AnaliseEPrevisaoDeSeriesTemporais_ptBr.ipynb

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  "metadata": {
    "colab": {
      "name": "AnaliseEPrevisaoDeSeriesTemporais-ptBr.ipynb",
      "provenance": [],
      "collapsed_sections": [
        "O39qpCI3C8AM",
        "aJlYNdZuskJa",
        "fWBWc-S0DBWU"
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      "machine_shape": "hm",
      "authorship_tag": "ABX9TyMhqUVRGbFmI6trmU4sb8SY",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/masdesouza/study_artificial_intelligence/blob/main/AnaliseEPrevisaoDeSeriesTemporais_ptBr.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fVBD_IVHcB3z"
      },
      "source": [
        "Introduction Time Series and Forecasting\n",
        "\n",
        "Time Series Analysis (Biblia)\n",
        "\n",
        "Gridesearch\n",
        "\n",
        "https://qastack.com.br/stats/383246/modeling-time-series-sensor-data-with-machine-learning-techniques\n",
        "\n",
        "http://repositorio.unicamp.br/jspui/handle/REPOSIP/276076\n",
        "\n",
        "https://crislanio-ufc.medium.com/um-tratamento-para-outliers-e-dados-ausentes-em-s%C3%A9ries-temporais-multivariadas-em-redes-de-7e2a83878e69\n",
        "\n",
        "https://blog.dominodatalab.com/towards-predictive-accuracy-tuning-hyperparameters-and-pipelines/\n",
        "\n",
        "https://paulovasconcellos.com.br/como-saber-se-seu-modelo-de-machine-learning-est%C3%A1-funcionando-mesmo-a5892f6468b"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4rlbCOGgCn88"
      },
      "source": [
        "#Aula do dia 03/05/2021"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "O39qpCI3C8AM"
      },
      "source": [
        "##Análise de Séries Temporais"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aJlYNdZuskJa"
      },
      "source": [
        "\n",
        "Objetivo: \n",
        "*   Comprender as características de um fenômeno temporal, investigando o mecanismo gerador da série temporal;\n",
        "*   Selecionar e estimar um modelo estocástico que possa ser utilizado para fazer previsão de valores futuros;\n",
        "> As previsões podem se feitas em curto prazo, ou a longo prazo: \n",
        "\n",
        "        Curto prazo: previsão de vendas, estoque, preço de ações...\n",
        "        Longo prazo: previsão de produtividade, população...\n",
        "\n",
        "\n",
        "*   Descrever apenas o comportamento da série verificando a existência de tendência, sazonalidade, ciclos, etc;\n",
        "*   Procurar periocidade relevantes nos dados.\n",
        "\n",
        "Aplicações:\n",
        "*   Entendimento de um comportamento temporal;\n",
        "*   Classificar um comportamento temporal;\n",
        "*   Detecção de anomalias;\n",
        "*   Previsão.\n",
        "\n",
        "\n",
        "---\n",
        "Campos de Aplicação:\n",
        "*   Mercado Financeiro;\n",
        "*   Meteorologia;\n",
        "*   Medicina;\n",
        "*   Contole de Qualidade e Processos;\n",
        "*   Governo;\n",
        "*   Epidemiologia;\n",
        "*   Transporte.\n",
        "\n",
        "\n",
        "\n",
        "---\n",
        "\n",
        "No geral, Análise de Séries Temporais consiste em:\n",
        "\n",
        "*   Analisar um conjunto de dados (ao longo do tempo);\n",
        "*   Selecionar e estimar um modelo matemático que \"possivelmente\" tenha gerado o conjunto de dados; \n",
        "*   A análise pode ser feita no domínio temporal, ou no domínio das frequências;\n",
        "*   Resumindo, deseja-se realiar previsões futuras baseando-se no comportamento passado dos dados.\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "---\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "https://www.ime.usp.br/~pam/ST.html\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1wpbfkXnCVgc"
      },
      "source": [
        "##Modelos de Séries Temporais\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fWBWc-S0DBWU"
      },
      "source": [
        "Dois pensamentos importantes:\n",
        "1.   A melhor maneira de prever o futuro é criá-lo (Peter Drucker);\n",
        "2.   Todos os modelos estão errados mas alguns são úteis.\n",
        "\n",
        "\n",
        "\n",
        "---\n",
        "Modelos Simples de Séries Temporais:\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uBrePFpDsu2G"
      },
      "source": [
        "\n",
        "Hierarchical time series forecasting is the process of generating coherent forecasts (or reconciling incoherent forecasts), allowing individual time series to be forecast individually, but preserving the relationships within the hierarchy."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vbqJQeMxx6gj"
      },
      "source": [
        "*   REMOVER TENDENCIAS E COMPONENTES SAZONAIS PARA OBTER RESIDUOS ESTACIONÁRIOS\n",
        "\n",
        "*   Escolher um modelo para ajustar aos residuos. Utilizando várias estatísticas amstrais como função de auto-correlação.\n",
        "\n",
        "*   Alcançar \n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vZDptiLyzr3n"
      },
      "source": [
        "#Processo Estacionário\n",
        "É um processo que se mantém em equilibrio estatico com propriedade que não se alteram no tempo.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "O-JpqHpy0r-Q"
      },
      "source": [
        "#Correlograma"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "T6E1AIBW02xU"
      },
      "source": [
        "#Modelos Estacionários: Função de corelação"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "t1UpxnXt3mfm"
      },
      "source": [
        "# Remoção de Outlier"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "m2KMpTSzs30g"
      },
      "source": [
        "#Importação das bibliotecas Python"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FeIEkLCQsuT9",
        "outputId": "d17a1a91-fde5-4e05-fd02-0ce0e078cd23"
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "\n",
        "from google.colab import drive\n",
        "drive.mount('/content/drive')\n",
        "\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YhwU70171tc0"
      },
      "source": [
        "#Exercicio Aula 03/05/2021"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 269
        },
        "id": "pB4_4kCvbiO2",
        "outputId": "4fc5569b-5584-4b5c-8482-14200b24869d"
      },
      "source": [
        "''' Slide 38\n",
        "\n",
        "1) Considere a série M-ICV:\n",
        "\n",
        "    a) Faça o gráfico da série; ela é estacionária?\n",
        "    b) Obtenha a primeira diferença da série e faça o grafico corresdente; a diferença é estacionária?\n",
        "    c) Mesmas questões de (b) para a segunda diferença.\n",
        "\n",
        "'''\n",
        "#a) Faça o gráfico da série; ela é estacionária?\n",
        "#Resposta: O gráfico mostra que a série não é estacionáaria, pois tem uma tendência de crescimento exponencial \n",
        "icv = \"/content/drive/MyDrive/BASE DE DADOS IA/AnaliseSeriesTemporais/ICV.xls\"\n",
        "dict_icv = pd.read_excel(icv, sheet_name='Plan1')\n",
        "\n",
        "df_icv = pd.DataFrame(dict_icv)\n",
        "\n",
        "df_icv.plot(kind='line',x='Mes/ano',y='ICV',color='blue')\n",
        "plt.show()\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CpoLAKUW2x7g"
      },
      "source": [
        "#b) Obtenha a primeira diferença da série e faça o grafico corresdente; a diferença é estacionária?\n",
        "\n",
        "\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OUXsEpj921R-"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}