ETS + ARIMA_actueel.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 344,
   "id": "cd03fe6a-7c8e-4c1d-916e-09407337787a",
   "metadata": {},
   "outputs": [],
   "source": [
    "library(dplyr)\n",
    "library(tsibble)\n",
    "library(fpp3) \n",
    "library(ggplot2)\n",
    "library(corrplot)\n",
    "library(ggcorrplot)\n",
    "\n",
    "\n",
    "library(dplyr)\n",
    "library(tsibble)\n",
    "library(fpp3) \n",
    "\n",
    "options(repr.plot.width=25)\n",
    "\n",
    "evaluation <- read.csv('Datasets/sales_test_evaluation_afcs_2024.csv')\n",
    "\n",
    "calendar <- read.csv('Datasets/calendar_afcs2024.csv')\n",
    "prices <- read.csv('Datasets/sell_prices_afcs2024.csv')\n",
    "sales_train <- read.csv('Datasets/sales_train_validation_afcs2024.csv')\n",
    "sales_val <- read.csv('Datasets/sales_test_validation_afcs2024.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 284,
   "id": "76908637-bfb6-452f-846a-f3c516059875",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Transpose sale CSV to match forcasting-usable format\n",
    "# After transpose, ensure datatypes are int, surpress row names being selected in df\n",
    "\n",
    "sales_transposed <- t(sales_train)\n",
    "sales_df <- as.data.frame(sales_transposed[-1,]) %>% mutate_all(as.integer)\n",
    "colnames(sales_df) <- sales_transposed[1,]\n",
    "row.names(sales_df) <- NULL\n",
    "\n",
    "#sales_df <- tibble::rownames_to_column(sales_df, \"Day\")\n",
    "#sales_ts <- as_tsibble(sales_df, index = Day)\n",
    "#sales_ts <- as_tsibble(sales_df)\n",
    "\n",
    "# Count totals over the rows, just as a possible informative data about overall food items sales\n",
    "# Add day counter to both df's to make merging possible (otherwise no key)\n",
    "\n",
    "sales_df$total <- rowSums(sales_df)\n",
    "sales_df$day <- 1:nrow(sales_df)\n",
    "calendar$day <- 1:nrow(calendar)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 285,
   "id": "60143af9-8819-4bf1-83ee-7858233b94a3",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Merge dataframes and convert to tsibble \n",
    "\n",
    "calendar_sales <- merge(calendar,sales_df)\n",
    "calendar_sales <- calendar_sales %>% as_tsibble(index = day)\n",
    "calendar_sales$is_event <- ifelse(calendar_sales$event_name_1 != \"NA\", 1, 0)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 286,
   "id": "477c300e-a075-48cd-be48-49fe9ffeac65",
   "metadata": {},
   "outputs": [
    {
     "data": {
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RCtjGWiRdxhFsQdjkO0MohWBcnKIFoZ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\nRtwRdw2Tb6ZcY1UbiHv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HQ7mHrD2VNp29Zu9jsmE7YC21h88rvEPbZb1EEomaIE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X9+Lp8eRbxb3qaWzYtWMDH4h7+Wd0qpmf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\nJyV3zVo8ww2stort5e3ivrxL3PMxefDwu9irAjvoYeZ8cfxZ1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tS+ZuSnLO1reELcT7yLK4K4QzuIuwxS60Dc+wdxlzFlahH3wUHcVSDu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\nAAAAABiA/w8WN8hwK6nZ4QAAAABJRU5ErkJggg==",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 1500
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Overall Food sales, informative. See cyclic behavior \n",
    "\n",
    "calendar_sales %>% autoplot(total)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 287,
   "id": "4c2f9763-55db-4b93-9487-8f62d56aa4aa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "TRUE"
      ],
      "text/latex": [
       "TRUE"
      ],
      "text/markdown": [
       "TRUE"
      ],
      "text/plain": [
       "[1] TRUE"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "is_regular(calendar_sales)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 288,
   "id": "715cf337-a207-4d17-a17a-b8fe08c70e8a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A tbl_ts: 5 × 837</caption>\n",
       "<thead>\n",
       "\t<tr><th scope=col>day</th><th scope=col>date</th><th scope=col>wm_yr_wk</th><th scope=col>weekday</th><th scope=col>wday</th><th scope=col>month</th><th scope=col>year</th><th scope=col>event_name_1</th><th scope=col>event_type_1</th><th scope=col>event_name_2</th><th scope=col>⋯</th><th scope=col>FOODS_3_820_TX_3_validation</th><th scope=col>FOODS_3_821_TX_3_validation</th><th scope=col>FOODS_3_822_TX_3_validation</th><th scope=col>FOODS_3_823_TX_3_validation</th><th scope=col>FOODS_3_824_TX_3_validation</th><th scope=col>FOODS_3_825_TX_3_validation</th><th scope=col>FOODS_3_826_TX_3_validation</th><th scope=col>FOODS_3_827_TX_3_validation</th><th scope=col>total</th><th scope=col>is_event</th></tr>\n",
       "\t<tr><th scope=col>&lt;int&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>⋯</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&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> 331</td><td>12/25/2011</td><td>11148</td><td>Sunday   </td><td>2</td><td>12</td><td>2011</td><td>Christmas</td><td>National</td><td>NA</td><td>⋯</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>2</td><td>1</td></tr>\n",
       "\t<tr><td> 697</td><td>12/25/2012</td><td>11248</td><td>Tuesday  </td><td>4</td><td>12</td><td>2012</td><td>Christmas</td><td>National</td><td>NA</td><td>⋯</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>2</td><td>1</td></tr>\n",
       "\t<tr><td>1062</td><td>12/25/2013</td><td>11348</td><td>Wednesday</td><td>5</td><td>12</td><td>2013</td><td>Christmas</td><td>National</td><td>NA</td><td>⋯</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>1</td><td>1</td></tr>\n",
       "\t<tr><td>1427</td><td>12/25/2014</td><td>11447</td><td>Thursday </td><td>6</td><td>12</td><td>2014</td><td>Christmas</td><td>National</td><td>NA</td><td>⋯</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>4</td><td>1</td></tr>\n",
       "\t<tr><td>1792</td><td>12/25/2015</td><td>11547</td><td>Friday   </td><td>7</td><td>12</td><td>2015</td><td>Christmas</td><td>National</td><td>NA</td><td>⋯</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>2</td><td>1</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A tbl\\_ts: 5 × 837\n",
       "\\begin{tabular}{lllllllllllllllllllll}\n",
       " day & date & wm\\_yr\\_wk & weekday & wday & month & year & event\\_name\\_1 & event\\_type\\_1 & event\\_name\\_2 & ⋯ & FOODS\\_3\\_820\\_TX\\_3\\_validation & FOODS\\_3\\_821\\_TX\\_3\\_validation & FOODS\\_3\\_822\\_TX\\_3\\_validation & FOODS\\_3\\_823\\_TX\\_3\\_validation & FOODS\\_3\\_824\\_TX\\_3\\_validation & FOODS\\_3\\_825\\_TX\\_3\\_validation & FOODS\\_3\\_826\\_TX\\_3\\_validation & FOODS\\_3\\_827\\_TX\\_3\\_validation & total & is\\_event\\\\\n",
       " <int> & <chr> & <int> & <chr> & <int> & <int> & <int> & <chr> & <chr> & <chr> & ⋯ & <int> & <int> & <int> & <int> & <int> & <int> & <int> & <int> & <dbl> & <dbl>\\\\\n",
       "\\hline\n",
       "\t  331 & 12/25/2011 & 11148 & Sunday    & 2 & 12 & 2011 & Christmas & National & NA & ⋯ & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 2 & 1\\\\\n",
       "\t  697 & 12/25/2012 & 11248 & Tuesday   & 4 & 12 & 2012 & Christmas & National & NA & ⋯ & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 2 & 1\\\\\n",
       "\t 1062 & 12/25/2013 & 11348 & Wednesday & 5 & 12 & 2013 & Christmas & National & NA & ⋯ & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 1 & 1\\\\\n",
       "\t 1427 & 12/25/2014 & 11447 & Thursday  & 6 & 12 & 2014 & Christmas & National & NA & ⋯ & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 4 & 1\\\\\n",
       "\t 1792 & 12/25/2015 & 11547 & Friday    & 7 & 12 & 2015 & Christmas & National & NA & ⋯ & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 2 & 1\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A tbl_ts: 5 × 837\n",
       "\n",
       "| day &lt;int&gt; | date &lt;chr&gt; | wm_yr_wk &lt;int&gt; | weekday &lt;chr&gt; | wday &lt;int&gt; | month &lt;int&gt; | year &lt;int&gt; | event_name_1 &lt;chr&gt; | event_type_1 &lt;chr&gt; | event_name_2 &lt;chr&gt; | ⋯ ⋯ | FOODS_3_820_TX_3_validation &lt;int&gt; | FOODS_3_821_TX_3_validation &lt;int&gt; | FOODS_3_822_TX_3_validation &lt;int&gt; | FOODS_3_823_TX_3_validation &lt;int&gt; | FOODS_3_824_TX_3_validation &lt;int&gt; | FOODS_3_825_TX_3_validation &lt;int&gt; | FOODS_3_826_TX_3_validation &lt;int&gt; | FOODS_3_827_TX_3_validation &lt;int&gt; | total &lt;dbl&gt; | is_event &lt;dbl&gt; |\n",
       "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
       "|  331 | 12/25/2011 | 11148 | Sunday    | 2 | 12 | 2011 | Christmas | National | NA | ⋯ | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 1 |\n",
       "|  697 | 12/25/2012 | 11248 | Tuesday   | 4 | 12 | 2012 | Christmas | National | NA | ⋯ | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 1 |\n",
       "| 1062 | 12/25/2013 | 11348 | Wednesday | 5 | 12 | 2013 | Christmas | National | NA | ⋯ | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 |\n",
       "| 1427 | 12/25/2014 | 11447 | Thursday  | 6 | 12 | 2014 | Christmas | National | NA | ⋯ | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 4 | 1 |\n",
       "| 1792 | 12/25/2015 | 11547 | Friday    | 7 | 12 | 2015 | Christmas | National | NA | ⋯ | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 1 |\n",
       "\n"
      ],
      "text/plain": [
       "  day  date       wm_yr_wk weekday   wday month year event_name_1 event_type_1\n",
       "1  331 12/25/2011 11148    Sunday    2    12    2011 Christmas    National    \n",
       "2  697 12/25/2012 11248    Tuesday   4    12    2012 Christmas    National    \n",
       "3 1062 12/25/2013 11348    Wednesday 5    12    2013 Christmas    National    \n",
       "4 1427 12/25/2014 11447    Thursday  6    12    2014 Christmas    National    \n",
       "5 1792 12/25/2015 11547    Friday    7    12    2015 Christmas    National    \n",
       "  event_name_2 ⋯ FOODS_3_820_TX_3_validation FOODS_3_821_TX_3_validation\n",
       "1 NA           ⋯ 0                           0                          \n",
       "2 NA           ⋯ 0                           0                          \n",
       "3 NA           ⋯ 0                           0                          \n",
       "4 NA           ⋯ 0                           0                          \n",
       "5 NA           ⋯ 0                           0                          \n",
       "  FOODS_3_822_TX_3_validation FOODS_3_823_TX_3_validation\n",
       "1 0                           0                          \n",
       "2 0                           0                          \n",
       "3 0                           0                          \n",
       "4 0                           0                          \n",
       "5 0                           0                          \n",
       "  FOODS_3_824_TX_3_validation FOODS_3_825_TX_3_validation\n",
       "1 0                           0                          \n",
       "2 0                           0                          \n",
       "3 0                           0                          \n",
       "4 0                           0                          \n",
       "5 0                           0                          \n",
       "  FOODS_3_826_TX_3_validation FOODS_3_827_TX_3_validation total is_event\n",
       "1 0                           0                           2     1       \n",
       "2 0                           0                           2     1       \n",
       "3 0                           0                           1     1       \n",
       "4 0                           0                           4     1       \n",
       "5 0                           0                           2     1       "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A tbl_ts: 1 × 837</caption>\n",
       "<thead>\n",
       "\t<tr><th scope=col>day</th><th scope=col>date</th><th scope=col>wm_yr_wk</th><th scope=col>weekday</th><th scope=col>wday</th><th scope=col>month</th><th scope=col>year</th><th scope=col>event_name_1</th><th scope=col>event_type_1</th><th scope=col>event_name_2</th><th scope=col>⋯</th><th scope=col>FOODS_3_820_TX_3_validation</th><th scope=col>FOODS_3_821_TX_3_validation</th><th scope=col>FOODS_3_822_TX_3_validation</th><th scope=col>FOODS_3_823_TX_3_validation</th><th scope=col>FOODS_3_824_TX_3_validation</th><th scope=col>FOODS_3_825_TX_3_validation</th><th scope=col>FOODS_3_826_TX_3_validation</th><th scope=col>FOODS_3_827_TX_3_validation</th><th scope=col>total</th><th scope=col>is_event</th></tr>\n",
       "\t<tr><th scope=col>&lt;int&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>⋯</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&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>1599</td><td>6/15/2015</td><td>11520</td><td>Monday</td><td>3</td><td>6</td><td>2015</td><td>NA</td><td>NA</td><td>NA</td><td>⋯</td><td>1</td><td>1</td><td>4</td><td>0</td><td>0</td><td>0</td><td>2</td><td>0</td><td>4027</td><td>NA</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A tbl\\_ts: 1 × 837\n",
       "\\begin{tabular}{lllllllllllllllllllll}\n",
       " day & date & wm\\_yr\\_wk & weekday & wday & month & year & event\\_name\\_1 & event\\_type\\_1 & event\\_name\\_2 & ⋯ & FOODS\\_3\\_820\\_TX\\_3\\_validation & FOODS\\_3\\_821\\_TX\\_3\\_validation & FOODS\\_3\\_822\\_TX\\_3\\_validation & FOODS\\_3\\_823\\_TX\\_3\\_validation & FOODS\\_3\\_824\\_TX\\_3\\_validation & FOODS\\_3\\_825\\_TX\\_3\\_validation & FOODS\\_3\\_826\\_TX\\_3\\_validation & FOODS\\_3\\_827\\_TX\\_3\\_validation & total & is\\_event\\\\\n",
       " <int> & <chr> & <int> & <chr> & <int> & <int> & <int> & <chr> & <chr> & <chr> & ⋯ & <int> & <int> & <int> & <int> & <int> & <int> & <int> & <int> & <dbl> & <dbl>\\\\\n",
       "\\hline\n",
       "\t 1599 & 6/15/2015 & 11520 & Monday & 3 & 6 & 2015 & NA & NA & NA & ⋯ & 1 & 1 & 4 & 0 & 0 & 0 & 2 & 0 & 4027 & NA\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A tbl_ts: 1 × 837\n",
       "\n",
       "| day &lt;int&gt; | date &lt;chr&gt; | wm_yr_wk &lt;int&gt; | weekday &lt;chr&gt; | wday &lt;int&gt; | month &lt;int&gt; | year &lt;int&gt; | event_name_1 &lt;chr&gt; | event_type_1 &lt;chr&gt; | event_name_2 &lt;chr&gt; | ⋯ ⋯ | FOODS_3_820_TX_3_validation &lt;int&gt; | FOODS_3_821_TX_3_validation &lt;int&gt; | FOODS_3_822_TX_3_validation &lt;int&gt; | FOODS_3_823_TX_3_validation &lt;int&gt; | FOODS_3_824_TX_3_validation &lt;int&gt; | FOODS_3_825_TX_3_validation &lt;int&gt; | FOODS_3_826_TX_3_validation &lt;int&gt; | FOODS_3_827_TX_3_validation &lt;int&gt; | total &lt;dbl&gt; | is_event &lt;dbl&gt; |\n",
       "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
       "| 1599 | 6/15/2015 | 11520 | Monday | 3 | 6 | 2015 | NA | NA | NA | ⋯ | 1 | 1 | 4 | 0 | 0 | 0 | 2 | 0 | 4027 | NA |\n",
       "\n"
      ],
      "text/plain": [
       "  day  date      wm_yr_wk weekday wday month year event_name_1 event_type_1\n",
       "1 1599 6/15/2015 11520    Monday  3    6     2015 NA           NA          \n",
       "  event_name_2 ⋯ FOODS_3_820_TX_3_validation FOODS_3_821_TX_3_validation\n",
       "1 NA           ⋯ 1                           1                          \n",
       "  FOODS_3_822_TX_3_validation FOODS_3_823_TX_3_validation\n",
       "1 4                           0                          \n",
       "  FOODS_3_824_TX_3_validation FOODS_3_825_TX_3_validation\n",
       "1 0                           0                          \n",
       "  FOODS_3_826_TX_3_validation FOODS_3_827_TX_3_validation total is_event\n",
       "1 2                           0                           4027  NA      "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Probably closed on christmas, sales is are aprox 0.\n",
    "# Positive outlier on jun 15 2015, 4027 sales for some reason\n",
    "\n",
    "calendar_sales[calendar_sales$total <=5, ]\n",
    "calendar_sales[calendar_sales$total >=3500, ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 289,
   "id": "cc7f30fc-f2ff-438f-840c-a72baeb21b30",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "calendar2 <- calendar \n",
    "calendar2$name <- 1:nrow(calendar)\n",
    "sales <- sales_train %>% pivot_longer (2:ncol(sales_train)) \n",
    "sales$name <- gsub(\"d_\", \"\", sales$name)\n",
    "sales$id <- gsub(\"_TX_3_validation\", \"\" ,sales$id)\n",
    "sales$name <- as.integer(sales$name)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 290,
   "id": "99bab811-a455-4c3a-b5b3-02bc65c80870",
   "metadata": {},
   "outputs": [],
   "source": [
    "calendar2_sales <- left_join(sales,calendar2, by='name') \n",
    "calendar2_sales$date <- as.Date(calendar2_sales$date, format = \"%m/%d/%Y\")\n",
    "calendar2_sales <- calendar2_sales %>% as_tsibble(index = date , key = id)\n",
    "calendar2_sales <- left_join(calendar2_sales, prices, by = c(\"wm_yr_wk\" = \"wm_yr_wk\", \"id\"=\"item_id\"))\n",
    "#calendar2_sales$is_event <- ifelse(calendar2_sales$event_name_1 != \"NA\", 1, 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 291,
   "id": "478715e1-9f8c-40fe-8dcd-1cd7da9b6d0e",
   "metadata": {},
   "outputs": [],
   "source": [
    "weekdays <- list('Monday', 'Tuesday', 'Wednesday', 'Thursday')\n",
    "weekend <- list('Friday','Saturday')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 292,
   "id": "3a6b03e3-f667-435b-896f-a504984c89ce",
   "metadata": {},
   "outputs": [],
   "source": [
    "calendar2_sales$is_weekend <- ifelse(calendar2_sales$weekday %in% weekend, 1, 0)\n",
    "calendar2_sales$event_name_1 <- replace(calendar2_sales$event_name_1, is.na(calendar2_sales$event_name_1), 0)\n",
    "calendar2_sales$is_event <- ifelse(calendar2_sales$event_name_1 != 0, 1, 0)\n",
    "\n",
    "final_data <- calendar2_sales[c(\"id\",\"name\",\"value\",'date','wday','wm_yr_wk','weekday','month', 'year','event_name_1','event_type_1','snap_TX','sell_price','is_event','is_weekend')]\n",
    "final_data_numeric <- final_data[c('id','name','value','month','sell_price','is_event','is_weekend')]\n",
    "\n",
    "final_data_numeric_2 <- final_data[c('id','name','value','month','sell_price','is_event','is_weekend')]\n",
    "final_data_numeric_2$id <- gsub(\"FOODS_3_\", \"\" ,final_data_numeric_2$id)\n",
    "\n",
    "final_data_numeric_3 <- final_data[c('value','wday','month','sell_price','is_event','is_weekend', 'snap_TX')]\n",
    "final_data_numeric_4 <- final_data[c('value','wday','month','sell_price','is_event','is_weekend', 'snap_TX')]\n",
    "names(final_data_numeric_4)[names(final_data_numeric_4) == 'value'] <- 'Amount sold'\n",
    "\n",
    "final_data_numeric$value <- as.double(final_data_numeric$value)\n",
    "final_data_numeric$name <- as.double(final_data_numeric$name)\n",
    "final_data_numeric$month <- as.double(final_data_numeric$month)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 294,
   "id": "84662a80-f581-4ce2-9e2c-063b3f0d45c1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A matrix: 7 × 7 of type dbl</caption>\n",
       "<thead>\n",
       "\t<tr><th></th><th scope=col>Amount sold</th><th scope=col>wday</th><th scope=col>month</th><th scope=col>sell_price</th><th scope=col>is_event</th><th scope=col>is_weekend</th><th scope=col>snap_TX</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>Amount sold</th><td> 1.00</td><td>-0.03</td><td> 0.00</td><td>-0.13</td><td> 0.00</td><td> 0.03</td><td>0.02</td></tr>\n",
       "\t<tr><th scope=row>wday</th><td>-0.03</td><td> 1.00</td><td> 0.00</td><td> 0.00</td><td>-0.04</td><td>-0.29</td><td>0.00</td></tr>\n",
       "\t<tr><th scope=row>month</th><td> 0.00</td><td> 0.00</td><td> 1.00</td><td> 0.00</td><td>-0.07</td><td> 0.00</td><td>0.00</td></tr>\n",
       "\t<tr><th scope=row>sell_price</th><td>-0.13</td><td> 0.00</td><td> 0.00</td><td> 1.00</td><td> 0.00</td><td> 0.00</td><td>0.00</td></tr>\n",
       "\t<tr><th scope=row>is_event</th><td> 0.00</td><td>-0.04</td><td>-0.07</td><td> 0.00</td><td> 1.00</td><td>-0.04</td><td>0.01</td></tr>\n",
       "\t<tr><th scope=row>is_weekend</th><td> 0.03</td><td>-0.29</td><td> 0.00</td><td> 0.00</td><td>-0.04</td><td> 1.00</td><td>0.01</td></tr>\n",
       "\t<tr><th scope=row>snap_TX</th><td> 0.02</td><td> 0.00</td><td> 0.00</td><td> 0.00</td><td> 0.01</td><td> 0.01</td><td>1.00</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A matrix: 7 × 7 of type dbl\n",
       "\\begin{tabular}{r|lllllll}\n",
       "  & Amount sold & wday & month & sell\\_price & is\\_event & is\\_weekend & snap\\_TX\\\\\n",
       "\\hline\n",
       "\tAmount sold &  1.00 & -0.03 &  0.00 & -0.13 &  0.00 &  0.03 & 0.02\\\\\n",
       "\twday & -0.03 &  1.00 &  0.00 &  0.00 & -0.04 & -0.29 & 0.00\\\\\n",
       "\tmonth &  0.00 &  0.00 &  1.00 &  0.00 & -0.07 &  0.00 & 0.00\\\\\n",
       "\tsell\\_price & -0.13 &  0.00 &  0.00 &  1.00 &  0.00 &  0.00 & 0.00\\\\\n",
       "\tis\\_event &  0.00 & -0.04 & -0.07 &  0.00 &  1.00 & -0.04 & 0.01\\\\\n",
       "\tis\\_weekend &  0.03 & -0.29 &  0.00 &  0.00 & -0.04 &  1.00 & 0.01\\\\\n",
       "\tsnap\\_TX &  0.02 &  0.00 &  0.00 &  0.00 &  0.01 &  0.01 & 1.00\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A matrix: 7 × 7 of type dbl\n",
       "\n",
       "| <!--/--> | Amount sold | wday | month | sell_price | is_event | is_weekend | snap_TX |\n",
       "|---|---|---|---|---|---|---|---|\n",
       "| Amount sold |  1.00 | -0.03 |  0.00 | -0.13 |  0.00 |  0.03 | 0.02 |\n",
       "| wday | -0.03 |  1.00 |  0.00 |  0.00 | -0.04 | -0.29 | 0.00 |\n",
       "| month |  0.00 |  0.00 |  1.00 |  0.00 | -0.07 |  0.00 | 0.00 |\n",
       "| sell_price | -0.13 |  0.00 |  0.00 |  1.00 |  0.00 |  0.00 | 0.00 |\n",
       "| is_event |  0.00 | -0.04 | -0.07 |  0.00 |  1.00 | -0.04 | 0.01 |\n",
       "| is_weekend |  0.03 | -0.29 |  0.00 |  0.00 | -0.04 |  1.00 | 0.01 |\n",
       "| snap_TX |  0.02 |  0.00 |  0.00 |  0.00 |  0.01 |  0.01 | 1.00 |\n",
       "\n"
      ],
      "text/plain": [
       "            Amount sold wday  month sell_price is_event is_weekend snap_TX\n",
       "Amount sold  1.00       -0.03  0.00 -0.13       0.00     0.03      0.02   \n",
       "wday        -0.03        1.00  0.00  0.00      -0.04    -0.29      0.00   \n",
       "month        0.00        0.00  1.00  0.00      -0.07     0.00      0.00   \n",
       "sell_price  -0.13        0.00  0.00  1.00       0.00     0.00      0.00   \n",
       "is_event     0.00       -0.04 -0.07  0.00       1.00    -0.04      0.01   \n",
       "is_weekend   0.03       -0.29  0.00  0.00      -0.04     1.00      0.01   \n",
       "snap_TX      0.02        0.00  0.00  0.00       0.01     0.01      1.00   "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cormat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 298,
   "id": "8578c802-d032-4d87-bbb0-ca7213e73c08",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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/thqnC/7FY3m7PdDVfdpm/7F8I9CeHepppd4Q6EEu5AnKvu8qHKNw+X/StQL7YH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\ngP8ffPP9LQgooOoAAAAASUVORK5CYII=",
      "text/plain": [
       "Plot with title \"\""
      ]
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 1500
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "cormat <- round(cor(final_data_numeric_4, use = \"pairwise.complete.obs\"),2)\n",
    "corrplot(cormat, method = \"color\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 397,
   "id": "aa90f44a-a017-4c0f-a701-7fb11cbefe4f",
   "metadata": {},
   "outputs": [],
   "source": [
    "x <-as_tsibble(final_data_numeric, index=\"name\", key=\"id\" )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 398,
   "id": "0755aeaf-3a1c-4ac2-a992-67afb7da53a9",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Warning message in sqrt(diag(best$var.coef)):\n",
      "\"NaNs produced\"\n",
      "Warning message in sqrt(diag(best$var.coef)):\n",
      "\"NaNs produced\"\n",
      "Warning message in sqrt(diag(best$var.coef)):\n",
      "\"NaNs produced\"\n",
      "Warning message in sqrt(diag(best$var.coef)):\n",
      "\"NaNs produced\"\n",
      "Warning message in sqrt(diag(best$var.coef)):\n",
      "\"NaNs produced\"\n",
      "Warning message in sqrt(diag(best$var.coef)):\n",
      "\"NaNs produced\"\n",
      "Warning message in sqrt(diag(best$var.coef)):\n",
      "\"NaNs produced\"\n",
      "Warning message in sqrt(diag(best$var.coef)):\n",
      "\"NaNs produced\"\n",
      "Warning message in sqrt(diag(best$var.coef)):\n",
      "\"NaNs produced\"\n",
      "Warning message in sqrt(diag(best$var.coef)):\n",
      "\"NaNs produced\"\n",
      "Warning message in sqrt(diag(best$var.coef)):\n",
      "\"NaNs produced\"\n",
      "Warning message in sqrt(diag(best$var.coef)):\n",
      "\"NaNs produced\"\n",
      "Warning message in sqrt(diag(best$var.coef)):\n",
      "\"NaNs produced\"\n",
      "Warning message in sqrt(diag(best$var.coef)):\n",
      "\"NaNs produced\"\n",
      "Warning message in sqrt(diag(best$var.coef)):\n",
      "\"NaNs produced\"\n",
      "Warning message in sqrt(diag(best$var.coef)):\n",
      "\"NaNs produced\"\n",
      "Warning message in sqrt(diag(best$var.coef)):\n",
      "\"NaNs produced\"\n",
      "Warning message in sqrt(diag(best$var.coef)):\n",
      "\"NaNs produced\"\n"
     ]
    }
   ],
   "source": [
    "arima <- x %>% model(ARIMA(value)) %>% forecast(h=28)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 461,
   "id": "6f490ef9-0030-42cc-81a5-afb8fad6fbf4",
   "metadata": {},
   "outputs": [],
   "source": [
    "eval_long$name <- (eval_long$name-28)\n",
    "eval_long <- as_tsibble(eval_long, index=\"name\", key=\"id\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 426,
   "id": "8df183f7-024b-4a46-aacf-24ea719a57e7",
   "metadata": {},
   "outputs": [],
   "source": [
    "arima_rounded <- arima\n",
    "arima_rounded$.mean <- ceiling(arima_rounded$.mean)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 363,
   "id": "359ec805-ac5c-4e6e-a689-2aa97e4d46bc",
   "metadata": {},
   "outputs": [],
   "source": [
    "sales_val_long <- sales_val %>% pivot_longer (2:ncol(sales_val))\n",
    "sales_val_long$name <- gsub (\"d_\", \"\" ,sales_val_long$name)\n",
    "sales_val_long$id <- gsub (\"_TX_3_validation\", \"\" ,sales_val_long$id)\n",
    "sales_val_long$name <- as.integer(sales_val_long$name)\n",
    "sales_val_long <-as_tsibble(sales_val_long, index=\"name\", key=\"id\" )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 462,
   "id": "5e96addf-cf4f-4ed5-b75e-c0e4b6071a83",
   "metadata": {},
   "outputs": [],
   "source": [
    "eval_long <- evaluation %>% pivot_longer (2:ncol(evaluation))\n",
    "eval_long$name <- gsub (\"d_\", \"\" ,eval_long$name)\n",
    "eval_long$id <- gsub (\"_TX_3_validation\", \"\" ,eval_long$id)\n",
    "eval_long$name <- as.integer(eval_long$name)\n",
    "eval_long <-as_tsibble(eval_long, index=\"name\", key=\"id\" )\n",
    "eval_long$name <- (eval_long$name-28)\n",
    "eval_long <- as_tsibble(eval_long, index=\"name\", key=\"id\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "d6c3ccb7-4e7d-41a8-b8e3-ea1c1a26eba2",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Validation set results\n",
    "arima_results <- summary(accuracy(arima,sales_val_long) %>% select(ME:MAE))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 415,
   "id": "8abc3b12-3f5e-4a0a-b5e2-c3ef8d400df3",
   "metadata": {},
   "outputs": [],
   "source": [
    "arima_rmse <- summary(accuracy(arima_rounded, eval_long) %>% select(ME:MAE))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 416,
   "id": "70ee0e83-6734-4fac-b9ce-f321022d1d70",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "       ME                 RMSE              MAE          \n",
       " Min.   :-11.90197   Min.   : 0.1822   Min.   : 0.03571  \n",
       " 1st Qu.: -0.22039   1st Qu.: 0.6572   1st Qu.: 0.54037  \n",
       " Median :  0.01877   Median : 1.1948   Median : 0.91017  \n",
       " Mean   :  0.28704   Mean   : 2.1621   Mean   : 1.66061  \n",
       " 3rd Qu.:  0.38596   3rd Qu.: 1.9853   3rd Qu.: 1.59565  \n",
       " Max.   : 23.32143   Max.   :27.2464   Max.   :23.32143  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "arima_rmse"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 452,
   "id": "656f9758-bfc9-4acd-ab33-94b244254e5d",
   "metadata": {},
   "outputs": [],
   "source": [
    "arima_rounded2 <- as_tsibble(arima_rounded2, index=\"name\", key=\"id\" )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 460,
   "id": "697315cb-15fb-4e54-ba9e-7887774a06b2",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Warning message:\n",
      "\"The future dataset is incomplete, incomplete out-of-sample data will be treated as missing. \n",
      "28 observations are missing between 1914 and 1941\"\n"
     ]
    }
   ],
   "source": [
    "arima_rounded_results <- summary(accuracy(arima,eval_long) %>% select(ME:MAE))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 330,
   "id": "265d080f-c843-4a43-b621-0734aef050f5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "       ME                RMSE             MAE         \n",
       " Min.   :-5.56427   Min.   : 0.000   Min.   : 0.0000  \n",
       " 1st Qu.:-0.09409   1st Qu.: 0.672   1st Qu.: 0.5326  \n",
       " Median : 0.07435   Median : 1.134   Median : 0.8709  \n",
       " Mean   : 0.26161   Mean   : 1.827   Mean   : 1.3997  \n",
       " 3rd Qu.: 0.34871   3rd Qu.: 1.957   3rd Qu.: 1.4982  \n",
       " Max.   :18.10714   Max.   :25.588   Max.   :19.6274  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "arima_rounded_results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 419,
   "id": "068e46d7-fd64-4168-9dea-ceb9fefa0b30",
   "metadata": {},
   "outputs": [],
   "source": [
    "ETS <- to_forecast %>% \n",
    "model(ETS(value)) %>% forecast(h=28)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 421,
   "id": "3bb7bf84-7f66-463c-8727-ed2fbdc08d94",
   "metadata": {},
   "outputs": [],
   "source": [
    "ETS_rounded <- ETS\n",
    "ETS_rounded$.mean <- ceiling(ETS_rounded$.mean)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 464,
   "id": "6375f0ac-20bb-455a-aca2-e4ab3229d578",
   "metadata": {},
   "outputs": [],
   "source": [
    "ETS_flat_results <- summary(accuracy(ETS_rounded,eval_long) %>% select(ME:MAE))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 467,
   "id": "0f3427d5-ad4e-4d35-9f62-9d2c0665cdf4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "       ME                 RMSE              MAE          \n",
       " Min.   :-23.09684   Min.   : 0.1859   Min.   : 0.05911  \n",
       " 1st Qu.: -0.21813   1st Qu.: 0.6552   1st Qu.: 0.54031  \n",
       " Median :  0.01052   Median : 1.1940   Median : 0.90828  \n",
       " Mean   :  0.22551   Mean   : 2.2271   Mean   : 1.73080  \n",
       " 3rd Qu.:  0.35749   3rd Qu.: 2.0116   3rd Qu.: 1.61768  \n",
       " Max.   : 23.32143   Max.   :25.6480   Max.   :23.32143  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ETS_flat_results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 474,
   "id": "38f8d5e2-a082-48cd-b195-2baccbb05aec",
   "metadata": {},
   "outputs": [],
   "source": [
    "FOODS_3_001 <-arima_rounded %>% filter(id == \"FOODS_3_827\") \n",
    "FOODS_3_001_ets <- ETS_rounded %>% filter(id == \"FOODS_3_827\") \n",
    "FOODS_3_001_truth <- eval_long %>% filter(id == \"FOODS_3_827\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 476,
   "id": "d6731689-7009-4b05-90b2-c0dd33cb4001",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<strong>pdf:</strong> 2"
      ],
      "text/latex": [
       "\\textbf{pdf:} 2"
      ],
      "text/markdown": [
       "**pdf:** 2"
      ],
      "text/plain": [
       "pdf \n",
       "  2 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "png(filename=\"final_pic.png\")\n",
    "\n",
    "plot(FOODS_3_001_truth$name,                           \n",
    "     FOODS_3_001_truth$value,\n",
    "     type = \"l\",\n",
    "     col = 2,\n",
    "     ylim = c(0, 12),\n",
    "     xlab = \"Day count\",\n",
    "     ylab = \"Values\")\n",
    " \n",
    "# Add line graphs of other two dataset\n",
    "lines(FOODS_3_001$name,                             \n",
    "      FOODS_3_001$.mean,\n",
    "      type = \"l\",\n",
    "      col = 3)\n",
    " \n",
    "lines(FOODS_3_001_ets$name,                             \n",
    "      FOODS_3_001_ets$.mean,\n",
    "      type = \"l\",\n",
    "      col = 4)\n",
    " \n",
    "# Add legend in top right corner\n",
    "legend(\"topright\",                           \n",
    "       c(\"Ground Truth\", \"ARIMA\", \"ETS\"),\n",
    "       lty = 1,\n",
    "       col = 2:4)\n",
    "\n",
    "dev.off()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 471,
   "id": "dc80bd61-abcb-4e92-b140-6f62e63fd36d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A tbl_ts: 28 × 6</caption>\n",
       "<thead>\n",
       "\t<tr><th scope=col>id</th><th scope=col>.model</th><th scope=col>name</th><th scope=col>value</th><th scope=col>.mean</th><th scope=col>index</th></tr>\n",
       "\t<tr><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dist&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>FOODS_3_827</td><td>ARIMA(value)</td><td>1942</td><td>N(1.2, 1.3)</td><td>2</td><td>1942</td></tr>\n",
       "\t<tr><td>FOODS_3_827</td><td>ARIMA(value)</td><td>1943</td><td>N(0.89, 1.4)</td><td>1</td><td>1943</td></tr>\n",
       "\t<tr><td>FOODS_3_827</td><td>ARIMA(value)</td><td>1944</td><td>N(0.96, 1.4)</td><td>1</td><td>1944</td></tr>\n",
       "\t<tr><td>FOODS_3_827</td><td>ARIMA(value)</td><td>1945</td><td>N(0.96, 1.4)</td><td>1</td><td>1945</td></tr>\n",
       "\t<tr><td>FOODS_3_827</td><td>ARIMA(value)</td><td>1946</td><td>N(0.96, 1.4)</td><td>1</td><td>1946</td></tr>\n",
       "\t<tr><td>FOODS_3_827</td><td>ARIMA(value)</td><td>1947</td><td>N(0.96, 1.4)</td><td>1</td><td>1947</td></tr>\n",
       "\t<tr><td>FOODS_3_827</td><td>ARIMA(value)</td><td>1948</td><td>N(0.96, 1.4)</td><td>1</td><td>1948</td></tr>\n",
       "\t<tr><td>FOODS_3_827</td><td>ARIMA(value)</td><td>1949</td><td>N(0.96, 1.4)</td><td>1</td><td>1949</td></tr>\n",
       "\t<tr><td>FOODS_3_827</td><td>ARIMA(value)</td><td>1950</td><td>N(0.96, 1.4)</td><td>1</td><td>1950</td></tr>\n",
       "\t<tr><td>FOODS_3_827</td><td>ARIMA(value)</td><td>1951</td><td>N(0.96, 1.4)</td><td>1</td><td>1951</td></tr>\n",
       "\t<tr><td>FOODS_3_827</td><td>ARIMA(value)</td><td>1952</td><td>N(0.96, 1.4)</td><td>1</td><td>1952</td></tr>\n",
       "\t<tr><td>FOODS_3_827</td><td>ARIMA(value)</td><td>1953</td><td>N(0.96, 1.4)</td><td>1</td><td>1953</td></tr>\n",
       "\t<tr><td>FOODS_3_827</td><td>ARIMA(value)</td><td>1954</td><td>N(0.96, 1.4)</td><td>1</td><td>1954</td></tr>\n",
       "\t<tr><td>FOODS_3_827</td><td>ARIMA(value)</td><td>1955</td><td>N(0.96, 1.4)</td><td>1</td><td>1955</td></tr>\n",
       "\t<tr><td>FOODS_3_827</td><td>ARIMA(value)</td><td>1956</td><td>N(0.96, 1.4)</td><td>1</td><td>1956</td></tr>\n",
       "\t<tr><td>FOODS_3_827</td><td>ARIMA(value)</td><td>1957</td><td>N(0.96, 1.4)</td><td>1</td><td>1957</td></tr>\n",
       "\t<tr><td>FOODS_3_827</td><td>ARIMA(value)</td><td>1958</td><td>N(0.96, 1.4)</td><td>1</td><td>1958</td></tr>\n",
       "\t<tr><td>FOODS_3_827</td><td>ARIMA(value)</td><td>1959</td><td>N(0.96, 1.4)</td><td>1</td><td>1959</td></tr>\n",
       "\t<tr><td>FOODS_3_827</td><td>ARIMA(value)</td><td>1960</td><td>N(0.96, 1.4)</td><td>1</td><td>1960</td></tr>\n",
       "\t<tr><td>FOODS_3_827</td><td>ARIMA(value)</td><td>1961</td><td>N(0.96, 1.4)</td><td>1</td><td>1961</td></tr>\n",
       "\t<tr><td>FOODS_3_827</td><td>ARIMA(value)</td><td>1962</td><td>N(0.96, 1.4)</td><td>1</td><td>1962</td></tr>\n",
       "\t<tr><td>FOODS_3_827</td><td>ARIMA(value)</td><td>1963</td><td>N(0.96, 1.4)</td><td>1</td><td>1963</td></tr>\n",
       "\t<tr><td>FOODS_3_827</td><td>ARIMA(value)</td><td>1964</td><td>N(0.96, 1.4)</td><td>1</td><td>1964</td></tr>\n",
       "\t<tr><td>FOODS_3_827</td><td>ARIMA(value)</td><td>1965</td><td>N(0.96, 1.4)</td><td>1</td><td>1965</td></tr>\n",
       "\t<tr><td>FOODS_3_827</td><td>ARIMA(value)</td><td>1966</td><td>N(0.96, 1.4)</td><td>1</td><td>1966</td></tr>\n",
       "\t<tr><td>FOODS_3_827</td><td>ARIMA(value)</td><td>1967</td><td>N(0.96, 1.4)</td><td>1</td><td>1967</td></tr>\n",
       "\t<tr><td>FOODS_3_827</td><td>ARIMA(value)</td><td>1968</td><td>N(0.96, 1.4)</td><td>1</td><td>1968</td></tr>\n",
       "\t<tr><td>FOODS_3_827</td><td>ARIMA(value)</td><td>1969</td><td>N(0.96, 1.4)</td><td>1</td><td>1969</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A tbl\\_ts: 28 × 6\n",
       "\\begin{tabular}{llllll}\n",
       " id & .model & name & value & .mean & index\\\\\n",
       " <chr> & <chr> & <dbl> & <dist> & <dbl> & <dbl>\\\\\n",
       "\\hline\n",
       "\t FOODS\\_3\\_827 & ARIMA(value) & 1942 & N(1.2, 1.3) & 2 & 1942\\\\\n",
       "\t FOODS\\_3\\_827 & ARIMA(value) & 1943 & N(0.89, 1.4) & 1 & 1943\\\\\n",
       "\t FOODS\\_3\\_827 & ARIMA(value) & 1944 & N(0.96, 1.4) & 1 & 1944\\\\\n",
       "\t FOODS\\_3\\_827 & ARIMA(value) & 1945 & N(0.96, 1.4) & 1 & 1945\\\\\n",
       "\t FOODS\\_3\\_827 & ARIMA(value) & 1946 & N(0.96, 1.4) & 1 & 1946\\\\\n",
       "\t FOODS\\_3\\_827 & ARIMA(value) & 1947 & N(0.96, 1.4) & 1 & 1947\\\\\n",
       "\t FOODS\\_3\\_827 & ARIMA(value) & 1948 & N(0.96, 1.4) & 1 & 1948\\\\\n",
       "\t FOODS\\_3\\_827 & ARIMA(value) & 1949 & N(0.96, 1.4) & 1 & 1949\\\\\n",
       "\t FOODS\\_3\\_827 & ARIMA(value) & 1950 & N(0.96, 1.4) & 1 & 1950\\\\\n",
       "\t FOODS\\_3\\_827 & ARIMA(value) & 1951 & N(0.96, 1.4) & 1 & 1951\\\\\n",
       "\t FOODS\\_3\\_827 & ARIMA(value) & 1952 & N(0.96, 1.4) & 1 & 1952\\\\\n",
       "\t FOODS\\_3\\_827 & ARIMA(value) & 1953 & N(0.96, 1.4) & 1 & 1953\\\\\n",
       "\t FOODS\\_3\\_827 & ARIMA(value) & 1954 & N(0.96, 1.4) & 1 & 1954\\\\\n",
       "\t FOODS\\_3\\_827 & ARIMA(value) & 1955 & N(0.96, 1.4) & 1 & 1955\\\\\n",
       "\t FOODS\\_3\\_827 & ARIMA(value) & 1956 & N(0.96, 1.4) & 1 & 1956\\\\\n",
       "\t FOODS\\_3\\_827 & ARIMA(value) & 1957 & N(0.96, 1.4) & 1 & 1957\\\\\n",
       "\t FOODS\\_3\\_827 & ARIMA(value) & 1958 & N(0.96, 1.4) & 1 & 1958\\\\\n",
       "\t FOODS\\_3\\_827 & ARIMA(value) & 1959 & N(0.96, 1.4) & 1 & 1959\\\\\n",
       "\t FOODS\\_3\\_827 & ARIMA(value) & 1960 & N(0.96, 1.4) & 1 & 1960\\\\\n",
       "\t FOODS\\_3\\_827 & ARIMA(value) & 1961 & N(0.96, 1.4) & 1 & 1961\\\\\n",
       "\t FOODS\\_3\\_827 & ARIMA(value) & 1962 & N(0.96, 1.4) & 1 & 1962\\\\\n",
       "\t FOODS\\_3\\_827 & ARIMA(value) & 1963 & N(0.96, 1.4) & 1 & 1963\\\\\n",
       "\t FOODS\\_3\\_827 & ARIMA(value) & 1964 & N(0.96, 1.4) & 1 & 1964\\\\\n",
       "\t FOODS\\_3\\_827 & ARIMA(value) & 1965 & N(0.96, 1.4) & 1 & 1965\\\\\n",
       "\t FOODS\\_3\\_827 & ARIMA(value) & 1966 & N(0.96, 1.4) & 1 & 1966\\\\\n",
       "\t FOODS\\_3\\_827 & ARIMA(value) & 1967 & N(0.96, 1.4) & 1 & 1967\\\\\n",
       "\t FOODS\\_3\\_827 & ARIMA(value) & 1968 & N(0.96, 1.4) & 1 & 1968\\\\\n",
       "\t FOODS\\_3\\_827 & ARIMA(value) & 1969 & N(0.96, 1.4) & 1 & 1969\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A tbl_ts: 28 × 6\n",
       "\n",
       "| id &lt;chr&gt; | .model &lt;chr&gt; | name &lt;dbl&gt; | value &lt;dist&gt; | .mean &lt;dbl&gt; | index &lt;dbl&gt; |\n",
       "|---|---|---|---|---|---|\n",
       "| FOODS_3_827 | ARIMA(value) | 1942 | N(1.2, 1.3) | 2 | 1942 |\n",
       "| FOODS_3_827 | ARIMA(value) | 1943 | N(0.89, 1.4) | 1 | 1943 |\n",
       "| FOODS_3_827 | ARIMA(value) | 1944 | N(0.96, 1.4) | 1 | 1944 |\n",
       "| FOODS_3_827 | ARIMA(value) | 1945 | N(0.96, 1.4) | 1 | 1945 |\n",
       "| FOODS_3_827 | ARIMA(value) | 1946 | N(0.96, 1.4) | 1 | 1946 |\n",
       "| FOODS_3_827 | ARIMA(value) | 1947 | N(0.96, 1.4) | 1 | 1947 |\n",
       "| FOODS_3_827 | ARIMA(value) | 1948 | N(0.96, 1.4) | 1 | 1948 |\n",
       "| FOODS_3_827 | ARIMA(value) | 1949 | N(0.96, 1.4) | 1 | 1949 |\n",
       "| FOODS_3_827 | ARIMA(value) | 1950 | N(0.96, 1.4) | 1 | 1950 |\n",
       "| FOODS_3_827 | ARIMA(value) | 1951 | N(0.96, 1.4) | 1 | 1951 |\n",
       "| FOODS_3_827 | ARIMA(value) | 1952 | N(0.96, 1.4) | 1 | 1952 |\n",
       "| FOODS_3_827 | ARIMA(value) | 1953 | N(0.96, 1.4) | 1 | 1953 |\n",
       "| FOODS_3_827 | ARIMA(value) | 1954 | N(0.96, 1.4) | 1 | 1954 |\n",
       "| FOODS_3_827 | ARIMA(value) | 1955 | N(0.96, 1.4) | 1 | 1955 |\n",
       "| FOODS_3_827 | ARIMA(value) | 1956 | N(0.96, 1.4) | 1 | 1956 |\n",
       "| FOODS_3_827 | ARIMA(value) | 1957 | N(0.96, 1.4) | 1 | 1957 |\n",
       "| FOODS_3_827 | ARIMA(value) | 1958 | N(0.96, 1.4) | 1 | 1958 |\n",
       "| FOODS_3_827 | ARIMA(value) | 1959 | N(0.96, 1.4) | 1 | 1959 |\n",
       "| FOODS_3_827 | ARIMA(value) | 1960 | N(0.96, 1.4) | 1 | 1960 |\n",
       "| FOODS_3_827 | ARIMA(value) | 1961 | N(0.96, 1.4) | 1 | 1961 |\n",
       "| FOODS_3_827 | ARIMA(value) | 1962 | N(0.96, 1.4) | 1 | 1962 |\n",
       "| FOODS_3_827 | ARIMA(value) | 1963 | N(0.96, 1.4) | 1 | 1963 |\n",
       "| FOODS_3_827 | ARIMA(value) | 1964 | N(0.96, 1.4) | 1 | 1964 |\n",
       "| FOODS_3_827 | ARIMA(value) | 1965 | N(0.96, 1.4) | 1 | 1965 |\n",
       "| FOODS_3_827 | ARIMA(value) | 1966 | N(0.96, 1.4) | 1 | 1966 |\n",
       "| FOODS_3_827 | ARIMA(value) | 1967 | N(0.96, 1.4) | 1 | 1967 |\n",
       "| FOODS_3_827 | ARIMA(value) | 1968 | N(0.96, 1.4) | 1 | 1968 |\n",
       "| FOODS_3_827 | ARIMA(value) | 1969 | N(0.96, 1.4) | 1 | 1969 |\n",
       "\n"
      ],
      "text/plain": [
       "   id          .model       name value        .mean index\n",
       "1  FOODS_3_827 ARIMA(value) 1942 N(1.2, 1.3)  2     1942 \n",
       "2  FOODS_3_827 ARIMA(value) 1943 N(0.89, 1.4) 1     1943 \n",
       "3  FOODS_3_827 ARIMA(value) 1944 N(0.96, 1.4) 1     1944 \n",
       "4  FOODS_3_827 ARIMA(value) 1945 N(0.96, 1.4) 1     1945 \n",
       "5  FOODS_3_827 ARIMA(value) 1946 N(0.96, 1.4) 1     1946 \n",
       "6  FOODS_3_827 ARIMA(value) 1947 N(0.96, 1.4) 1     1947 \n",
       "7  FOODS_3_827 ARIMA(value) 1948 N(0.96, 1.4) 1     1948 \n",
       "8  FOODS_3_827 ARIMA(value) 1949 N(0.96, 1.4) 1     1949 \n",
       "9  FOODS_3_827 ARIMA(value) 1950 N(0.96, 1.4) 1     1950 \n",
       "10 FOODS_3_827 ARIMA(value) 1951 N(0.96, 1.4) 1     1951 \n",
       "11 FOODS_3_827 ARIMA(value) 1952 N(0.96, 1.4) 1     1952 \n",
       "12 FOODS_3_827 ARIMA(value) 1953 N(0.96, 1.4) 1     1953 \n",
       "13 FOODS_3_827 ARIMA(value) 1954 N(0.96, 1.4) 1     1954 \n",
       "14 FOODS_3_827 ARIMA(value) 1955 N(0.96, 1.4) 1     1955 \n",
       "15 FOODS_3_827 ARIMA(value) 1956 N(0.96, 1.4) 1     1956 \n",
       "16 FOODS_3_827 ARIMA(value) 1957 N(0.96, 1.4) 1     1957 \n",
       "17 FOODS_3_827 ARIMA(value) 1958 N(0.96, 1.4) 1     1958 \n",
       "18 FOODS_3_827 ARIMA(value) 1959 N(0.96, 1.4) 1     1959 \n",
       "19 FOODS_3_827 ARIMA(value) 1960 N(0.96, 1.4) 1     1960 \n",
       "20 FOODS_3_827 ARIMA(value) 1961 N(0.96, 1.4) 1     1961 \n",
       "21 FOODS_3_827 ARIMA(value) 1962 N(0.96, 1.4) 1     1962 \n",
       "22 FOODS_3_827 ARIMA(value) 1963 N(0.96, 1.4) 1     1963 \n",
       "23 FOODS_3_827 ARIMA(value) 1964 N(0.96, 1.4) 1     1964 \n",
       "24 FOODS_3_827 ARIMA(value) 1965 N(0.96, 1.4) 1     1965 \n",
       "25 FOODS_3_827 ARIMA(value) 1966 N(0.96, 1.4) 1     1966 \n",
       "26 FOODS_3_827 ARIMA(value) 1967 N(0.96, 1.4) 1     1967 \n",
       "27 FOODS_3_827 ARIMA(value) 1968 N(0.96, 1.4) 1     1968 \n",
       "28 FOODS_3_827 ARIMA(value) 1969 N(0.96, 1.4) 1     1969 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "FOODS_3_001"
   ]
  }
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