{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Time Series regression - SVM"
   ]
  },
  {
   "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$test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### data preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "preproc <- ts_norm_gminmax()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Model training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "model <- ts_svm(ts_norm_gminmax(), input_size=4)\n",
    "model <- fit(model, x=io_train$input, y=io_train$output)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Evaluation of adjustment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "5.13009820927412e-07"
      ],
      "text/latex": [
       "5.13009820927412e-07"
      ],
      "text/markdown": [
       "5.13009820927412e-07"
      ],
      "text/plain": [
       "[1] 5.130098e-07"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "adjust <- predict(model, io_train$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>0.412689338912016</li><li>0.173334476101177</li><li>-0.0756329888924858</li><li>-0.319834089168869</li><li>-0.544817810267272</li></ol>\n",
       "</dd>\n",
       "\t<dt>$smape</dt>\n",
       "\t\t<dd>0.00268419711357045</dd>\n",
       "\t<dt>$mse</dt>\n",
       "\t\t<dd>3.19999100281712e-07</dd>\n",
       "\t<dt>$R2</dt>\n",
       "\t\t<dd>0.999997236151242</dd>\n",
       "\t<dt>$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>3.199991e-07</td><td>0.002684197</td><td>0.9999972</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 0.412689338912016\n",
       "\\item 0.173334476101177\n",
       "\\item -0.0756329888924858\n",
       "\\item -0.319834089168869\n",
       "\\item -0.544817810267272\n",
       "\\end{enumerate*}\n",
       "\n",
       "\\item[\\$smape] 0.00268419711357045\n",
       "\\item[\\$mse] 3.19999100281712e-07\n",
       "\\item[\\$R2] 0.999997236151242\n",
       "\\item[\\$metrics] A data.frame: 1 × 3\n",
       "\\begin{tabular}{lll}\n",
       " mse & smape & R2\\\\\n",
       " <dbl> & <dbl> & <dbl>\\\\\n",
       "\\hline\n",
       "\t 3.199991e-07 & 0.002684197 & 0.9999972\\\\\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. 0.412689338912016\n",
       "2. 0.173334476101177\n",
       "3. -0.0756329888924858\n",
       "4. -0.319834089168869\n",
       "5. -0.544817810267272\n",
       "\n",
       "\n",
       "\n",
       "$smape\n",
       ":   0.00268419711357045\n",
       "$mse\n",
       ":   3.19999100281712e-07\n",
       "$R2\n",
       ":   0.999997236151242\n",
       "$metrics\n",
       ":   \n",
       "A data.frame: 1 × 3\n",
       "\n",
       "| mse &lt;dbl&gt; | smape &lt;dbl&gt; | R2 &lt;dbl&gt; |\n",
       "|---|---|---|\n",
       "| 3.199991e-07 | 0.002684197 | 0.9999972 |\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]  0.41268934  0.17333448 -0.07563299 -0.31983409 -0.54481781\n",
       "\n",
       "$smape\n",
       "[1] 0.002684197\n",
       "\n",
       "$mse\n",
       "[1] 3.199991e-07\n",
       "\n",
       "$R2\n",
       "[1] 0.9999972\n",
       "\n",
       "$metrics\n",
       "           mse       smape        R2\n",
       "1 3.199991e-07 0.002684197 0.9999972\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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lBWVrZkyRJjY+PFixdzt4iLpaXlSy+9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\nAABQBMUOAABAERQ7AAAARVDsAAAAFPEfc5f86HmBCOYAAAAASUVORK5CYII=",
      "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
}