{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Time Series regression - ELM"
   ]
  },
  {
   "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$y, 10)\n",
    "ts_head(ts, 3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA0gAAANICAMAAADKOT/pAAADAFBMVEUAAAABAQECAgIDAwME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",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 420
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "library(ggplot2)\n",
    "plot_ts(x=sin_data$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$test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### data preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "preproc <- ts_norm_gminmax()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Model training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "model <- ts_elm(ts_norm_gminmax(), input_size=4, nhid=3, actfun=\"purelin\")\n",
    "model <- fit(model, x=io_train$input, y=io_train$output)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Evaluation of adjustment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "2.36435074831741e-27"
      ],
      "text/latex": [
       "2.36435074831741e-27"
      ],
      "text/markdown": [
       "2.36435074831741e-27"
      ],
      "text/plain": [
       "[1] 2.364351e-27"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "adjust <- predict(model, io_train$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>0.412118485241686</li><li>0.173889485380267</li><li>-0.0751511204622126</li><li>-0.319519193622904</li><li>-0.544021110890444</li></ol>\n",
       "</dd>\n",
       "\t<dt>$smape</dt>\n",
       "\t\t<dd>2.08907219169357e-12</dd>\n",
       "\t<dt>$mse</dt>\n",
       "\t\t<dd>3.49308643903653e-25</dd>\n",
       "\t<dt>$R2</dt>\n",
       "\t\t<dd>1</dd>\n",
       "\t<dt>$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>3.493086e-25</td><td>2.089072e-12</td><td>1</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 0.412118485241686\n",
       "\\item 0.173889485380267\n",
       "\\item -0.0751511204622126\n",
       "\\item -0.319519193622904\n",
       "\\item -0.544021110890444\n",
       "\\end{enumerate*}\n",
       "\n",
       "\\item[\\$smape] 2.08907219169357e-12\n",
       "\\item[\\$mse] 3.49308643903653e-25\n",
       "\\item[\\$R2] 1\n",
       "\\item[\\$metrics] A data.frame: 1 × 3\n",
       "\\begin{tabular}{lll}\n",
       " mse & smape & R2\\\\\n",
       " <dbl> & <dbl> & <dbl>\\\\\n",
       "\\hline\n",
       "\t 3.493086e-25 & 2.089072e-12 & 1\\\\\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. 0.412118485241686\n",
       "2. 0.173889485380267\n",
       "3. -0.0751511204622126\n",
       "4. -0.319519193622904\n",
       "5. -0.544021110890444\n",
       "\n",
       "\n",
       "\n",
       "$smape\n",
       ":   2.08907219169357e-12\n",
       "$mse\n",
       ":   3.49308643903653e-25\n",
       "$R2\n",
       ":   1\n",
       "$metrics\n",
       ":   \n",
       "A data.frame: 1 × 3\n",
       "\n",
       "| mse &lt;dbl&gt; | smape &lt;dbl&gt; | R2 &lt;dbl&gt; |\n",
       "|---|---|---|\n",
       "| 3.493086e-25 | 2.089072e-12 | 1 |\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]  0.41211849  0.17388949 -0.07515112 -0.31951919 -0.54402111\n",
       "\n",
       "$smape\n",
       "[1] 2.089072e-12\n",
       "\n",
       "$mse\n",
       "[1] 3.493086e-25\n",
       "\n",
       "$R2\n",
       "[1] 1\n",
       "\n",
       "$metrics\n",
       "           mse        smape R2\n",
       "1 3.493086e-25 2.089072e-12  1\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": "iVBORw0KGgoAAAANSUhEUgAAA0gAAANICAIAAAByhViMAAAACXBIWXMAABJ0AAASdAHeZh94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\nOwAAAEX8C6L8xYl/f4s+AAAAAElFTkSuQmCC",
      "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
}