{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Normalization Exponential Adaptive Normalization"
   ]
  },
  {
   "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 1.01.727\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": [],
   "source": [
    "data(sin_data)"
   ]
  },
  {
   "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": [
    "### sliding windows"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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"
    },
    {
     "data": {
      "text/plain": [
       "       t0          \n",
       " Min.   :-0.99929  \n",
       " 1st Qu.:-0.55091  \n",
       " Median : 0.05397  \n",
       " Mean   : 0.02988  \n",
       " 3rd Qu.: 0.63279  \n",
       " Max.   : 0.99460  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sw_size <- 10\n",
    "ts <- ts_data(sin_data$y, sw_size)\n",
    "ts_head(ts, 3)\n",
    "summary(ts[,10])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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aqasTjgd8Ku31C1SFPhFV5DG2ANPIIdQQxxVxSqWqSdcCevoQ0wEqZiRxBE2DXu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\nAAAAAElFTkSuQmCC",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 420
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "library(ggplot2)\n",
    "plot_ts(y=ts[,10]) + theme(text = element_text(size=16))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### normalization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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>0.2323665</td><td>0.3104452</td><td>0.3836695</td><td>0.4474864</td><td>0.4979282</td><td>0.5318587</td><td>0.5471682</td><td>0.5429048</td><td>0.5193337</td><td>0.4779203</td></tr>\n",
       "\t<tr><td>0.3580339</td><td>0.4312581</td><td>0.4950750</td><td>0.5455169</td><td>0.5794473</td><td>0.5947568</td><td>0.5904935</td><td>0.5669224</td><td>0.5255090</td><td>0.4688283</td></tr>\n",
       "\t<tr><td>0.4924682</td><td>0.5562851</td><td>0.6067269</td><td>0.6406574</td><td>0.6559669</td><td>0.6517035</td><td>0.6281324</td><td>0.5867191</td><td>0.5300384</td><td>0.4616144</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 0.2323665 & 0.3104452 & 0.3836695 & 0.4474864 & 0.4979282 & 0.5318587 & 0.5471682 & 0.5429048 & 0.5193337 & 0.4779203\\\\\n",
       "\t 0.3580339 & 0.4312581 & 0.4950750 & 0.5455169 & 0.5794473 & 0.5947568 & 0.5904935 & 0.5669224 & 0.5255090 & 0.4688283\\\\\n",
       "\t 0.4924682 & 0.5562851 & 0.6067269 & 0.6406574 & 0.6559669 & 0.6517035 & 0.6281324 & 0.5867191 & 0.5300384 & 0.4616144\\\\\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",
       "| 0.2323665 | 0.3104452 | 0.3836695 | 0.4474864 | 0.4979282 | 0.5318587 | 0.5471682 | 0.5429048 | 0.5193337 | 0.4779203 |\n",
       "| 0.3580339 | 0.4312581 | 0.4950750 | 0.5455169 | 0.5794473 | 0.5947568 | 0.5904935 | 0.5669224 | 0.5255090 | 0.4688283 |\n",
       "| 0.4924682 | 0.5562851 | 0.6067269 | 0.6406574 | 0.6559669 | 0.6517035 | 0.6281324 | 0.5867191 | 0.5300384 | 0.4616144 |\n",
       "\n"
      ],
      "text/plain": [
       "     t9        t8        t7        t6        t5        t4        t3       \n",
       "[1,] 0.2323665 0.3104452 0.3836695 0.4474864 0.4979282 0.5318587 0.5471682\n",
       "[2,] 0.3580339 0.4312581 0.4950750 0.5455169 0.5794473 0.5947568 0.5904935\n",
       "[3,] 0.4924682 0.5562851 0.6067269 0.6406574 0.6559669 0.6517035 0.6281324\n",
       "     t2        t1        t0       \n",
       "[1,] 0.5429048 0.5193337 0.4779203\n",
       "[2,] 0.5669224 0.5255090 0.4688283\n",
       "[3,] 0.5867191 0.5300384 0.4616144"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "       t0        \n",
       " Min.   :0.4545  \n",
       " 1st Qu.:0.4608  \n",
       " Median :0.4804  \n",
       " Mean   :0.4911  \n",
       " 3rd Qu.:0.5226  \n",
       " Max.   :0.5437  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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AhwFiHVu1g9Kz0CnEVIdVYXfIXlvuEVIdUZqGZLjwB3EVKtze0+v196BriLkGqN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\nAElFTkSuQmCC",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 420
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "preproc <- ts_norm_ean(nw = 3)\n",
    "preproc <- fit(preproc, ts)\n",
    "tst <- transform(preproc, ts)\n",
    "ts_head(tst, 3)\n",
    "summary(tst[,10])\n",
    "plot_ts(y=ts[1,]) + 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": "4.4.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}