AutoCorrelação.ipynb

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  "metadata": {
    "colab": {
      "name": "AutoCorrelação.ipynb",
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    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "d8cCftYvtzV2"
      },
      "source": [
        "#AutoCorrelação"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2C7R03xZt1ot"
      },
      "source": [
        "##Importar Libs"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3CAqlf3h93NX"
      },
      "source": [
        "Atualizar para a última versão do plotly"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "r7gF01NJ0tvF"
      },
      "source": [
        "!pip install plotly --upgrade"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7plcH9BAt3IN"
      },
      "source": [
        "import numpy as np                                              #Manipulações Matemáticas\n",
        "import pandas as pd                                             #Manipulações de DataFrames e Séries\n",
        "import plotly.express as px                                     #Criação de Gráficos Interativos\n",
        "import matplotlib.pyplot as plt                                 #Manipulações Gráficas\n",
        "import statsmodels.api as sm                                    #Importação de Dataset de Manchas Solares\n",
        "from matplotlib.pylab import rcParams                           #Manipulação de parâmetros de gráficos do matplotlib\n",
        "from statsmodels.graphics.tsaplots import plot_acf, plot_pacf   #Criação de Gráficos de AutoCorrelação"
      ],
      "execution_count": 23,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xJHPYizX0NDd"
      },
      "source": [
        "Ajustando tamanho da figura dos gráficos do matplotlib"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6meX6y5PuAVM"
      },
      "source": [
        "rcParams['figure.figsize'] = 15, 6"
      ],
      "execution_count": 24,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fFLMElsQvWKa"
      },
      "source": [
        "##Primeiro Caso: Série Aleatória"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "B2f8gXmG_aOE"
      },
      "source": [
        "Série aleatória normalizada com valores mensais entre 1970 à 1975"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5TZMz10F-4Lv"
      },
      "source": [
        "###Criação da Série"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NHDGolQ-9_t2"
      },
      "source": [
        "Criar série aleatória baseada em:\n",
        "\n",
        "* Média = 0\n",
        "* Desvio Padrão = 1\n",
        "* Quantidade Valores = 72"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dR3qweVeuEDQ",
        "outputId": "c371aeff-c60a-4950-df86-b777d3cd125b"
      },
      "source": [
        "np.random.seed(6)\n",
        "dados1 = np.random.normal(0, 1, 72)\n",
        "dados1"
      ],
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([-0.31178367,  0.72900392,  0.21782079, -0.8990918 , -2.48678065,\n",
              "        0.91325152,  1.12706373, -1.51409323,  1.63929108, -0.4298936 ,\n",
              "        2.63128056,  0.60182225, -0.33588161,  1.23773784,  0.11112817,\n",
              "        0.12915125,  0.07612761, -0.15512816,  0.63422534,  0.810655  ,\n",
              "        0.35480861,  1.81259031, -1.3564758 , -0.46363197,  0.82465384,\n",
              "       -1.17643148,  1.56448966,  0.71270509, -0.1810066 ,  0.53419953,\n",
              "       -0.58661296, -1.48185327,  0.85724762,  0.94309899,  0.11444143,\n",
              "       -0.02195668, -2.12714455, -0.83440747, -0.46550831,  0.23371059,\n",
              "        1.38503523, -0.51962709, -0.78015214,  0.95560959, -0.12673638,\n",
              "       -1.36861282,  1.21848065, -0.85750144, -0.56147088, -1.0335199 ,\n",
              "        0.35877096,  1.07368134, -0.37550472,  0.39636757, -0.47144628,\n",
              "        2.33660781,  1.50278553, -0.59545972,  0.52834106,  0.9398248 ,\n",
              "        0.42628539, -0.75815703, -0.16236698,  0.72680996,  0.44408297,\n",
              "       -0.85682264,  0.44692842, -1.01464799, -2.1323234 ,  0.17386307,\n",
              "        0.95120126,  0.44189739])"
            ]
          },
          "metadata": {},
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_VVS9vVc-r3Z"
      },
      "source": [
        "Criação do índice"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qBPHw2Uj-XtD",
        "outputId": "7dc657ba-1155-4367-e623-2af6cdfeffd2"
      },
      "source": [
        "indice1 = pd.date_range('1970-01', periods=len(dados1), freq='M')\n",
        "indice1"
      ],
      "execution_count": 51,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DatetimeIndex(['1970-01-31', '1970-02-28', '1970-03-31', '1970-04-30',\n",
              "               '1970-05-31', '1970-06-30', '1970-07-31', '1970-08-31',\n",
              "               '1970-09-30', '1970-10-31', '1970-11-30', '1970-12-31',\n",
              "               '1971-01-31', '1971-02-28', '1971-03-31', '1971-04-30',\n",
              "               '1971-05-31', '1971-06-30', '1971-07-31', '1971-08-31',\n",
              "               '1971-09-30', '1971-10-31', '1971-11-30', '1971-12-31',\n",
              "               '1972-01-31', '1972-02-29', '1972-03-31', '1972-04-30',\n",
              "               '1972-05-31', '1972-06-30', '1972-07-31', '1972-08-31',\n",
              "               '1972-09-30', '1972-10-31', '1972-11-30', '1972-12-31',\n",
              "               '1973-01-31', '1973-02-28', '1973-03-31', '1973-04-30',\n",
              "               '1973-05-31', '1973-06-30', '1973-07-31', '1973-08-31',\n",
              "               '1973-09-30', '1973-10-31', '1973-11-30', '1973-12-31',\n",
              "               '1974-01-31', '1974-02-28', '1974-03-31', '1974-04-30',\n",
              "               '1974-05-31', '1974-06-30', '1974-07-31', '1974-08-31',\n",
              "               '1974-09-30', '1974-10-31', '1974-11-30', '1974-12-31',\n",
              "               '1975-01-31', '1975-02-28', '1975-03-31', '1975-04-30',\n",
              "               '1975-05-31', '1975-06-30', '1975-07-31', '1975-08-31',\n",
              "               '1975-09-30', '1975-10-31', '1975-11-30', '1975-12-31'],\n",
              "              dtype='datetime64[ns]', freq='M')"
            ]
          },
          "metadata": {},
          "execution_count": 51
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Ap37isPr-wTX"
      },
      "source": [
        "Criação da Série"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zVWxNwJz-lAP"
      },
      "source": [
        "serie1 = pd.Series(dados1, index=indice1)"
      ],
      "execution_count": 53,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Yl-SfNUG-KKT"
      },
      "source": [
        "###Gráfico da Série"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "id": "IDG2viC0uKY_",
        "outputId": "5006a9e2-3f02-47e3-ceae-744df294e2f1"
      },
      "source": [
        "fig = px.line(x = serie1.index, y = serie1.values)\n",
        "\n",
        "fig.update_layout(\n",
        "    title = \"Média de Manchas Solares Mensais de 1970 à 1975\",\n",
        "    xaxis_title = \"Anos\",\n",
        "    yaxis_title = \"Valores\")\n",
        "fig.show()"
      ],
      "execution_count": 54,
      "outputs": [
        {
          "output_type": "display_data",
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de Manchas Solares Mensais de 1970 \\u00e0 1975\"},\"xaxis\":{\"anchor\":\"y\",\"domain\":[0.0,1.0],\"title\":{\"text\":\"Anos\"}},\"yaxis\":{\"anchor\":\"x\",\"domain\":[0.0,1.0],\"title\":{\"text\":\"Valores\"}}},                        {\"responsive\": true}                    ).then(function(){\n",
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    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9nNyCxUhvcoJ"
      },
      "source": [
        "###ACF (Sequencial)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 390
        },
        "id": "-5zwP10suO0l",
        "outputId": "fe0c6a3f-a84b-414b-d025-a86d54a3e0a2"
      },
      "source": [
        "plot_acf(serie1, lags=8)\n",
        "plt.show()"
      ],
      "execution_count": 55,
      "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": "8TV0bnt2_NbI"
      },
      "source": [
        "A maioria dos dados está dentro da zona de confiança, não existe uma autoCorrelação significante."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MtctRwh9veMI"
      },
      "source": [
        "###PACF (Aleatório)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 390
        },
        "id": "jtwYWJ2Kut6S",
        "outputId": "94b089e5-f791-4c88-8e8f-57b7c4158900"
      },
      "source": [
        "plot_pacf(serie1, lags=30)\n",
        "plt.show()"
      ],
      "execution_count": 56,
      "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": "HAL45XSa_Vvn"
      },
      "source": [
        "A maioria dos dados está dentro da zona de confiança, não existe uma autoCorrelação significante."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vOaNlIQJvieM"
      },
      "source": [
        "##Segundo Caso: Dados Externos de Manchas Solares"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LXdk4PFNwA-G"
      },
      "source": [
        "Número médio mensal de manchas solares relativas de 1749 à 1983 coletados no Observatório Federal Suíço de Zurique até 1960 e depois no Observatório Astronômico de Tóquio."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6_iN1OzOxtQQ"
      },
      "source": [
        "###Criação da Série"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ppfSQmP49t4m"
      },
      "source": [
        "Importando Base de dados"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 203
        },
        "id": "TQdYQ4rGvp0d",
        "outputId": "50e2889a-e354-490f-d851-915611634a20"
      },
      "source": [
        "dados2 = pd.read_csv('/content/drive/MyDrive/Dataset_Excel_CSV/sunspots.csv')\n",
        "dados2.columns = ['valores']\n",
        "dados2.head()"
      ],
      "execution_count": 37,
      "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>1</th>\n",
              "      <td>58.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>62.6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>70.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>55.7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>85.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   valores\n",
              "1     58.0\n",
              "2     62.6\n",
              "3     70.0\n",
              "4     55.7\n",
              "5     85.0"
            ]
          },
          "metadata": {},
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oJ7H_6PSwj7e"
      },
      "source": [
        "Excluir índices anteriores (O índice iniciou no número 1 ao invés do 0)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ThZguUr2wAdn"
      },
      "source": [
        "dados2 = dados2.reset_index(drop=True) "
      ],
      "execution_count": 38,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-u3F-3nlw_P8"
      },
      "source": [
        "Dados mensais, criar índice baseado em meses referentes ao período"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6bBbMSjVwpLH",
        "outputId": "dd907527-2f67-4d4d-9011-b22b91a849ad"
      },
      "source": [
        "indice = pd.date_range('1749', periods=len(dados2), freq='M')\n",
        "indice"
      ],
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DatetimeIndex(['1749-01-31', '1749-02-28', '1749-03-31', '1749-04-30',\n",
              "               '1749-05-31', '1749-06-30', '1749-07-31', '1749-08-31',\n",
              "               '1749-09-30', '1749-10-31',\n",
              "               ...\n",
              "               '1983-03-31', '1983-04-30', '1983-05-31', '1983-06-30',\n",
              "               '1983-07-31', '1983-08-31', '1983-09-30', '1983-10-31',\n",
              "               '1983-11-30', '1983-12-31'],\n",
              "              dtype='datetime64[ns]', length=2820, freq='M')"
            ]
          },
          "metadata": {},
          "execution_count": 39
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "L6OT_vLo9wgl"
      },
      "source": [
        "Criar Série"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SikQesZww4aH"
      },
      "source": [
        "serie2 = pd.Series(dados2['valores'].values, index = indice)"
      ],
      "execution_count": 40,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Nl6rhvUPxlFb"
      },
      "source": [
        "###Gráfico da Série"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "id": "PF6mCZTY1C3M",
        "outputId": "4350d798-7a29-456f-c521-27b172ce1907"
      },
      "source": [
        "fig = px.line(x = serie2.index, y = serie2.values)\n",
        "\n",
        "fig.update_layout(\n",
        "    title = \"Média de Manchas Solares Mensais de 1749 à 1983\",\n",
        "    xaxis_title = \"Anos\",\n",
        "    yaxis_title = \"Valores\")\n",
        "fig.show()"
      ],
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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de Manchas Solares Mensais de 1749 \\u00e0 1983\"},\"xaxis\":{\"anchor\":\"y\",\"domain\":[0.0,1.0],\"title\":{\"text\":\"Anos\"}},\"yaxis\":{\"anchor\":\"x\",\"domain\":[0.0,1.0],\"title\":{\"text\":\"Valores\"}}},                        {\"responsive\": true}                    ).then(function(){\n",
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    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "X6npgU2Pxnnc"
      },
      "source": [
        "###ACF (Sequencial)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 390
        },
        "id": "xoiLtaMZxW3t",
        "outputId": "5ad8c618-86aa-4600-a087-74979ef6b90a"
      },
      "source": [
        "plot_acf(serie2)\n",
        "plt.show()"
      ],
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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\n",
            "text/plain": [
              "<Figure size 1080x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dgfTpVVw9YKl"
      },
      "source": [
        "Muitos Lags fora da zona de confiança (Alta AutoCorrelação tanto positiva)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dyACsI0sxj8e"
      },
      "source": [
        "###PACF (Aleatória)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 390
        },
        "id": "w4rNGcVjxqPJ",
        "outputId": "5164e0b0-c7e0-4e53-fe10-2baa533eda28"
      },
      "source": [
        "plot_pacf(serie2)\n",
        "plt.show()"
      ],
      "execution_count": 43,
      "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": "rbDyWKZ_9a_F"
      },
      "source": [
        "Muitos Lags fora da zona de confiança (Alta AutoCorrelação tanto positiva como negativa)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mZS7BmItyI-W"
      },
      "source": [
        "##Terceiro Caso: Dados do Python sobre Manchas Solares"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1mS7OS1AyPKw"
      },
      "source": [
        "Dados anuais (1700-2008) sobre manchas solares. Fonte: National Geophysical Data Center"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XTf1eq41zSJZ"
      },
      "source": [
        "###Criação da Série"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0lwyh8Vy9nJj"
      },
      "source": [
        "Importando base de dados"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 203
        },
        "id": "LpSzvuI7yXjD",
        "outputId": "124523c0-7c64-4f9a-d7b8-85c1872af2e9"
      },
      "source": [
        "manchas_solares = sm.datasets.sunspots.load_pandas()['data']\n",
        "manchas_solares.head()"
      ],
      "execution_count": 44,
      "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>YEAR</th>\n",
              "      <th>SUNACTIVITY</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1700.0</td>\n",
              "      <td>5.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1701.0</td>\n",
              "      <td>11.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1702.0</td>\n",
              "      <td>16.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1703.0</td>\n",
              "      <td>23.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1704.0</td>\n",
              "      <td>36.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "     YEAR  SUNACTIVITY\n",
              "0  1700.0          5.0\n",
              "1  1701.0         11.0\n",
              "2  1702.0         16.0\n",
              "3  1703.0         23.0\n",
              "4  1704.0         36.0"
            ]
          },
          "metadata": {},
          "execution_count": 44
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kmkX5dBw9pzv"
      },
      "source": [
        "Criando a série"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3Eo8M9K3yxed",
        "outputId": "ba688610-db61-43e7-99b7-44e4eef04166"
      },
      "source": [
        "serie3 = pd.Series(manchas_solares['SUNACTIVITY'].values, index=manchas_solares['YEAR'])\n",
        "serie3"
      ],
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "YEAR\n",
              "1700.0     5.0\n",
              "1701.0    11.0\n",
              "1702.0    16.0\n",
              "1703.0    23.0\n",
              "1704.0    36.0\n",
              "          ... \n",
              "2004.0    40.4\n",
              "2005.0    29.8\n",
              "2006.0    15.2\n",
              "2007.0     7.5\n",
              "2008.0     2.9\n",
              "Length: 309, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 45
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NWQEX5vGzUWK"
      },
      "source": [
        "###Gráfico da Série"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "id": "ZKgLIv7VzGXb",
        "outputId": "55a80bb7-b687-4647-8db9-627220d30ce6"
      },
      "source": [
        "fig = px.line(x = serie3.index, y = serie3.values)\n",
        "\n",
        "fig.update_layout(\n",
        "    title = \"Média de Manchas Solares Anuais de 1700 à 2008\",\n",
        "    xaxis_title = \"Anos\",\n",
        "    yaxis_title = \"Valores\")\n",
        "fig.show()"
      ],
      "execution_count": 46,
      "outputs": [
        {
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      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DTB4mVj6zY24"
      },
      "source": [
        "###ACF (Sequencial)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 390
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        "id": "0ujyxAAOzZJK",
        "outputId": "84dd0654-827c-49a4-b98f-14edaee9ba9a"
      },
      "source": [
        "plot_acf(serie3, lags=45)\n",
        "plt.show()"
      ],
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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IGYIcAAAAAKQMQQ4AAAAAUoYgBwAAAAApQ5ADAAAAgJQhyAEAAABAyhDkAAAAACBlCHIAAAAAkDIEOQAAAABIGYIcAAAAAKQMQQ4AAAAAUoYgBwAAAAApQ5ADAAAAgJRpSboBQLMLQtfhEyM6en5Mt27q1r4dvcpmLOlmAQAAoI4R5IAEBaHr3kee0eBwTlP5QB1tWQ309ejRA3sJcwAAAFgSQyuBBB0+MaLB4Zwm84Fc0mQ+0OBwTodPjCTdNAAAANQxghyQoKPnxzSVDxZsm8oHOnZ+LKEWAQAAIA0YWgkk6NZN3epoy2oyFuY62rLatak7wVZdG+b6AQAA1A5BLmW4WW4s+3b0aqCvR3/73ZfkmRatbW/VQF+P9u3oTbppK8JcPwAAgNoiyKUIN8uNJ5sxPXpgr970Tw8o39mrT330X6YynMfn+kkL5/rdsXNjwq0DAABoPMyRSxEKYzSmbMa0NndKPeee1h07N6YuxEnM9QMAAKg1glyKcLOMelWa6xeX1rl+AAAAaUCQSxFullGvSnP9LMhLHmptNOw3bXP9AAAA0oIglyLcLKNeleb6bTj55+o5+039wT23MXcTAABgFVHsJEUapTAGGlNprt/a3CkKnKCqqNYLAMDlCHIpw80ygGZCtV4AAMpjaCUAoG5Vo1pvELoOHb+ghw6d1KHjFxSEvnoNBgCgRuiRAwDUreWq9V7NqAR69AAAjaoqPXJmdqeZnTCzITN7YJnj/nczczPbU43zAgAaW6XVell/EwDQqCoOcmaWlfRpSfsl7ZJ0j5ntKnPcdZI+KOmZSs8JAGgOlVbrZf1NAECjqkaP3O2Shtz9lLvnJX1J0t1ljvtNSb8taboK5wQANIFKl7Zg/U0AQKOqRpC7WdJw7PXZaNscM9stqc/d/9tyH2RmB83sOTN77uLFi1VoGgAg7UrVenvOPa07dm5c0dw21t8EADSqVa9aaWYZSb8j6UNXOtbdH3b3Pe6+Z8OGDavdNABAg2OxegBAo6pG1cpzkvpirzdH20quk/T3JR02M0l6naTHzewud3+uCucHAGBJrL8JAGhE1eiRe1bSdjO7xczaJL1b0uOlne7+qrvf6O797t4v6WlJhDgAAAAAuEYVBzl3L0i6X9JTko5Leszdj5rZx83srko/HwAAAACwUFUWBHf3JyQ9sWjbx5Y4dl81zgkAAAAAzWrVi50AAAAAAKqLIAcAAAAAKUOQAwAAAICUqcocOQAAAAAox90VeuxRLncVv7Rwn1wK3eWKHqNj5LHPm/vc+DYvs21hGxbvn98nrets1XVrWqvx7dYMQQ4AAAC4RkHoCt0VhMXQEbjPBRDFQ4uKYSIeQkoBxWOv3eNBpUzquAaLP6b0Oh5+Fp9zQTsXBau5gBVrcyl8+dwxsXCWAu2tGYIcAAAAUC+C0BeErdBdYVgMXMttd/diKAuLISWMQsncvjA9IQWNiSAHAACQkCB0HT4xoqPnx3Trpm7t29GrbMZW5Vzx8BEPJsUAs7DXSLp8+NpcX9LCh/mepcvOt4K2LWpnqbfnsiF2y/QMxY8NQ4IWGh9BDgAAIAFB6Lr3kWc0OJzTVD5QR1tWA309+uzP3S6pGEgKpUASlnqHYtsW9CjND/FbOLyvuG9uqB+AhkGQAwAAdS8MF/UgxYonLOzNKT2JbVtUBKFcAYS4cnmnbIGEWNdUvF3xHqJ4O0tFHkpD9Z459QMdefGSpmdDSdJkPtDzZy7p//3GKe3euu6q/lwANC+CHAAAqLp4z1EhDFUIFr4Oov2lgBOfoxRGc5MavTfp714e00wU4kryhVCnRycIcgCuiCAHAAAWKM2lKsSG7y34clchmD8mCF2zQbjgdcDkpCvqX9+ptpaMZgrzYa6tJaP+9Z0JtgpAWhDkAACoksW9S6XhdYG7PJwvxlDufSs6j65cutxjvVjxfdL8kL/5+VdSIQyL2wIKRNTKQF+PtvV26eiLr0jZFrW3tmhbb5cG+nqSbhqAFCDIAQAQmQ2KQwDzQahCEKoQuvKF4mNpX7liEvMlyZP+DpAmmYzpwf079Qsf/JCCro26//0HNdDXo8wqVa0E0FgIcgCAVVPL0upLnb805M9dGhmbjkJacftsUJyvVXpOEEOtZTKmttEhaXRIu7d+OOnmAEgRghwAYFUsVVr90QN7KwpzpXAWD2T5QjGMlXrQSsGsNE9rYqYgSfrexYmqfG8AACSNIAcAWBVf/7sLGhzOaTIfSCqWVh8czukvvvOS3vKGDXML9gZRpcLSGlnl1saamCnIJf337/+AIhoAAIggBwB1r1QBMIxXEowtDhwsqipYbnHgcutolQpsuOJrZM0fN7ccV3w9rkVjD8uu3xV56ugFTUUhrmQqH+jwiYu6obN9xX8G8UcARWHoGhzO6fTohPrXdzLHDmgiBDkAqJEwdM2W1tOK5mbFi2gU52rNl3IvVRRM67wtSqsDqysMXZ948riGRsaVL4Rqa8loW2+XHty/kzAHNAGCHABchcXrapV6xeKP8TW2gnBhWCsEzbeuViOVVqfXA/VocDinoZHxuV+WzBRCDY2Ma3A4x4LiQBMgyAFInLtrplAsXpEvzFcYvNo1seb2qzS8b+E+KT6kMDZ0sMw2xY4vBrJwbpgiVqZRSqvT64F6dXp0QvlYj7ck5QuhTo9OEOSAJkCQA7DqClGFwZnZ+ceZQqCZQqiZqMJgWocPYnmNUFqdXg/UK4YvA82NIAegKuLDC09dHF8Q3AoBKQ3pRa8H6lUjDV8GsHIEOQDXJAxdr80UNDY1q7HpWY1PFzQZrdV1YWwm4dYB1UOvB+pVowxfBnBtCHIArkoYusbzxeD26lQxuDFtDM2AXg8spR6K4DTC8GUA14YgB6Asd9f4TEGvTs1qbKqg8ZkCBT/QlOj1QDkUwQGQNIIcgDlB6DqXm9LY1Kxemya4ASX0emAxiuAASBpBDmhi+UKoV6dmlZvM67XpgtxdL45OJt0sAKh7FMEBkDSCHNBE3IsFSl6dnFVuclbjUXGS0j4AwNWhCA4aXTXmgNbDPNJGRpADGtxMISgGt6hICUsBAEDlKIKDRlaNOaDMI119maQbAKC63F2vTs3qxdFJfftsTkfO5PS9ixMaHc8T4gCgSkpFcLqOfVUd3/8b/Yu3bucGFQ0jPgfUtXAOaC0/A8sjyAEpN1MIlJvMK18INZkP9OzpSzp2fkznclOamAmSbh4ANKxSEZyOM9/U7q3rCHFoGMvNAa3lZ2B5DK0EUiAMXdOFQFP5QFOzgaZnA03lQ03NBnOVJadni6GNSpMAAKAS1ZgDyjzS1VeVIGdmd0r6fUlZSX/k7p9ctP+XJb1PUkHSRUn/p7ufqca5gUZSCIrhbGp2PrRN5YPisATyGQAAqIFqzAFlHunqqzjImVlW0qclvV3SWUnPmtnj7n4sdtgLkva4+6SZ/TNJ/17ST1V6bqAS7q58EGo2cIXuci9uc5c82l98XPRc88eEUe/XZdujz5Hinze/vXRs6b3jMwW5S8+evpTAnwQAAMC80hzQX/jghxR0bdT97z+44oqT1fgMLK8aPXK3Sxpy91OSZGZfknS3pLkg5+5fjx3/tKSfrcJ5gSUFoStfCJUPwoWPhfnXs0H99HKFDIcEAAB1pDQHVKND2r31w4l9BpZWjSB3s6Th2OuzkvYuc/wBSU9W4bxoYjOFQNOzoWZmi8MOZwrFYFYKaVRnBAAAQCOrabETM/tZSXsk/dgS+w9KOihJW7ZsqWHLUI/iYW16Npwr9jFTCCnoAQAAgKZWjSB3TlJf7PXmaNsCZvY2SR+R9GPuPlPug9z9YUkPS9KePXu4U29wpTlq8bBWqshIWAMAAACWVo0g96yk7WZ2i4oB7t2Sfjp+gJndJuk/SrrT3UeqcE7UAXdXIXQFYfQYuAphOP967jFUIXQVAl+wj6AGAAAAXJuKg5y7F8zsfklPqbj8wGfc/aiZfVzSc+7+uKT/S1KXpD8xM0l60d3vqvTcuDqFINREvjgscTJf0GRU1n65IFUvRUAAAAAAXK4qc+Tc/QlJTyza9rHY87dV4zxYXhi6pmaDYlDLB5qIQls+thAjAAAAgPSrabETVE/orksTeU3OBpqcme9loycNaRaGrsHhnE6PTqh/fSfrzQAAACyBIJcyP5jIa2KmoCB0/d3LryXdnKZH8KieMHR94snjGhoZV74Qqq0lo229XXpw/07+TAEAABYhyKXE6PiMzuWmNDGz/Nw21A7Bo7oGh3MaGhnXTDQUeKYQamhkXIPDOe3eui7h1gEAANSXTNINwNLcXa+Mz+hbwzl998K4JmaCpJuEmHjwcC0MHli506MTl83nzBdCnR6dSKhFAAAA9YseuTpUDHB5nctNaSpPeKtXywUPepBWrn99p9paMnM9cpLU1pJR//rOBFsFAACSwhSW5RHk6oi76+L4jM5dmtL0LJUm6x3Bo7oG+nq0rbdLR198Rcq2qL21Rdt6uzTQ15N00wCgoXGz3Hga4WfKFJYrI8jVAXfXyGvFOXAzBLjUIHhUVyZjenD/Tv3CBz+koGuj7n//wVT+wwMAacLNcuNplJ8pc+evjDlyCQpD18uvTuuF4ZxOXZwgxKVMKXh0HfuqOr7/N/oXb92eur8k600mY2obHVLHmW9q99Z1/FkCwCpjvnfjaZSfKXPnr4wgl5CXXp3SC8M5ff8VAlyaETwAAGnGzXLjaZSfaWkKSxxTWBYiyNVQELryhVCvTRd0+pXJy/4nAwAAqCVulhtPo/xMS1NYVMhLHqo9GiLKFJZ5BLkaCELXudyUXnjxkqZnA7mzDhwAAEgeN8uNp1F+pkxhuTKC3CoqBKHOXprUCy9e0oujk5oNCHAAAKB+cLPceBrpZ8oUluVRtXIVFIJQL706rZfHplUgvAEAgDpWulnW6JB2b/1w0s1BFfAzbQ4EuSqbKQT69tlXCXAAAAAAVg1BrsrcRYgD0BAaYUFZAAAaFUEOAHCZRllQFgCARkWxEwDAZRplQVlgKWHoOnLmkv70yFkdOXNJYchoGgDpQo8cAOAyyy0ou3vruoRaBVQHPc4AGgE9cgCAyzTKgrJAOfQ4A2gEBDkAwGUaZUFZoJzlepwBIC0IcgCAyzTSgrLAYvQ4A2gEBDkAQFmlBWU7znxTu7euI8ShYdDjDKAREOQAAEBToccZQCMgyAEAgKZDjzOAtGP5AQAAaiAMXYPDOZ0enVD/+k4N9PUQHgAA14wg14S4mQCA2mLdMgBAtRHkmgw3EwBQe/F1y6SF65axwDoA4FowR67JsAgqANQe65YBAKqNINdkuJkAgNpj3TIAQLUR5JoMNxMAUHusWwYAqDaCXJPhZgIAao91ywAA1UaQazLcTABAMli3DABqLwxdR85c0p8eOasjZy4pDD3pJlUNVSubUOlmQqND2r31w0k3BwAAAKi6Rq/WXpUeOTO708xOmNmQmT1QZn+7mX052v+MmfVX47wAAAAAUE6jV2uvOMiZWVbSpyXtl7RL0j1mtmvRYQckXXL3bZJ+V9JvV3peAAAAAFhKo1drr0aP3O2Shtz9lLvnJX1J0t2Ljrlb0uei51+RdIeZpb8/EwAAAEBdavRq7eZe2YQ/M/tJSXe6+/ui1/dK2uvu98eO+U50zNno9feiY15Z6nNv2LrT3/7gZypqW7UNfmtQkjTwxoEljwndNT5TWHL/yWPfkSRt3/X3r7kd9fIZKKqXn0eln1EPbajWZ6B66uVnyvWN1VAvP9NGub5RX+rlukjy+nZ3vfiDKU3OzEoyWcbU0ZrVlhs6tLhPqaM1q9Zs/dWBfOz9P/K8u+8pt6+ugpyZHZR0UJK6bvqhf/iOX3u0orYl4UpBrlHUw//Y9fQZqC+NclNUL5+B+sL1Xb02oLoa5bpI+jPcXd8dOiVl27Rp003qas9eFjpw9dxd4zOBZmYDtbdml/zzbNYg9yZJv+7u/yR6/auS5O6/FTvmqeiYvzWzFkkvS9rgy5x8z549/txzz1XUtiRMzwZ64cXGmEC5nF/86bskSZ/+48f5DNSdSn+m9XJd1ctnoL5wfVevDaiuRrkukvyMUpXFoy++ImVb1N7a0lBVFuvZ6zd0amP3mqSbcRkzWzLIVfO8RyYAABWDSURBVCN2Pitpu5ndYmZtkt4tafFV+7ik+6LnPynpa8uFOAAAAKDZlKosqqVNskzDVVlEdVUc5Ny9IOl+SU9JOi7pMXc/amYfN7O7osMekbTezIYk/bKky5YoAAAAAJpZo1dZRHVVZUFwd39C0hOLtn0s9nxa0v9RjXMBAAAAjahUZXEmFuYaqcoiqqv+ZvQBAAAATWigr0fbervU3pKRSWpvyWhbb5cG+nqSbhrqUFV65AAAAABUJpMxPbh/pwaHczo9OqH+9Z0a6Ouh0AnKIsgBAAAAdSKTMe3euk67t65LuimocwytBAAAQEXC0JVfv01TW9+sI2cuKQwpTg6sNnrkAAAAcM1Ka5+N73qnlG3RQ187ydpnQA3QIwcAAIBrxtpnQDIIcgAAALhmrH0GJIMgBwAAgGtWWvssjrXPgNVHkAMAAMA1Y+0zIBkUOwEAAMA1Y+0zIBkEOQAAAFSEtc+A2mNoJQAAAACkDEEOAAAAAFKGIAcAAAAAKUOQAwAAAICUIcgBAAAAQMoQ5AAAQKqEoSu/fpumtr5ZR85cUhh60k0CgJojyFWZmZRl3RQgMdzgAY0tDF2fePK4xne9U1O3vEUPfe2kPvHkcf5fB9B0CHJV1t6S1W1berR5XYdasgQ6oJa4wQMa3+BwTkMj41JLm2QZzRRCDY2Ma3A4l3TTAKCmCHKroDWbUd8Na3VbH4EOqCVu8IDGd3p0QvlCuGBbvhDq9OhEQi0CgGQQ5FZRSxTodm9Zp74bOtRKoANWFTd4CzHMFI2of32n2loW3r60tWTUv74zoRYBQDIIcjWQzZg2r1ur27as09b1a9XWQqADVgM3ePMYZopGNdDXo229XWpvycgktbdktK23SwN9PUk3DQBqqiXpBjSTbMa0qadDG7vXaOS1aZ3PTSlf4KYKqJbSDd7QyLjyhVBtTXyDt2CYqbRgmOnuresSbh1w7TIZ04P7d2pwOKfToxPqX9+pgb4eZSg0BqDJEOQSkM2Ybrq+QxuvW6OR12Z0Ljd12XAwACvHDd685YaZEuSQdpmMaffWdVzLAJoaQS5BmYzpddevUe917bo4Xgx0M7MEuloqzSEKujbqyJlLTXvT30i4wSsqDTOdiYW5Zh1mCgBAI2KOXB3IZEwbu9fotr4e/dCGTq1p5cdSC8whQiNjHhEAAI2NHrk6Ymbq7V6jDaUeuktTmqaHbtUwhwiNjGGmAAA0NoJcHTIz9V63Rhu62vXKeF7nclOaygdJN6vhMIcIjY5hpgAANC7G8NUxM9OG69r1xs3Xa/vGLq1tyybdpIZCqXoAacPagACAEoJcCpiZbuxq1xv7evSGjV3qbCfQVQNziACkCfN6gXTgFy6oFYZWpsz6rnat72rX9GygyXygyXxBU/lAE/lA07OBnL8rrhpziACkCfN6gfoX/4WLsi166Gsnta23Sw/u38n9BaqOIJdSa1qzWtOa1Q2dbXPbwtA1NRtoIgp3paDHouNLYw4RgLRgXi9Q//iFC2qJINdAMhlTZ3uLOtsX/lhng3Au1E3mg7mQF9DVDwCpwdqAQP3jFy6opYqCnJndIOnLkvolnZb0Lne/tOiYAUn/QVK3pEDSv3P3L1dyXqxMazaj6zsyur6j9Zo/wxeN2eyOPmtP/zoFoasQuoLAVQjD+dehazZY+Lr4GKoQECIBYCVK83qHRsaVL4RqY14vUHf4hQtqqdIeuQckHXL3T5rZA9HrDy86ZlLSe9z9pJltkvS8mT3l7rkKz40aMis/rrs1m1HrNdZe6e5oVeiuXTd1a7pQnOM3PRtGj4HoMASAeczrBeofv3BBLVUa5O6WtC96/jlJh7UoyLn7d2PPz5vZiKQNkghyUMZM169t1fW6vLdwphBoOh8S8gAgwrxeoL7xCxfUUqVBbqO7vxQ9f1nSxuUONrPbJbVJ+t4S+w9KOihJW7ZsqbBpSLv2lqzaW7JLh7zZUGvasnKXNlzXrnwhVD4IlS+EzP8DAACJ4BcuqJUrBjkz+2tJryuz6yPxF+7uZrbk3bOZ3STpUUn3uXtY7hh3f1jSw5K0Z88e7sSxpFLIa8sWl0Lc1tu1YH8QejHYFULNBMHc89kg2h4EVPMEAABAal0xyLn725baZ2YXzOwmd38pCmojSxzXLem/SfqIuz99za0FrlI2Y+poy6qjLSuV6dGTigVcZgqhZoOwOFTTJZfLXQrd5ZI82hb9V9wePffouaLj5t8z/17JFfr85/gyz2eDYtAEAAAArqTSoZWPS7pP0iejxz9bfICZtUn6/yR93t2/UuH5gKoxs7n1+OrFbBBqajbQdD7Q1Gz0lQ80UwhZ7B0A0NDC0JVfv01B10YdOXOJuWXAFVQa5D4p6TEzOyDpjKR3SZKZ7ZH0fnd/X7TtH0lab2bvjd73XncfrPDcQMMpVgHNqHvNwl7E0mLvpWA3HXvOdEAAQNqFoesTTx7X+K53StkWPfS1k9rW26UH9+8kzAFLqCjIufuopDvKbH9O0vui51+Q9IVKzgM0u6UWe5c0V8lzMh9obHpWY1MFir0AAFJlcDinoZFxqaVNkjRTCDU0Mq7B4RxFQ4AlVNojByBhpeGhPWulTepQGLpemy4oN5VXbnJWk/kg6SYCALCs06MTyhcW1sLLF0KdHp0gyAFLIMgBDSaTidbmW9uqreuLSzW8OjmrS5OzGpueVYGCKgCAOtO/vlNtLRnNxMJcW0tG/es7E2wVUN8IckCDa2/Jqrc7q97uNXJ3jU0X9OrkrHJTeU3M0FsHAEjeQF+PtvV2aWhkXPlCqLaWjLb1dmmgryfppgF1iyAHNBEz0/Udrbq+o1VbtFb5QqjcVD4KdvTWAQCSkcmYHty/U4PDOZ0enVD/+k6qVgJXQJADmlhbS0a9161R73XF3rrxmYJenSoWTBmfoWgKAKB2MhnT7q3rmBMHXCWCHABJxd6669a06ro1rdK6Yino8XxBY1Gwe216lqUOAAAA6gRBDkBZmYype01rcU27KNi9NhMFu+lZjU8XCHYAAAAJIchhxYLQNdnzeuU7N+rQ8Qvat6NXWcawN7xMZn5+naS5ZQ7Gpmf16tSsxmcKcoIdAABATRDksCJB6Lr3kWd0cftPyDMt+sAXX9BAX48ePbCXMNdk4ssc9Kl4bYxPFzSeL2hmNtBMIVS+EGqmEDLXDgAAoMoIcliRwydGNDick2fbJEmT+UCDwzkdPjGiO3ZuTLh1SFI2FuwWKwTFQDcf7oK5kDdTCDUbhPTmAU0kDF359dsUdG3UkTOXqE4IANeAIIcVOXp+TFP5hWuPTeUDHTs/RpDDklqyGbVkM+psL7/f3RcEvSB0uVzukkf7S0Fv7nHx/rl9sWM1/x6PXpX7nPhrSXO9y2taMwrdVQic+YBAlYSh6xNPHtf4rndK2RY99LWT2tbbpQf37yTMAcAKEOSwIrdu6lZHW1aTsTDX0ZbVrk3dCbYKaWdmWtOa1ZrWbNJNkSR1thf/arxty3wJbHdXIXQFpS93BUH0GC78KoSulmxGLldne1azgasQhIRBQNLgcE5DI+NSS3Fkx0wh1NDIuAaHc5SdR+LoLUaaEOSwIvt29Gqgr0eDwzlN5QN1tGU10NejfTt6k24asKrMTK1Z09VmzbVtxQP/weaeuW1B6JoNij2OhcA1G4bFxyCMQmIYhb7iPjOTM+YUDeb06ITyhXDBtnwh1OnRCYIcEkVvMdKGIIcVyWZMjx7Yq8MnRnTs/Jh2beqmaiVwlbIZUzZzdUkwCF3Z171B+c6Nem16Vj+6/UaZbK4HMIx6/sJYj2D8+fxxC4ePzg83jbYpPqy0/LDU+PHxfVp0DHA1+td3qq0lo5lYmGtryah/fWeCrQLoLUb6EOSwYtmM6Y6dG5kTB6ySxdVhf+nLg6mqDru4F9FdxZAZzXUsBc0wFjrDUGW2LQyks2Go2QJDVNNuoK9H23q7NDQyrnwhVFtLRtt6uzTQ13PlNwOriN5ipA1BDgBi6mGdxLRXhzWzRa+ljKxq/+CUhqjmg2Kwm42Gpxa/5vcVAmfpizqUyZge3L9Tg8M5nR6dUP/6TuYhoSoqnd9GbzHShiAHAJF6WSeR6rDLKw1RvZriOPHQV1gU+Aqx7YVofiLDRGsjkzHt3rqOXg5UTTXmt9FbjLQhyAFApF56wqgOWz0rCX2lyqTxoBfv7SuEpaUoSl/FIaNhOP+cIAgkoxrz2+gtRtoQ5AAgUi89YVSHTcZ8ZdLMNX9GGC4KeWUC39VUIr3SEXNrI/p8wZrFRW0Wr7/oioJo73YVOjdqaOQ17d66bn4OYxgvnHPNfwRAIqo1v43eYqQJQQ4AIvXSE0Z12PTKZEwZ1efPqTR0+NW/d7c806LfevLvlhw6HC80Ey8+U4gqpq5pzcolve76NQrC+d7K0jIaxZ7LZL5PNCfmt6EZEeQAIFJPPWFUh0W1rWTocCmQLjUita2l2Gt5y41L3ySXgl8hCnpBEHsdLAyIYaxX0D2qYMqwVawA89vQjAhyABChJwyNrNZDhzMZU1vG1KZrH6oaVxq2Gl/GInRfMAx1LuzNrYfol+1bePzS6yMu+Ly5Y8ocFQ1lLYXN+ON8CC1tmx9yW+4YejKvHfPb0IwIcgAQQ08YGlW9DB2+VqVewka/cXG/fH3FhfMXFz4vDXctHd/MxXiY34Zm0+h/HwIAANXX0GEszczUkl2dwLq4V7MUCktFeOLFeOY6Nxf3bsYeFu9bLihebYaMF+kJfWHbpNLrhT2fpWPjvZzuFO5B4yPIAQDQBBg6jGbp1Yxb3MNZCrJhWGZ7rMczPgx2bu7mXFXa4v7SdiApzfT/MgAATY2hw2g2q9nDKc33FpYLeYqW/YgvCSItWjJk0RIi8bmcV7NUyZXbV/714l7U+R7Ycj2y83M8XcXgO/d9lXpHNd8r6qXXoS/ouUX1EeQAAACAa2Bmypro2V5Gabjs4qJAWvTaJXksJMYDoi4LnLHPX0lRo3L1iqJta9uWKNNbxwhyAFBlQeia7Hm98p0bdej4BYavoaFwfQNYCTOTRX9FZOt0nc20qk5NYCABpZuJ3M1v0qHjF4rDGICElRZdvrj9J5Tb/CP6wBdf0L2PPMP1iYbA9Q0A9YMgh1TiZgL1asGiy5ZZsOgykHZc3wBQPwhySCVuJlCvllt0GUg7rm8AqB8EOaQSNxOoV6VFl+PStOgysByubwCoHxUFOTO7wcz+ysxORo/rljm228zOmtkfVnJOQOJmAvWrtOjy2rbilO61LLqMBsL1DQD1o9KqlQ9IOuTunzSzB6LXH17i2N+U9I0KzwdImr+ZGBzOaSofqIObCdQJFl1GI+P6BoD6UWmQu1vSvuj55yQdVpkgZ2b/UNJGSX8haU+F5wS4mUBdY9FlNDKubwCoD5UGuY3u/lL0/GUVw9oCZpaR9ClJPyvpbRWeD5jDzQQAAACa1RWDnJn9taTXldn1kfgLd3czK1f7/Z9LesLdz5ot31tiZgclHZSkLVu2XKlpAAAAANCUrhjk3H3JXjQzu2BmN7n7S2Z2k6Rytd/fJOktZvbPJXVJajOzcXd/oMy5Hpb0sCTt2bOHBcEAAAAAoIxKlx94XNJ90fP7JP3Z4gPc/WfcfYu790v6FUmfLxfi0FyC0DXZ83rlbn6TDh2/wELeAAAAwApUGuQ+KentZnZSxflvn5QkM9tjZn9UaePQmILQde8jz+ji9p9QbvOP6ANffEH3PvIMYQ4AAAC4ShUVO3H3UUl3lNn+nKT3ldn+WUmfreScSL/DJ0Y0OJyTZ9skSZP5QIPDOR0+MULhEgAAAOAqVNojB6zY0fNjmsoHC7ZN5QMdOz+WUIsAAACAdCHIoeZu3dStjrbsgm0dbVnt2tSdUIsAAACAdCHIoeb27ejVQF+P1rZlZZLWtmU10NejfTt6k24aAAAAkAqVLggOrFg2Y3r0wF4dPjGiY+fHtGtTt/bt6FU2s/w6gwAAAACKCHJIRDZjumPnRoqbAAAAANeAoZUAAAAAkDIEOQAAAABIGYIcAAAAAKQMQQ4AAAAAUoYgBwAAAAApQ5ADAAAAgJQhyAEAAABAyhDkAAAAACBlCHIAAAAAkDIEOQAAAABIGXP3pNtQlpldlHQm6XaUcaOkV5JuBLAErk/UK65N1CuuTdQrrk1I0lZ331BuR90GuXplZs+5+56k2wGUw/WJesW1iXrFtYl6xbWJK2FoJQAAAACkDEEOAAAAAFKGILdyDyfdAGAZXJ+oV1ybqFdcm6hXXJtYFnPkAAAAACBl6JEDAAAAgJQhyK2Amd1pZifMbMjMHki6PWhuZvYZMxsxs+/Ett1gZn9lZiejx3VJthHNx8z6zOzrZnbMzI6a2Qej7VybSJyZrTGz/25m34quz9+Itt9iZs9E/75/2czakm4rmpOZZc3sBTP7r9Frrk0siSB3lcwsK+nTkvZL2iXpHjPblWyr0OQ+K+nORdsekHTI3bdLOhS9BmqpIOlD7r5L0g9L+sXo70quTdSDGUlvdfc3ShqQdKeZ/bCk35b0u+6+TdIlSQcSbCOa2wclHY+95trEkghyV+92SUPufsrd85K+JOnuhNuEJubu35D0g0Wb75b0uej55yS9s6aNQtNz95fc/Uj0/DUVb0huFtcm6oAXjUcvW6Mvl/RWSV+JtnN9IhFmtlnS/ybpj6LXJq5NLIMgd/VuljQce3022gbUk43u/lL0/GVJG5NsDJqbmfVLuk3SM+LaRJ2Ihq4NShqR9FeSvicp5+6F6BD+fUdSfk/Sv5YURq/Xi2sTyyDIAQ3KiyVpKUuLRJhZl6T/IumX3H0svo9rE0ly98DdByRtVnG0zd9LuEmAzOzHJY24+/NJtwXp0ZJ0A1LknKS+2OvN0Tagnlwws5vc/SUzu0nF3zgDNWVmrSqGuP/s7n8abebaRF1x95yZfV3SmyT1mFlL1PPBv+9Iwpsl3WVm75C0RlK3pN8X1yaWQY/c1XtW0vaoelCbpHdLejzhNgGLPS7pvuj5fZL+LMG2oAlFczoekXTc3X8ntotrE4kzsw1m1hM975D0dhXncX5d0k9Gh3F9oubc/VfdfbO796t4j/k1d/8ZcW1iGSwIvgLRb0l+T1JW0mfc/d8l3CQ0MTP7oqR9km6UdEHSr0n6qqTHJG2RdEbSu9x9cUEUYNWY2Y9K+htJ/0Pz8zweVHGeHNcmEmVm/0DFghFZFX+Z/Zi7f9zMXq9iEbMbJL0g6WfdfSa5lqKZmdk+Sb/i7j/OtYnlEOQAAAAAIGUYWgkAAAAAKUOQAwAAAICUIcgBAAAAQMoQ5AAAAAAgZQhyAAAAAJAyBDkAAAAASBmCHAAAAACkDEEOAAAAAFLmfwLWol/IrvqsawAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 1080x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2ccuivXy9FXd"
      },
      "source": [
        "Muitos Lags fora da zona de confiança (Alta AutoCorrelação tanto positiva como negativa)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ko5_V-ugzZO_"
      },
      "source": [
        "###PACF (Aleatória)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 390
        },
        "id": "F-Mn3z2BzZTk",
        "outputId": "c50c513c-5d15-4e28-c505-9b88fb16f4b2"
      },
      "source": [
        "plot_pacf(serie3, lags=30)\n",
        "plt.show()"
      ],
      "execution_count": 48,
      "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": "Zh6r34hj9N72"
      },
      "source": [
        "Muitos Lags fora da zona de confiança (Alta AutoCorrelação positiva)"
      ]
    }
  ]
}