{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Criando Previsões de Preços para 5 dias Adiante\n",
    "\n",
    "Dentro de uma empresa existem diversos fatores de risco para serem administrados, alguns destes são a variação de preços de produtos que podem impactar no fluxo de caixa, previsões de demanda de produtos, onde deve-se avaliar os melhores momentos de venda como também fazer a gestão de estoques, outro fator importante é de fazer previsões sobre gastos recorrentes, pois valores expressivos podem impactar capital de giro da empresa.\n",
    "\n",
    "Usualmente, as estimativas de preços, demanda, gastos, etc. são criadas levando em consideração conhecimentos técnicos dos gestores, como também com informações essenciais sobre o assunto específico que influenciam a tomada de decisão. \n",
    "Quando modelos de previsão são usados, os dados são separados entre treino e teste, os coeficientes de multiplicação são gerados nos dados de treino e normalmente são testados passo a passo na base de testes, ou seja, somente um período é previsto, atualiza-se com dados reais e se prevê mais um dia e assim por diante até finalizar os dados de teste.\n",
    "\n",
    "Neste notebook será criado um modelo [ARIMA](https://pt.wikipedia.org/wiki/ARIMA) e o teste dos coeficientes obtidos no treino será feito de uma maneira um pouco diferente, 5 dias consecutivos serão previstos, posteriormente atualizados com dados reais e novamente serão criadas novas previsões para 5 períodos adiante até os dados de teste acabarem. \n",
    "\n",
    "Este tipo de abordagem é interessante, pois para quem deseja criar modelos de previsão com vários períodos adiante pode-se comparar mais facilmente as extrapolações de previsões com os dados reais, assim, verifica-se mais facilmente quais são as principais características do modelo e quais são os pontos que podem ser melhorados.\n",
    "\n",
    "O objetivo do modelo é prever o preço diário dos próximos 5 dias do Açúcar Cristal base São Paulo informado pela Esalq, os dados podem ser baixados **[aqui](https://www.cepea.esalq.usp.br/br/indicador/acucar.aspx).**\n",
    "\n",
    "A partir de agora iremos usar os seguintes passos, extraidos deste meu post **[Princípios Básicos para Criar Previsões de Séries Temporais](https://medium.com/@leandrorabelo_65568/princ%C3%ADpios-b%C3%A1sicos-para-criar-previs%C3%B5es-de-s%C3%A9ries-temporais-e58c451a25b)**, caso você não tenha muita noção sobre as bases para se criar essas previsões, recomendo a leitura:\n",
    "\n",
    " * Dividir série em treino e teste\n",
    " * Transformar a série em estacionária\n",
    " * Procurar lags correlacionados\n",
    " * Criar o modelo\n",
    " * Plotar o gráfico comparativo na base de treino\n",
    " * Avaliar o modelo e o erro na base de testes\n",
    " * Sugerir possíveis melhorias do modelo.\n",
    "\n",
    "Antes de tudo vamos importar as bibliotecas essenciais para se trabalhar com séries temporais:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "#importando bibliotecas essenciais\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import statsmodels.api as sm\n",
    "from statsmodels.graphics.tsaplots import plot_acf, plot_pacf\n",
    "from statsmodels.tsa.stattools import adfuller\n",
    "from statsmodels.tsa.seasonal import seasonal_decompose\n",
    "from statsmodels.tsa.arima_model import ARIMA\n",
    "from matplotlib.animation import FuncAnimation\n",
    "from math import sqrt\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib notebook\n",
    "sns.set_context(\"paper\")\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "scrolled": true
   },
   "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>Cristal</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Data</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2003-05-20</th>\n",
       "      <td>29.65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2003-05-21</th>\n",
       "      <td>29.26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2003-05-22</th>\n",
       "      <td>28.81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2003-05-23</th>\n",
       "      <td>28.35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2003-05-26</th>\n",
       "      <td>27.83</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Cristal\n",
       "Data               \n",
       "2003-05-20    29.65\n",
       "2003-05-21    29.26\n",
       "2003-05-22    28.81\n",
       "2003-05-23    28.35\n",
       "2003-05-26    27.83"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_excel('EsalqCristal.xls')\n",
    "df['Data'] = pd.to_datetime(df['Data'], dayfirst=True)\n",
    "df.sort_values('Data', inplace=True)\n",
    "df.set_index('Data', inplace=True)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Criando base de treino e teste\n",
    "\n",
    "Temos um histórico de preços de aproximadamente 16 anos, vamos reservar os últimos **250 dias úteis** ( aprox. 1 ano) para testar o modelo, serão 50 comparativos de 5 dias cada, entre os valores previstos e reais. Todos os dados anteriores serão usados para treinar o modelo:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dias = 250\n",
    "treino = df.iloc[:-dias,0:1].copy()\n",
    "teste = df.iloc[-dias:,0:1].copy()\n",
    "plt.figure(figsize=(18,5))\n",
    "plt.title('Preço do Açúcar Cristal - Base Esalq')\n",
    "plt.plot(treino['Cristal'], color='teal')\n",
    "plt.plot(teste['Cristal'], color='orangered')\n",
    "plt.legend(['Treino','Teste'])\n",
    "plt.xlabel('Data')\n",
    "plt.ylabel('Preço')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Teste de estacionariedade e verificando Correlações\n",
    "\n",
    "Para se criar uma previsão de série temporal é necessário que ela seja estacionária, ou seja, que ela tenha as seguintes condições relativamente constantes: média, variância e autocorrelação.\n",
    "\n",
    "Abaixo irei usar a função abaixo para plotar a série, sua distribuição, [autocorrelação](https://pt.wikipedia.org/wiki/Autocorrela%C3%A7%C3%A3o) e autocorrelação parcial além de checar a estacionariedade usando o teste de [Dickey Fuller](https://en.wikipedia.org/wiki/Dickey%E2%80%93Fuller_test):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "#criando uma função para plotar o gráfico e mostrar o resultado do teste:\n",
    "def checar_estacionariedade(y, lags_plots=48, figsize=(22,8)):\n",
    "    \"Use Series como parâmetro\"\n",
    "    \n",
    "    # Criando plots do DF\n",
    "    y = pd.Series(y)\n",
    "    fig = plt.figure()\n",
    "\n",
    "    ax1 = plt.subplot2grid((3, 3), (0, 0), colspan=2)\n",
    "    ax2 = plt.subplot2grid((3, 3), (1, 0))\n",
    "    ax3 = plt.subplot2grid((3, 3), (1, 1))\n",
    "    ax4 = plt.subplot2grid((3, 3), (2, 0), colspan=2)\n",
    "\n",
    "    y.plot(ax=ax1, figsize=figsize, color='teal')\n",
    "    ax1.set_title('Série de Preços')\n",
    "    plot_acf(y, lags=lags_plots, zero=False, ax=ax2, color='teal');\n",
    "    plot_pacf(y, lags=lags_plots, zero=False, ax=ax3, method='ols', color='teal');\n",
    "    sns.distplot(y, bins=int(sqrt(len(y))), ax=ax4, color='teal')\n",
    "    ax4.set_title('Distribuição dos Preços')\n",
    "\n",
    "    plt.tight_layout()\n",
    "    \n",
    "    print('Resultados do teste de Dickey-Fuller:')\n",
    "    adfinput = adfuller(y)\n",
    "    adftest = pd.Series(adfinput[0:4], index=['Teste Statistico','Valor-P','Lags Usados','Números de Observações'])\n",
    "    adftest = round(adftest,4)\n",
    "    \n",
    "    for key, value in adfinput[4].items():\n",
    "        adftest[\"Valores Críticos (%s)\"%key] = value.round(4)\n",
    "        \n",
    "    print(adftest)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Para verificar se a série de treino é estacionária, iremos usar o valor P de 5% como base para estacionariedade, caso o valor P do teste seja inferior a 5% podemos considerar que a série é estacionária:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Resultados do teste de Dickey-Fuller:\n",
      "Teste Statistico            -2.6288\n",
      "Valor-P                      0.0872\n",
      "Lags Usados                  7.0000\n",
      "Números de Observações    3714.0000\n",
      "Valores Críticos (1%)       -3.4321\n",
      "Valores Críticos (5%)       -2.8623\n",
      "Valores Críticos (10%)      -2.5672\n",
      "dtype: float64\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1584x576 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "checar_estacionariedade(treino['Cristal'], lags_plots=60)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Analisando o gráfico da série de preços, nota-se que ele tem uma leve tendência de alta, observando os dados estatísticos a série ficou com o valor P de 8,7%, a distribuição dos dados não segue uma normalidade gaussiana, e as características da **autocorrelação** mostram correlações significativas em todos os lags, isso é sinal de uma série com tendência e que pode-se fazer ao menos uma diferenciação.\n",
    "\n",
    "Assim, essa série não satisfaz os critérios estabelecidos para ser considerada estacionária.\n",
    "\n",
    "Vou fazer a primeira diferenciação para retirar a tendência e ficar somente as movimentações dia-a-dia:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Resultados do teste de Dickey-Fuller:\n",
      "Teste Statistico           -11.3823\n",
      "Valor-P                      0.0000\n",
      "Lags Usados                  5.0000\n",
      "Números de Observações    3715.0000\n",
      "Valores Críticos (1%)       -3.4321\n",
      "Valores Críticos (5%)       -2.8623\n",
      "Valores Críticos (10%)      -2.5672\n",
      "dtype: float64\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1584x576 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "checar_estacionariedade(treino['Cristal'].diff().dropna(), lags_plots=60)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "O teste de Dickey Fuller retornou o **valor P** de zero, isso significa que a série tornou-se estacionária, para complementar, nota-se no gráfico que a tendência desapareceu e os preços estão relativamente estáveis.\n",
    "\n",
    "No gráfico de auto correlação os valores vão decrescendo gradualmente, diferente do gráfico sem diferenciação.\n",
    "\n",
    "A auto correlação parcial cai mais abruptamente, demonstrando que os valores que realmente impactam sobre os valores atuais são principalmente os 3 primeiros lags, aparentemente não há correlação sazonal significativa para ser considerada nas previsões.\n",
    "\n",
    "Dessa maneira, podemos considerar os 3 primeiros lags para um [modelo ARIMA](https://pt.wikipedia.org/wiki/ARIMA) com uma diferenciação, assim sendo usaremos a seguinte terminologia para o modelo: **ARIMA(3,1,0)**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Criando o Modelo\n",
    "\n",
    "Inicialmente o modelo será criado com base nos dados de treino, assim, os coeficientes para os 3 lags auto correlacionados serão gerados e servirão para testar a extrapolação de 5 dias consecutivos na base de testes.\n",
    "\n",
    "Existem diversas maneiras de se fazer extrapolações para o futuro, as principais são:\n",
    "\n",
    " * Criar um modelo para cada dia a ser previsto e agregar tudo posteriormente,\n",
    " * Recursivo, prevendo o primeiro dia e usar essa previsão como base para prever o próximo dia.\n",
    " \n",
    "Usaremos o último modelo neste exemplo, neste caso iremos prever os próximos 5 dias, comparar com dados reais, adicionar estes últimos novamente na base para fazer uma nova extrapolação, posteriormente iremos calcular o erro do modelo e analisar fatos relevantes da previsão."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Treinando o modelo\n",
    "modelo = ARIMA(treino, order=(3,1,0)).fit()\n",
    "pred_treino = modelo.predict()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Vamos agora comparar os dados reais com os previstos, como a previsão foi feita sobre o modelo com diferenciação (como pode ser visto abaixo) o primeiro valor real será adicionado e somado cumulativamente para ter de volta a base de preço em R$/m3."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Data\n",
       "2003-05-21    0.006286\n",
       "2003-05-22   -0.164735\n",
       "2003-05-23   -0.250335\n",
       "2003-05-26   -0.298730\n",
       "2003-05-27   -0.328869\n",
       "dtype: float64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Base de previsão com diferenciação\n",
    "pred_treino[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Data\n",
       "2003-05-21    29.656286\n",
       "2003-05-22    29.491551\n",
       "2003-05-23    29.241216\n",
       "2003-05-26    28.942486\n",
       "2003-05-27    28.613616\n",
       "dtype: float64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Voltando para a base de preços em R$/m3\n",
    "pred_treino[0] += treino.iloc[0,0]\n",
    "pred_treino = np.cumsum(pred_treino)\n",
    "pred_treino.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Agora os valores previstos serão adicionados na variável de treino para plotarmos o gráfico com as duas séries:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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>Cristal</th>\n",
       "      <th>Pred Cristal</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Data</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2003-05-21</th>\n",
       "      <td>29.26</td>\n",
       "      <td>29.656286</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2003-05-22</th>\n",
       "      <td>28.81</td>\n",
       "      <td>29.491551</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2003-05-23</th>\n",
       "      <td>28.35</td>\n",
       "      <td>29.241216</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2003-05-26</th>\n",
       "      <td>27.83</td>\n",
       "      <td>28.942486</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2003-05-27</th>\n",
       "      <td>27.76</td>\n",
       "      <td>28.613616</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Cristal  Pred Cristal\n",
       "Data                             \n",
       "2003-05-21    29.26     29.656286\n",
       "2003-05-22    28.81     29.491551\n",
       "2003-05-23    28.35     29.241216\n",
       "2003-05-26    27.83     28.942486\n",
       "2003-05-27    27.76     28.613616"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "treino['Pred Cristal'] = pred_treino\n",
    "treino.dropna(inplace=True)\n",
    "treino.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotando o gráfico\n",
    "treino.plot(figsize=(18,6), \n",
    "            title='Açúcar Cristal - Real vs Previsto na base de Treino',\n",
    "           color=['Teal','orangered'])\n",
    "plt.ylabel('Preço')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Na base de treino o modelo consegue capturar os principais momentos do mercado. Somente quando há maiores variações que a previsão não consegue capturar o movimento, agora vamos analisar o erro do modelo:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "O RMSE foi de R$ 4.9671/sc\n"
     ]
    }
   ],
   "source": [
    "#Medindo o erro do treino pela métrica Root Mean Squared Error\n",
    "from sklearn.metrics import mean_squared_error\n",
    "erro = sqrt(mean_squared_error(treino['Cristal'], treino['Pred Cristal']))\n",
    "print(f'O RMSE foi de R$ {round(erro,4)}/sc')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Obtendo os Coeficientes\n",
    "\n",
    "Após treinar o modelo, podemos obter os coeficientes de multiplicação dos lags junto com o valor da constante, esses valores servirão como base para extrapolarmos as previsões para os próximos 5 dias."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Valor da constante 0.0063\n",
      "Coeficientes do Lag 1: 0.2332, Lag 2: 0.2297 e Lag 3: 0.2309\n"
     ]
    }
   ],
   "source": [
    "# Pegando os coeficientes de múltiplicação dos lags para usar na base de testes:\n",
    "const, l1, l2, l3 = modelo.params\n",
    "\n",
    "print(f'Valor da constante {round(const,4)}')\n",
    "print(f'Coeficientes do Lag 1: {round(l1,4)}, Lag 2: {round(l2,4)} e Lag 3: {round(l3,4)}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "O próximo passo é criar uma função para fazer as previsões, usarei todas as bases com diferenciação, criarei a previsão para 5 períodos adiante e atualizarei os dados reais com mais 5 dias e assim sucessivamente até finalizarem os dados de teste."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "def prever_n_per(treino, teste, passos):\n",
    "    \n",
    "    # Adicionando a série com diferenciação para projetar previsões\n",
    "    diff_treino = treino.diff().dropna().values\n",
    "    hist = [x for x in diff_treino]\n",
    "\n",
    "    diff_teste = teste.diff().dropna().values\n",
    "    diff_teste = [x for x in diff_teste]\n",
    "\n",
    "    # Adicionando primeira diferenciação no teste que ficou entre o treino e teste\n",
    "    prim_diff = teste[0] - treino[-1]\n",
    "    diff_teste.insert(0, prim_diff)\n",
    "    \n",
    "    prev = []\n",
    "\n",
    "    for i in range(0,len(teste),passos):\n",
    "        for j in range(passos):\n",
    "    \n",
    "            yhat = const + (l1 * hist[-1]) + (l2 * hist[-2]) + (l3 * hist[-3])\n",
    "            prev.append(yhat)\n",
    "            # usando a previsão atual como base para novas previsões\n",
    "            hist.append(yhat)\n",
    "        obs = diff_teste[i:i+passos]\n",
    "        hist = list(np.append(hist,obs))\n",
    "    return prev"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Previsões antes de reverter os preços: \n",
      "[0.17477782558127006, 0.053966184856814814, 0.059013042826703804, 0.07279059649919215, 0.04927191191727104]\n"
     ]
    }
   ],
   "source": [
    "# Testando a função\n",
    "steps = 5\n",
    "prev = prever_n_per(treino['Cristal'], teste['Cristal'], passos=steps)\n",
    "print('Previsões antes de reverter os preços: ')\n",
    "print(prev[:5])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Do mesmo jeito que criamos a função para prever os preços com 5 dias de extrapolação, iremos criar a função abaixo para transformar os preços previstos para a base comparável com os preços reais da base de teste."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "def inverter_diff(treino, teste, previsão, passos):\n",
    "    '''\n",
    "    Usando o último dia da base de treino, após isso, usa o último valor previsto como base sucessivamente.\n",
    "    \n",
    "    A cada passo, volta a usar o primeiro valor da base de teste que se tornou real.\n",
    "    '''\n",
    "    treino = [x for x in treino]\n",
    "    teste = [x for x in teste]\n",
    "    prev_inv = []\n",
    "    \n",
    "    for i in range(0,len(teste), passos): \n",
    "        inverter = previsão[i] + treino[-1]\n",
    "        prev_inv.append(inverter)\n",
    "        \n",
    "        for j in range(passos-1):\n",
    "            inverter += previsão[i+j+1]\n",
    "            prev_inv.append(inverter)\n",
    "        treino = np.append(treino, teste[i:i+passos])\n",
    "    return prev_inv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Adicionando a previsão na base de testes para comparar posteriormente\n",
    "teste['Previsão 5D'] = inverter_diff(treino['Cristal'],\n",
    "                                    teste['Cristal'],\n",
    "                                    prev, passos=steps)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Plotando o Comparativo\n",
    "\n",
    "Para finalizar, a função abaixo irá separar cada previsão pelos períodos de 5 dias e comparar com a base de testes:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plotar_comparativo(orig, prev, passos, titulo='', figsize=(18,8)):\n",
    "    plt.figure(figsize=figsize)\n",
    "    \n",
    "    orig = np.array(orig)\n",
    "    prev = np.array(prev)\n",
    "    plt.plot(orig, label='Real', color='teal')\n",
    "    \n",
    "    for i in range(0,len(orig), passos):\n",
    "        eixo_x = [x for x in range(i,i+passos)]\n",
    "        plt.plot(eixo_x, prev[i:i+passos], color='orangered')\n",
    "    \n",
    "    plt.title(titulo)\n",
    "    plt.ylabel('Preços')\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plotar_comparativo(teste['Cristal'], teste['Previsão 5D'], passos=steps, \n",
    "                   titulo='Comparativo Cristal - Valor Real x Previsto (Extrapolando 5 dias)')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "O RMSE da base de testes foi de R$ 1.1522/sc\n"
     ]
    }
   ],
   "source": [
    "erro_teste = sqrt(mean_squared_error(teste['Cristal'], teste['Previsão 5D']))\n",
    "print(f'O RMSE da base de testes foi de R$ {round(erro_teste,4)}/sc')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Avaliando o gráfico acima, nota-se que a principal característica desse tipo de previsão é que ele segue a tendência mais recente que está sendo negociada, porém ainda há espaço para melhoras, o modelo não é capaz de prever possíveis mudanças de tendências.\n",
    "\n",
    "No próximo vai ser analisar o erro (previsto - real), pois neste tipo de análise pode-se obter informações para melhorar o modelo."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Analisando o Erro\n",
    "\n",
    "Para analisar o erro criarei uma função semelhante às anteriores, irão ser analisados os valores médios de cada um dos 5 passos, normalmente a tendência é que o erro, neste caso o RMSE, aumente de acordo com os períodos que vão se expandindo devido às incertezas, veremos abaixo:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "def erro_n_passos(orig, prev, passos, plot=True):\n",
    "    # Calculando o RMSE de cada linha\n",
    "    erro = ((np.array(orig) - np.array(prev))**2)**(1/2)\n",
    "    \n",
    "    # Mudando o formato para ficar: (erros/exemplos, passos)\n",
    "    erro = erro.reshape(int(len(erro)/passos), passos)\n",
    "    \n",
    "    # Fazendo a média do erro para cada passo\n",
    "    media = erro.mean(axis=0)\n",
    "    \n",
    "    # Plotando o gráfico de erro\n",
    "    if plot:\n",
    "        indice = np.arange(0,passos)\n",
    "        plt.figure(figsize=(12,4))\n",
    "        plt.plot(media, 'o-', color='teal', markersize=8)\n",
    "        plt.title('Erro Médio para Cada Passo', fontsize=15)\n",
    "        plt.xlabel('Passos', fontsize=15)\n",
    "        plt.ylabel('Erro', fontsize=15)\n",
    "        for x,y in zip(indice, media):\n",
    "            label = \"{:.4f}\".format(y)\n",
    "            plt.annotate(label, (x, y), xytext=(x-0.1, y+.075), fontsize=12)\n",
    "        plt.xticks(np.arange(passos), [x for x in range(1,passos+1)])\n",
    "        plt.yticks([x for x in np.linspace(min(media)-0.2, max(media)+0.2, 10)])\n",
    "        plt.grid(axis='y')\n",
    "        plt.show()\n",
    "    print(f'O RMSE para cada passo ficou assim: \\n{media}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "O RMSE para cada passo ficou assim: \n",
      "[0.48524694 0.69167403 0.81373203 1.00020175 1.12683736]\n"
     ]
    }
   ],
   "source": [
    "erro_passo = erro_n_passos(teste['Cristal'], teste['Previsão 5D'], steps)\n",
    "erro_passo"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Como previsto, a cada passo, a tendência é que o erro aumente, isso é devido ao aumento das incertezas quanto ao futuro e também que as previsões a partir do segundo passo são feitas sobre valores previstos e não reais.\n",
    "\n",
    "Abaixo algumas ideias podem ajudar a melhorar a acurácia do modelo:\n",
    "\n",
    " * Pode-se criar um ensemble de vários modelos (LSTM, Garch, MLP, TBATS, etc) e fazer uma média dos preços,\n",
    " * Alguns modelos podem ter previsões melhores para algum período específico, pode-se usar a cada período o modelo que tiver o menor erro, podendo assim, ter um modelo final com bases de modelos diferentes e com erros menores,\n",
    " * Analisar métricas de erro diferentes tipo MAE, Bias, etc para verificar características Específicas de cada tipo de métrica,\n",
    " * Testar possíveis lags sazonais para prever movimentos recorrentes,\n",
    " * Adicionar dados externos correlacionados,\n",
    " * Normalizar os dados,\n",
    " * Fazer a transformação com logaritmos ou usando a transformação Box-Cox.\n",
    " \n",
    "Modelos simples ou até com técnicas de Inteligência Artificial para criar previsões com vários períodos adiante são sem dúvidas uma ferramenta de extrema importância.\n",
    "\n",
    "Diante de um mercado cada vez mais competitivo, criar um modelo com acurácia testada pode ser um diferencial para a empresa, pois ela poderá captar possíveis movimentos de mercado que podem ser essenciais na tomada de decisão do administrador.\n",
    "\n",
    "Modelos semelhantes a este podem ser usados como ferramentas auxiliares para se criar fluxos de caixa, administrar produção e estoques, como também identificar oportunidades de mercado.\n",
    "\n",
    "Espero que tenha gostado, qualquer dúvida ou informação, estou à disposição para responder eventuais questionamentos, segue também o link do meu [LinkedIn](https://www.linkedin.com/in/leandro-rabelo-08722824/) para contato."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Adicional:\n",
    "\n",
    "Abaixo criei uma animação, mostrando a dinâmica das previsões com as atualizações dos preços reais posteriormente:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "/* Put everything inside the global mpl namespace */\n",
       "window.mpl = {};\n",
       "\n",
       "\n",
       "mpl.get_websocket_type = function() {\n",
       "    if (typeof(WebSocket) !== 'undefined') {\n",
       "        return WebSocket;\n",
       "    } else if (typeof(MozWebSocket) !== 'undefined') {\n",
       "        return MozWebSocket;\n",
       "    } else {\n",
       "        alert('Your browser does not have WebSocket support.' +\n",
       "              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
       "              'Firefox 4 and 5 are also supported but you ' +\n",
       "              'have to enable WebSockets in about:config.');\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
       "    this.id = figure_id;\n",
       "\n",
       "    this.ws = websocket;\n",
       "\n",
       "    this.supports_binary = (this.ws.binaryType != undefined);\n",
       "\n",
       "    if (!this.supports_binary) {\n",
       "        var warnings = document.getElementById(\"mpl-warnings\");\n",
       "        if (warnings) {\n",
       "            warnings.style.display = 'block';\n",
       "            warnings.textContent = (\n",
       "                \"This browser does not support binary websocket messages. \" +\n",
       "                    \"Performance may be slow.\");\n",
       "        }\n",
       "    }\n",
       "\n",
       "    this.imageObj = new Image();\n",
       "\n",
       "    this.context = undefined;\n",
       "    this.message = undefined;\n",
       "    this.canvas = undefined;\n",
       "    this.rubberband_canvas = undefined;\n",
       "    this.rubberband_context = undefined;\n",
       "    this.format_dropdown = undefined;\n",
       "\n",
       "    this.image_mode = 'full';\n",
       "\n",
       "    this.root = $('<div/>');\n",
       "    this._root_extra_style(this.root)\n",
       "    this.root.attr('style', 'display: inline-block');\n",
       "\n",
       "    $(parent_element).append(this.root);\n",
       "\n",
       "    this._init_header(this);\n",
       "    this._init_canvas(this);\n",
       "    this._init_toolbar(this);\n",
       "\n",
       "    var fig = this;\n",
       "\n",
       "    this.waiting = false;\n",
       "\n",
       "    this.ws.onopen =  function () {\n",
       "            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
       "            fig.send_message(\"send_image_mode\", {});\n",
       "            if (mpl.ratio != 1) {\n",
       "                fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
       "            }\n",
       "            fig.send_message(\"refresh\", {});\n",
       "        }\n",
       "\n",
       "    this.imageObj.onload = function() {\n",
       "            if (fig.image_mode == 'full') {\n",
       "                // Full images could contain transparency (where diff images\n",
       "                // almost always do), so we need to clear the canvas so that\n",
       "                // there is no ghosting.\n",
       "                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
       "            }\n",
       "            fig.context.drawImage(fig.imageObj, 0, 0);\n",
       "        };\n",
       "\n",
       "    this.imageObj.onunload = function() {\n",
       "        fig.ws.close();\n",
       "    }\n",
       "\n",
       "    this.ws.onmessage = this._make_on_message_function(this);\n",
       "\n",
       "    this.ondownload = ondownload;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_header = function() {\n",
       "    var titlebar = $(\n",
       "        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
       "        'ui-helper-clearfix\"/>');\n",
       "    var titletext = $(\n",
       "        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
       "        'text-align: center; padding: 3px;\"/>');\n",
       "    titlebar.append(titletext)\n",
       "    this.root.append(titlebar);\n",
       "    this.header = titletext[0];\n",
       "}\n",
       "\n",
       "\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_canvas = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var canvas_div = $('<div/>');\n",
       "\n",
       "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
       "\n",
       "    function canvas_keyboard_event(event) {\n",
       "        return fig.key_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
       "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
       "    this.canvas_div = canvas_div\n",
       "    this._canvas_extra_style(canvas_div)\n",
       "    this.root.append(canvas_div);\n",
       "\n",
       "    var canvas = $('<canvas/>');\n",
       "    canvas.addClass('mpl-canvas');\n",
       "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
       "\n",
       "    this.canvas = canvas[0];\n",
       "    this.context = canvas[0].getContext(\"2d\");\n",
       "\n",
       "    var backingStore = this.context.backingStorePixelRatio ||\n",
       "\tthis.context.webkitBackingStorePixelRatio ||\n",
       "\tthis.context.mozBackingStorePixelRatio ||\n",
       "\tthis.context.msBackingStorePixelRatio ||\n",
       "\tthis.context.oBackingStorePixelRatio ||\n",
       "\tthis.context.backingStorePixelRatio || 1;\n",
       "\n",
       "    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
       "\n",
       "    var rubberband = $('<canvas/>');\n",
       "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
       "\n",
       "    var pass_mouse_events = true;\n",
       "\n",
       "    canvas_div.resizable({\n",
       "        start: function(event, ui) {\n",
       "            pass_mouse_events = false;\n",
       "        },\n",
       "        resize: function(event, ui) {\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "        stop: function(event, ui) {\n",
       "            pass_mouse_events = true;\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "    });\n",
       "\n",
       "    function mouse_event_fn(event) {\n",
       "        if (pass_mouse_events)\n",
       "            return fig.mouse_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    rubberband.mousedown('button_press', mouse_event_fn);\n",
       "    rubberband.mouseup('button_release', mouse_event_fn);\n",
       "    // Throttle sequential mouse events to 1 every 20ms.\n",
       "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
       "\n",
       "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
       "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
       "\n",
       "    canvas_div.on(\"wheel\", function (event) {\n",
       "        event = event.originalEvent;\n",
       "        event['data'] = 'scroll'\n",
       "        if (event.deltaY < 0) {\n",
       "            event.step = 1;\n",
       "        } else {\n",
       "            event.step = -1;\n",
       "        }\n",
       "        mouse_event_fn(event);\n",
       "    });\n",
       "\n",
       "    canvas_div.append(canvas);\n",
       "    canvas_div.append(rubberband);\n",
       "\n",
       "    this.rubberband = rubberband;\n",
       "    this.rubberband_canvas = rubberband[0];\n",
       "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
       "    this.rubberband_context.strokeStyle = \"#000000\";\n",
       "\n",
       "    this._resize_canvas = function(width, height) {\n",
       "        // Keep the size of the canvas, canvas container, and rubber band\n",
       "        // canvas in synch.\n",
       "        canvas_div.css('width', width)\n",
       "        canvas_div.css('height', height)\n",
       "\n",
       "        canvas.attr('width', width * mpl.ratio);\n",
       "        canvas.attr('height', height * mpl.ratio);\n",
       "        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
       "\n",
       "        rubberband.attr('width', width);\n",
       "        rubberband.attr('height', height);\n",
       "    }\n",
       "\n",
       "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
       "    // upon first draw.\n",
       "    this._resize_canvas(600, 600);\n",
       "\n",
       "    // Disable right mouse context menu.\n",
       "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
       "        return false;\n",
       "    });\n",
       "\n",
       "    function set_focus () {\n",
       "        canvas.focus();\n",
       "        canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    window.setTimeout(set_focus, 100);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items) {\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) {\n",
       "            // put a spacer in here.\n",
       "            continue;\n",
       "        }\n",
       "        var button = $('<button/>');\n",
       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
       "                        'ui-button-icon-only');\n",
       "        button.attr('role', 'button');\n",
       "        button.attr('aria-disabled', 'false');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "\n",
       "        var icon_img = $('<span/>');\n",
       "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
       "        icon_img.addClass(image);\n",
       "        icon_img.addClass('ui-corner-all');\n",
       "\n",
       "        var tooltip_span = $('<span/>');\n",
       "        tooltip_span.addClass('ui-button-text');\n",
       "        tooltip_span.html(tooltip);\n",
       "\n",
       "        button.append(icon_img);\n",
       "        button.append(tooltip_span);\n",
       "\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    var fmt_picker_span = $('<span/>');\n",
       "\n",
       "    var fmt_picker = $('<select/>');\n",
       "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
       "    fmt_picker_span.append(fmt_picker);\n",
       "    nav_element.append(fmt_picker_span);\n",
       "    this.format_dropdown = fmt_picker[0];\n",
       "\n",
       "    for (var ind in mpl.extensions) {\n",
       "        var fmt = mpl.extensions[ind];\n",
       "        var option = $(\n",
       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
       "        fmt_picker.append(option)\n",
       "    }\n",
       "\n",
       "    // Add hover states to the ui-buttons\n",
       "    $( \".ui-button\" ).hover(\n",
       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
       "    );\n",
       "\n",
       "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
       "    // which will in turn request a refresh of the image.\n",
       "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_message = function(type, properties) {\n",
       "    properties['type'] = type;\n",
       "    properties['figure_id'] = this.id;\n",
       "    this.ws.send(JSON.stringify(properties));\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_draw_message = function() {\n",
       "    if (!this.waiting) {\n",
       "        this.waiting = true;\n",
       "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
       "    }\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    var format_dropdown = fig.format_dropdown;\n",
       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
       "    fig.ondownload(fig, format);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
       "    var size = msg['size'];\n",
       "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
       "        fig._resize_canvas(size[0], size[1]);\n",
       "        fig.send_message(\"refresh\", {});\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
       "    var x0 = msg['x0'] / mpl.ratio;\n",
       "    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
       "    var x1 = msg['x1'] / mpl.ratio;\n",
       "    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
       "    x0 = Math.floor(x0) + 0.5;\n",
       "    y0 = Math.floor(y0) + 0.5;\n",
       "    x1 = Math.floor(x1) + 0.5;\n",
       "    y1 = Math.floor(y1) + 0.5;\n",
       "    var min_x = Math.min(x0, x1);\n",
       "    var min_y = Math.min(y0, y1);\n",
       "    var width = Math.abs(x1 - x0);\n",
       "    var height = Math.abs(y1 - y0);\n",
       "\n",
       "    fig.rubberband_context.clearRect(\n",
       "        0, 0, fig.canvas.width, fig.canvas.height);\n",
       "\n",
       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
       "    // Updates the figure title.\n",
       "    fig.header.textContent = msg['label'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
       "    var cursor = msg['cursor'];\n",
       "    switch(cursor)\n",
       "    {\n",
       "    case 0:\n",
       "        cursor = 'pointer';\n",
       "        break;\n",
       "    case 1:\n",
       "        cursor = 'default';\n",
       "        break;\n",
       "    case 2:\n",
       "        cursor = 'crosshair';\n",
       "        break;\n",
       "    case 3:\n",
       "        cursor = 'move';\n",
       "        break;\n",
       "    }\n",
       "    fig.rubberband_canvas.style.cursor = cursor;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
       "    fig.message.textContent = msg['message'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
       "    // Request the server to send over a new figure.\n",
       "    fig.send_draw_message();\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
       "    fig.image_mode = msg['mode'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Called whenever the canvas gets updated.\n",
       "    this.send_message(\"ack\", {});\n",
       "}\n",
       "\n",
       "// A function to construct a web socket function for onmessage handling.\n",
       "// Called in the figure constructor.\n",
       "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
       "    return function socket_on_message(evt) {\n",
       "        if (evt.data instanceof Blob) {\n",
       "            /* FIXME: We get \"Resource interpreted as Image but\n",
       "             * transferred with MIME type text/plain:\" errors on\n",
       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
       "             * to be part of the websocket stream */\n",
       "            evt.data.type = \"image/png\";\n",
       "\n",
       "            /* Free the memory for the previous frames */\n",
       "            if (fig.imageObj.src) {\n",
       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
       "                    fig.imageObj.src);\n",
       "            }\n",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                evt.data);\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
       "            fig.imageObj.src = evt.data;\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        var msg = JSON.parse(evt.data);\n",
       "        var msg_type = msg['type'];\n",
       "\n",
       "        // Call the  \"handle_{type}\" callback, which takes\n",
       "        // the figure and JSON message as its only arguments.\n",
       "        try {\n",
       "            var callback = fig[\"handle_\" + msg_type];\n",
       "        } catch (e) {\n",
       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        if (callback) {\n",
       "            try {\n",
       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
       "                callback(fig, msg);\n",
       "            } catch (e) {\n",
       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
       "            }\n",
       "        }\n",
       "    };\n",
       "}\n",
       "\n",
       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
       "mpl.findpos = function(e) {\n",
       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
       "    var targ;\n",
       "    if (!e)\n",
       "        e = window.event;\n",
       "    if (e.target)\n",
       "        targ = e.target;\n",
       "    else if (e.srcElement)\n",
       "        targ = e.srcElement;\n",
       "    if (targ.nodeType == 3) // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "\n",
       "    // jQuery normalizes the pageX and pageY\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    // offset() returns the position of the element relative to the document\n",
       "    var x = e.pageX - $(targ).offset().left;\n",
       "    var y = e.pageY - $(targ).offset().top;\n",
       "\n",
       "    return {\"x\": x, \"y\": y};\n",
       "};\n",
       "\n",
       "/*\n",
       " * return a copy of an object with only non-object keys\n",
       " * we need this to avoid circular references\n",
       " * http://stackoverflow.com/a/24161582/3208463\n",
       " */\n",
       "function simpleKeys (original) {\n",
       "  return Object.keys(original).reduce(function (obj, key) {\n",
       "    if (typeof original[key] !== 'object')\n",
       "        obj[key] = original[key]\n",
       "    return obj;\n",
       "  }, {});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
       "    var canvas_pos = mpl.findpos(event)\n",
       "\n",
       "    if (name === 'button_press')\n",
       "    {\n",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x * mpl.ratio;\n",
       "    var y = canvas_pos.y * mpl.ratio;\n",
       "\n",
       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
       "                             step: event.step,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "\n",
       "    /* This prevents the web browser from automatically changing to\n",
       "     * the text insertion cursor when the button is pressed.  We want\n",
       "     * to control all of the cursor setting manually through the\n",
       "     * 'cursor' event from matplotlib */\n",
       "    event.preventDefault();\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    // Handle any extra behaviour associated with a key event\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.key_event = function(event, name) {\n",
       "\n",
       "    // Prevent repeat events\n",
       "    if (name == 'key_press')\n",
       "    {\n",
       "        if (event.which === this._key)\n",
       "            return;\n",
       "        else\n",
       "            this._key = event.which;\n",
       "    }\n",
       "    if (name == 'key_release')\n",
       "        this._key = null;\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.which != 17)\n",
       "        value += \"ctrl+\";\n",
       "    if (event.altKey && event.which != 18)\n",
       "        value += \"alt+\";\n",
       "    if (event.shiftKey && event.which != 16)\n",
       "        value += \"shift+\";\n",
       "\n",
       "    value += 'k';\n",
       "    value += event.which.toString();\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, {key: value,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
       "    if (name == 'download') {\n",
       "        this.handle_save(this, null);\n",
       "    } else {\n",
       "        this.send_message(\"toolbar_button\", {name: name});\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
       "    this.message.textContent = tooltip;\n",
       "};\n",
       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
       "\n",
       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
       "    // object with the appropriate methods. Currently this is a non binary\n",
       "    // socket, so there is still some room for performance tuning.\n",
       "    var ws = {};\n",
       "\n",
       "    ws.close = function() {\n",
       "        comm.close()\n",
       "    };\n",
       "    ws.send = function(m) {\n",
       "        //console.log('sending', m);\n",
       "        comm.send(m);\n",
       "    };\n",
       "    // Register the callback with on_msg.\n",
       "    comm.on_msg(function(msg) {\n",
       "        //console.log('receiving', msg['content']['data'], msg);\n",
       "        // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
       "        ws.onmessage(msg['content']['data'])\n",
       "    });\n",
       "    return ws;\n",
       "}\n",
       "\n",
       "mpl.mpl_figure_comm = function(comm, msg) {\n",
       "    // This is the function which gets called when the mpl process\n",
       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
       "\n",
       "    var id = msg.content.data.id;\n",
       "    // Get hold of the div created by the display call when the Comm\n",
       "    // socket was opened in Python.\n",
       "    var element = $(\"#\" + id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm)\n",
       "\n",
       "    function ondownload(figure, format) {\n",
       "        window.open(figure.imageObj.src);\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy,\n",
       "                           ondownload,\n",
       "                           element.get(0));\n",
       "\n",
       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
       "    // web socket which is closed, not our websocket->open comm proxy.\n",
       "    ws_proxy.onopen();\n",
       "\n",
       "    fig.parent_element = element.get(0);\n",
       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
       "    if (!fig.cell_info) {\n",
       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
       "        return;\n",
       "    }\n",
       "\n",
       "    var output_index = fig.cell_info[2]\n",
       "    var cell = fig.cell_info[0];\n",
       "\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
       "    var width = fig.canvas.width/mpl.ratio\n",
       "    fig.root.unbind('remove')\n",
       "\n",
       "    // Update the output cell to use the data from the current canvas.\n",
       "    fig.push_to_output();\n",
       "    var dataURL = fig.canvas.toDataURL();\n",
       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
       "    // the notebook keyboard shortcuts fail.\n",
       "    IPython.keyboard_manager.enable()\n",
       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
       "    fig.close_ws(fig, msg);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
       "    fig.send_message('closing', msg);\n",
       "    // fig.ws.close()\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
       "    // Turn the data on the canvas into data in the output cell.\n",
       "    var width = this.canvas.width/mpl.ratio\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Tell IPython that the notebook contents must change.\n",
       "    IPython.notebook.set_dirty(true);\n",
       "    this.send_message(\"ack\", {});\n",
       "    var fig = this;\n",
       "    // Wait a second, then push the new image to the DOM so\n",
       "    // that it is saved nicely (might be nice to debounce this).\n",
       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items){\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) { continue; };\n",
       "\n",
       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
       "    buttongrp.append(button);\n",
       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
       "    titlebar.prepend(buttongrp);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(el){\n",
       "    var fig = this\n",
       "    el.on(\"remove\", function(){\n",
       "\tfig.close_ws(fig, {});\n",
       "    });\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
       "    // this is important to make the div 'focusable\n",
       "    el.attr('tabindex', 0)\n",
       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
       "    // off when our div gets focus\n",
       "\n",
       "    // location in version 3\n",
       "    if (IPython.notebook.keyboard_manager) {\n",
       "        IPython.notebook.keyboard_manager.register_events(el);\n",
       "    }\n",
       "    else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    var manager = IPython.notebook.keyboard_manager;\n",
       "    if (!manager)\n",
       "        manager = IPython.keyboard_manager;\n",
       "\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which == 13) {\n",
       "        this.canvas_div.blur();\n",
       "        event.shiftKey = false;\n",
       "        // Send a \"J\" for go to next cell\n",
       "        event.which = 74;\n",
       "        event.keyCode = 74;\n",
       "        manager.command_mode();\n",
       "        manager.handle_keydown(event);\n",
       "    }\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.find_output_cell = function(html_output) {\n",
       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
       "    // IPython event is triggered only after the cells have been serialised, which for\n",
       "    // our purposes (turning an active figure into a static one), is too late.\n",
       "    var cells = IPython.notebook.get_cells();\n",
       "    var ncells = cells.length;\n",
       "    for (var i=0; i<ncells; i++) {\n",
       "        var cell = cells[i];\n",
       "        if (cell.cell_type === 'code'){\n",
       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
       "                var data = cell.output_area.outputs[j];\n",
       "                if (data.data) {\n",
       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
       "                    data = data.data;\n",
       "                }\n",
       "                if (data['text/html'] == html_output) {\n",
       "                    return [cell, data, j];\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "}\n",
       "\n",
       "// Register the function which deals with the matplotlib target/channel.\n",
       "// The kernel may be null if the page has been refreshed.\n",
       "if (IPython.notebook.kernel != null) {\n",
       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
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     "output_type": "display_data"
    },
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\" width=\"1050\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib notebook\n",
    "\n",
    "passos = 5\n",
    "eixo_x_prev = []\n",
    "eixo_x_real = [x for x in range(len(teste))]\n",
    "prev = []\n",
    "pred = np.array([x for x in teste['Previsão 5D']])\n",
    "base = [x for x in teste['Cristal']]\n",
    "\n",
    "for i in range(0,len(teste), passos):\n",
    "    eixo_x_prev.append([x for x in range(i,i+passos)])\n",
    "    prev.append([x for x in pred[i:i+passos]])\n",
    "\n",
    "# Criando a base dos gráficos\n",
    "fig = plt.figure(figsize=(10.5,5))\n",
    "plt.axis([0, len(base), min(base)-2.5,max(base)+2.5])\n",
    "\n",
    "def animate(i):\n",
    "    \n",
    "    plt.plot(eixo_x_prev[i], prev[i], c='orangered', lw=1)\n",
    "    plt.plot(eixo_x_real[:i*passos], base[:i*passos], c='teal', lw=0.50)\n",
    "plt.xlabel('Intervalos', fontsize=12)\n",
    "plt.ylabel('Preços', fontsize=15)\n",
    "plt.title('Comparativo Preço Açúcar Cristal - Real x Previsto', fontsize=15)\n",
    "plt.axis([0, len(base), min(base)-1.5 ,max(base)+1.5])\n",
    "    \n",
    "\n",
    "ani = FuncAnimation(fig, animate, frames=50, interval=250)\n",
    "#ani.save('comparativo.htm')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Fontes:\n",
    "\n",
    "[Multi-step Time Series Forecasting with Machine Learning for Electricity Usage](https://machinelearningmastery.com/multi-step-time-series-forecasting-with-machine-learning-models-for-household-electricity-consumption/)\n",
    "\n",
    "[4 Strategies for Multi-Step Time Series Forecasting](https://machinelearningmastery.com/multi-step-time-series-forecasting/)."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}