---
title: "Tidy functions"
author: "Alexander Häußer"
output: rmarkdown::html_vignette
vignette: >
  %\VignetteIndexEntry{Tidy functions}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---

```{r, include = FALSE}
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  fig.height = 5,
  fig.width = 7
)
```

## Load package

```{r packages, message = FALSE, warning = FALSE}
library(echos)
library(tidyverse)
library(tsibble)
library(fable)
```

## Prepare dataset

In this example, we will use the `m4_monthly_subset`. The dataset is a monthly `tsibble`, which is filtered to include only the time series `"M21655"` and `"M2717"`. The resulting object `train_frame` contains the training data and is visualized below.

```{r data, fig.alt = "Plot actual values"}
train_frame <- m4_monthly_subset %>%
  filter(series %in% c("M21655", "M2717"))

train_frame

p <- ggplot()

p <- p + geom_line(
  data = train_frame,
  aes(
    x = index,
    y = value),
  linewidth = 0.5
)

p <- p + facet_wrap(
  vars(series),
  ncol = 1,
  scales = "free")

p
```


## Train ESN model

The function `ESN()` is used in combination with `model()` from the `fabletools` package to train an Echo State Network for the variable `value`. The trained models are stored as a `mable` (i.e., *model table*). Additionally, an `ARIMA()` model is trained as a benchmark.

```{r mable_frame}
mable_frame <- train_frame %>%
  model(
    "ESN" = ESN(value),
    "ARIMA" = ARIMA(value)
    )

mable_frame
```

## Forecast ESN model

Forecasts are generated via the function `forecast()`, where the forecast horizon is set to `h = 18` (i.e., 18-month-ahead forecasts). The forecasts are stored as a `fable` (i.e., *forecast table*) and visualized along the historical training data.

```{r fable_frame, fig.alt = "Plot forecast and train data"}
fable_frame <- mable_frame %>%
  forecast(h = 18)

fable_frame

fable_frame %>%
  autoplot(train_frame, level = NULL)
```
