{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Time Series regression - Random Forest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Loading required package: daltoolbox\n",
      "\n",
      "Registered S3 method overwritten by 'quantmod':\n",
      "  method            from\n",
      "  as.zoo.data.frame zoo \n",
      "\n",
      "\n",
      "Attaching package: ‘daltoolbox’\n",
      "\n",
      "\n",
      "The following object is masked from ‘package:base’:\n",
      "\n",
      "    transform\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# DAL ToolBox\n",
    "# version 1.0.777\n",
    "\n",
    "source(\"https://raw.githubusercontent.com/cefet-rj-dal/daltoolbox/main/jupyter.R\")\n",
    "\n",
    "#loading DAL\n",
    "load_library(\"daltoolbox\") "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Series for studying"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A matrix: 3 × 10 of type dbl</caption>\n",
       "<thead>\n",
       "\t<tr><th scope=col>t9</th><th scope=col>t8</th><th scope=col>t7</th><th scope=col>t6</th><th scope=col>t5</th><th scope=col>t4</th><th scope=col>t3</th><th scope=col>t2</th><th scope=col>t1</th><th scope=col>t0</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><td>0.0000000</td><td>0.2474040</td><td>0.4794255</td><td>0.6816388</td><td>0.8414710</td><td>0.9489846</td><td>0.9974950</td><td>0.9839859</td><td>0.9092974</td><td>0.7780732</td></tr>\n",
       "\t<tr><td>0.2474040</td><td>0.4794255</td><td>0.6816388</td><td>0.8414710</td><td>0.9489846</td><td>0.9974950</td><td>0.9839859</td><td>0.9092974</td><td>0.7780732</td><td>0.5984721</td></tr>\n",
       "\t<tr><td>0.4794255</td><td>0.6816388</td><td>0.8414710</td><td>0.9489846</td><td>0.9974950</td><td>0.9839859</td><td>0.9092974</td><td>0.7780732</td><td>0.5984721</td><td>0.3816610</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A matrix: 3 × 10 of type dbl\n",
       "\\begin{tabular}{llllllllll}\n",
       " t9 & t8 & t7 & t6 & t5 & t4 & t3 & t2 & t1 & t0\\\\\n",
       "\\hline\n",
       "\t 0.0000000 & 0.2474040 & 0.4794255 & 0.6816388 & 0.8414710 & 0.9489846 & 0.9974950 & 0.9839859 & 0.9092974 & 0.7780732\\\\\n",
       "\t 0.2474040 & 0.4794255 & 0.6816388 & 0.8414710 & 0.9489846 & 0.9974950 & 0.9839859 & 0.9092974 & 0.7780732 & 0.5984721\\\\\n",
       "\t 0.4794255 & 0.6816388 & 0.8414710 & 0.9489846 & 0.9974950 & 0.9839859 & 0.9092974 & 0.7780732 & 0.5984721 & 0.3816610\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A matrix: 3 × 10 of type dbl\n",
       "\n",
       "| t9 | t8 | t7 | t6 | t5 | t4 | t3 | t2 | t1 | t0 |\n",
       "|---|---|---|---|---|---|---|---|---|---|\n",
       "| 0.0000000 | 0.2474040 | 0.4794255 | 0.6816388 | 0.8414710 | 0.9489846 | 0.9974950 | 0.9839859 | 0.9092974 | 0.7780732 |\n",
       "| 0.2474040 | 0.4794255 | 0.6816388 | 0.8414710 | 0.9489846 | 0.9974950 | 0.9839859 | 0.9092974 | 0.7780732 | 0.5984721 |\n",
       "| 0.4794255 | 0.6816388 | 0.8414710 | 0.9489846 | 0.9974950 | 0.9839859 | 0.9092974 | 0.7780732 | 0.5984721 | 0.3816610 |\n",
       "\n"
      ],
      "text/plain": [
       "     t9        t8        t7        t6        t5        t4        t3       \n",
       "[1,] 0.0000000 0.2474040 0.4794255 0.6816388 0.8414710 0.9489846 0.9974950\n",
       "[2,] 0.2474040 0.4794255 0.6816388 0.8414710 0.9489846 0.9974950 0.9839859\n",
       "[3,] 0.4794255 0.6816388 0.8414710 0.9489846 0.9974950 0.9839859 0.9092974\n",
       "     t2        t1        t0       \n",
       "[1,] 0.9839859 0.9092974 0.7780732\n",
       "[2,] 0.9092974 0.7780732 0.5984721\n",
       "[3,] 0.7780732 0.5984721 0.3816610"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data(sin_data)\n",
    "ts <- ts_data(sin_data$y, 10)\n",
    "ts_head(ts, 3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Fahjowb9zPPKkvnIi63aFBK/JnqLYEi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",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 420
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "library(ggplot2)\n",
    "plot_ts(x=sin_data$x, y=sin_data$y) + theme(text = element_text(size=16))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### data sampling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "samp <- ts_sample(ts, test_size = 5)\n",
    "io_train <- ts_projection(samp$train)\n",
    "io_test <- ts_projection(samp$test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### data preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "preproc <- ts_norm_gminmax()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Model training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "model <- ts_rf(ts_norm_gminmax(), input_size=8, nodesize=1, ntree=20)\n",
    "model <- fit(model, x=io_train$input, y=io_train$output)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Evaluation of adjustment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "0.0130007017353472"
      ],
      "text/latex": [
       "0.0130007017353472"
      ],
      "text/markdown": [
       "0.0130007017353472"
      ],
      "text/plain": [
       "[1] 0.0130007"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "adjust <- predict(model, io_train$input)\n",
    "adjust <- as.vector(adjust)\n",
    "output <- as.vector(io_train$output)\n",
    "ev_adjust <- evaluate(model, output, adjust)\n",
    "ev_adjust$mse"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Prediction of test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<dl>\n",
       "\t<dt>$values</dt>\n",
       "\t\t<dd><style>\n",
       ".list-inline {list-style: none; margin:0; padding: 0}\n",
       ".list-inline>li {display: inline-block}\n",
       ".list-inline>li:not(:last-child)::after {content: \"\\00b7\"; padding: 0 .5ex}\n",
       "</style>\n",
       "<ol class=list-inline><li>0.412118485241757</li><li>0.173889485380434</li><li>-0.0751511204618093</li><li>-0.319519193622274</li><li>-0.54402111088937</li></ol>\n",
       "</dd>\n",
       "\t<dt>$prediction</dt>\n",
       "\t\t<dd><style>\n",
       ".list-inline {list-style: none; margin:0; padding: 0}\n",
       ".list-inline>li {display: inline-block}\n",
       ".list-inline>li:not(:last-child)::after {content: \"\\00b7\"; padding: 0 .5ex}\n",
       "</style>\n",
       "<ol class=list-inline><li>0.402155615660453</li><li>0.184556008771018</li><li>0.00324536266109932</li><li>-0.1028411883046</li><li>-0.232685009857555</li></ol>\n",
       "</dd>\n",
       "\t<dt>$smape</dt>\n",
       "\t\t<dd>0.782340582088694</dd>\n",
       "\t<dt>$mse</dt>\n",
       "\t\t<dd>0.030047713570343</dd>\n",
       "\t<dt>$R2</dt>\n",
       "\t\t<dd>0.740476345882331</dd>\n",
       "\t<dt>$metrics</dt>\n",
       "\t\t<dd><table class=\"dataframe\">\n",
       "<caption>A data.frame: 1 × 3</caption>\n",
       "<thead>\n",
       "\t<tr><th scope=col>mse</th><th scope=col>smape</th><th scope=col>R2</th></tr>\n",
       "\t<tr><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><td>0.03004771</td><td>0.7823406</td><td>0.7404763</td></tr>\n",
       "</tbody>\n",
       "</table>\n",
       "</dd>\n",
       "</dl>\n"
      ],
      "text/latex": [
       "\\begin{description}\n",
       "\\item[\\$values] \\begin{enumerate*}\n",
       "\\item 0.412118485241757\n",
       "\\item 0.173889485380434\n",
       "\\item -0.0751511204618093\n",
       "\\item -0.319519193622274\n",
       "\\item -0.54402111088937\n",
       "\\end{enumerate*}\n",
       "\n",
       "\\item[\\$prediction] \\begin{enumerate*}\n",
       "\\item 0.402155615660453\n",
       "\\item 0.184556008771018\n",
       "\\item 0.00324536266109932\n",
       "\\item -0.1028411883046\n",
       "\\item -0.232685009857555\n",
       "\\end{enumerate*}\n",
       "\n",
       "\\item[\\$smape] 0.782340582088694\n",
       "\\item[\\$mse] 0.030047713570343\n",
       "\\item[\\$R2] 0.740476345882331\n",
       "\\item[\\$metrics] A data.frame: 1 × 3\n",
       "\\begin{tabular}{lll}\n",
       " mse & smape & R2\\\\\n",
       " <dbl> & <dbl> & <dbl>\\\\\n",
       "\\hline\n",
       "\t 0.03004771 & 0.7823406 & 0.7404763\\\\\n",
       "\\end{tabular}\n",
       "\n",
       "\\end{description}\n"
      ],
      "text/markdown": [
       "$values\n",
       ":   1. 0.412118485241757\n",
       "2. 0.173889485380434\n",
       "3. -0.0751511204618093\n",
       "4. -0.319519193622274\n",
       "5. -0.54402111088937\n",
       "\n",
       "\n",
       "\n",
       "$prediction\n",
       ":   1. 0.402155615660453\n",
       "2. 0.184556008771018\n",
       "3. 0.00324536266109932\n",
       "4. -0.1028411883046\n",
       "5. -0.232685009857555\n",
       "\n",
       "\n",
       "\n",
       "$smape\n",
       ":   0.782340582088694\n",
       "$mse\n",
       ":   0.030047713570343\n",
       "$R2\n",
       ":   0.740476345882331\n",
       "$metrics\n",
       ":   \n",
       "A data.frame: 1 × 3\n",
       "\n",
       "| mse &lt;dbl&gt; | smape &lt;dbl&gt; | R2 &lt;dbl&gt; |\n",
       "|---|---|---|\n",
       "| 0.03004771 | 0.7823406 | 0.7404763 |\n",
       "\n",
       "\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "$values\n",
       "[1]  0.41211849  0.17388949 -0.07515112 -0.31951919 -0.54402111\n",
       "\n",
       "$prediction\n",
       "[1]  0.402155616  0.184556009  0.003245363 -0.102841188 -0.232685010\n",
       "\n",
       "$smape\n",
       "[1] 0.7823406\n",
       "\n",
       "$mse\n",
       "[1] 0.03004771\n",
       "\n",
       "$R2\n",
       "[1] 0.7404763\n",
       "\n",
       "$metrics\n",
       "         mse     smape        R2\n",
       "1 0.03004771 0.7823406 0.7404763\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "prediction <- predict(model, x=io_test$input[1,], steps_ahead=5)\n",
    "prediction <- as.vector(prediction)\n",
    "output <- as.vector(io_test$output)\n",
    "ev_test <- evaluate(model, output, prediction)\n",
    "ev_test"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plot results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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xcUFIhORJrFYieGh4dHhw4dgoOD+TVGRHpj3bp1Mpns+Uft/BoejrIy9OihnVCE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/f3Fxsegs5KDMZnNaWlpAQMDYsWNFZyEi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\nAGgEwQ4AAEAjCHYAAAAaQbADAADQiP8Dv62A8rmetEEAAAAASUVORK5CYII=",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 420
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "yvalues <- c(io_train$output, io_test$output)\n",
    "plot_ts_pred(y=yvalues, yadj=adjust, ypre=prediction) + theme(text = element_text(size=16))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "R",
   "language": "R",
   "name": "ir"
  },
  "language_info": {
   "codemirror_mode": "r",
   "file_extension": ".r",
   "mimetype": "text/x-r-source",
   "name": "R",
   "pygments_lexer": "r",
   "version": "4.3.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}