10%: 0.3470\n",
            "Estacionário - 5%: 0.4630\n",
            "Estacionário - 2.5%: 0.5740\n",
            "Estacionário - 1%: 0.7390\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YmWxnqX3yudA"
      },
      "source": [
        "###Teste Dickey Fuller\n",
        "\n",
        "* H0 = Não é estacionário: Estátistica do teste > valor crítico\n",
        "* H1 = É estacionário: Estátistica do teste < valor crítico"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rv0A78wmyw94",
        "outputId": "41694aad-1643-4bd7-cee6-225ff7c61b62"
      },
      "source": [
        "df = statsmodels.tsa.stattools.adfuller(serie1);\n",
        "\n",
        "print('Estátistica do Teste: {:.4f}'.format(df[0]))\n",
        "print('p-value: {:.4f}'.format(df[1])) \n",
        "print('\\n Valores Críticos:')\n",
        "\n",
        "for chave, valor in df[4].items():\n",
        "  if df[0] < valor:\n",
        "    print('Estacionário - {}: {:.4f}'.format(chave, valor))\n",
        "  else:\n",
        "    print('Não Estacionário - {}: {:.4f}'.format(chave, valor))"
      ],
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Estátistica do Teste: -5.8642\n",
            "p-value: 0.0000\n",
            "\n",
            " Valores Críticos:\n",
            "Estacionário - 1%: -3.6056\n",
            "Estacionário - 5%: -2.9371\n",
            "Estacionário - 10%: -2.6070\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OjfGKsErzcgX"
      },
      "source": [
        "##Dataset Importado"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7XoL-wABzify"
      },
      "source": [
        "AirPassegers: Dados Clássicos da compainha aérea Box & Jenkins. Totais mensais de passageiros de compainhas aéreas internacionais de 1949 à 1960."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 203
        },
        "id": "Gr6QDrI5zfsS",
        "outputId": "97f34cc6-0200-4265-9621-5fb91eebbd90"
      },
      "source": [
        "serie2 = pd.read_csv('/content/drive/MyDrive/Dataset_Excel_CSV/AirPassengers.csv')\n",
        "serie2.columns = ['mes_anos','qt_passageiros']\n",
        "serie2.head()"
      ],
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>mes_ano</th>\n",
              "      <th>qt_passageiros</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1949-01</td>\n",
              "      <td>112</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1949-02</td>\n",
              "      <td>118</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1949-03</td>\n",
              "      <td>132</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1949-04</td>\n",
              "      <td>129</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1949-05</td>\n",
              "      <td>121</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   mes_ano  qt_passageiros\n",
              "0  1949-01             112\n",
              "1  1949-02             118\n",
              "2  1949-03             132\n",
              "3  1949-04             129\n",
              "4  1949-05             121"
            ]
          },
          "metadata": {},
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "swpMmAh00Si2",
        "outputId": "fe2164fc-9e0c-4597-abbc-2eeb71b1410f"
      },
      "source": [
        "serie2 = pd.Series(serie2['qt_passageiros'].values, index=serie2['mes_ano'])\n",
        "serie2"
      ],
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "mes_ano\n",
              "1949-01    112\n",
              "1949-02    118\n",
              "1949-03    132\n",
              "1949-04    129\n",
              "1949-05    121\n",
              "          ... \n",
              "1960-08    606\n",
              "1960-09    508\n",
              "1960-10    461\n",
              "1960-11    390\n",
              "1960-12    432\n",
              "Length: 144, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 404
        },
        "id": "QL_mJFlI0lkB",
        "outputId": "3c16016f-386a-445c-f873-6ac0ebbd9917"
      },
      "source": [
        "serie2.plot()\n",
        "plt.title('Quantidade de Passageiro por mês e ano entre 1949 à 1960')\n",
        "plt.xlabel('Mês e Ano')\n",
        "plt.ylabel('Quantidade de Passageiros')\n",
        "plt.show()"
      ],
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1080x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "coo8jJh704OX"
      },
      "source": [
        "###Testes de Estacionaridade"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "O66d6qLc1Ig-"
      },
      "source": [
        "####KPSS\n",
        "\n",
        "* H0 = Não é estacionário: Estátistica do teste > valor crítico\n",
        "* H1 = É estacionário: Estátistica do teste < valor crítico"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OWz9BVa406Dq",
        "outputId": "707132f6-ea83-4788-8b94-e78cee572bb0"
      },
      "source": [
        "kpss = statsmodels.tsa.stattools.kpss(serie2);\n",
        "\n",
        "print('Estátistica do Teste: {:.4f}'.format(kpss[0]))\n",
        "print('p-value: {:.4f}'.format(kpss[1]))\n",
        "print('Número de Lags: {}'.format(kpss[2]))\n",
        "print('\\n Valores Críticos:')\n",
        "\n",
        "for chave, valor in kpss[3].items():\n",
        "  if kpss[0] < valor:\n",
        "    print('Estacionário - {}: {:.4f}'.format(chave, valor))\n",
        "  else:\n",
        "    print('Não Estacionário - {}: {:.4f}'.format(chave, valor))"
      ],
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Estátistica do Teste: 1.0522\n",
            "p-value: 0.0100\n",
            "Número de Lags: 14\n",
            "\n",
            " Valores Críticos:\n",
            "Não Estacionário - 10%: 0.3470\n",
            "Não Estacionário - 5%: 0.4630\n",
            "Não Estacionário - 2.5%: 0.5740\n",
            "Não Estacionário - 1%: 0.7390\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0AUwFC4H1Pky"
      },
      "source": [
        "####Dickey Fuller\n",
        "\n",
        "* H0 = Não é estacionário: Estátistica do teste > valor crítico\n",
        "* H1 = É estacionário: Estátistica do teste < valor crítico"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kUf-1Hbm1RxC",
        "outputId": "4fe173a4-bfd1-45da-c2db-6b68111e0b7f"
      },
      "source": [
        "df = statsmodels.tsa.stattools.adfuller(serie2);\n",
        "\n",
        "print('Estátistica do Teste: {:.4f}'.format(df[0]))\n",
        "print('p-value: {:.4f}'.format(df[1])) \n",
        "print('\\n Valores Críticos:')\n",
        "\n",
        "for chave, valor in df[4].items():\n",
        "  if df[0] < valor:\n",
        "    print('Estacionário - {}: {:.4f}'.format(chave, valor))\n",
        "  else:\n",
        "    print('Não Estacionário - {}: {:.4f}'.format(chave, valor))"
      ],
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Estátistica do Teste: 0.8154\n",
            "p-value: 0.9919\n",
            "\n",
            " Valores Críticos:\n",
            "Não Estacionário - 1%: -3.4817\n",
            "Não Estacionário - 5%: -2.8840\n",
            "Não Estacionário - 10%: -2.5788\n"
          ]
        }
      ]
    }
  ]
}