Author: Alexander Häußer

# Tidy functions

#### Alexander Häußer

## Load package

    library(echos)
    library(tidyverse)
    library(tsibble)
    library(fable)

## Prepare dataset

In this example, we will use the `m4_data`. 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.

    train_frame <- m4_data %>%
      filter(series %in% c("M21655", "M2717"))

    train_frame
    #> # A tsibble: 494 x 4 [1M]
    #> # Key:       series [2]
    #>    series category       index value
    #>    <chr>  <fct>          <mth> <dbl>
    #>  1 M21655 Demographic 1995 Jan  4970
    #>  2 M21655 Demographic 1995 Feb  5010
    #>  3 M21655 Demographic 1995 Mrz  5060
    #>  4 M21655 Demographic 1995 Apr  5010
    #>  5 M21655 Demographic 1995 Mai  5610
    #>  6 M21655 Demographic 1995 Jun  6040
    #>  7 M21655 Demographic 1995 Jul  6450
    #>  8 M21655 Demographic 1995 Aug  6370
    #>  9 M21655 Demographic 1995 Sep  5190
    #> 10 M21655 Demographic 1995 Okt  5250
    #> # ℹ 484 more rows

    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 benchmark.

    mable_frame <- train_frame %>%
      model(
        "ESN" = ESN(value),
        "ARIMA" = ARIMA(value)
        )

    mable_frame
    #> # A mable: 2 x 3
    #> # Key:     series [2]
    #>   series                              ESN                     ARIMA
    #>   <chr>                           <model>                   <model>
    #> 1 M21655   <ESN({97, 1, 1}, {194, 25.8})> <ARIMA(1,0,0)(1,1,2)[12]>
    #> 2 M2717  <ESN({100, 1, 1}, {200, 28.78})> <ARIMA(2,1,4)(0,1,0)[12]>

## 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 `fable` (i.e., forecast table) and visualized along the historic training data.

    fable_frame <- mable_frame %>%
      forecast(h = 18)

    fable_frame
    #> # A fable: 72 x 5 [1M]
    #> # Key:     series, .model [4]
    #>    series .model    index
    #>    <chr>  <chr>     <mth>
    #>  1 M21655 ESN    2015 Apr
    #>  2 M21655 ESN    2015 Mai
    #>  3 M21655 ESN    2015 Jun
    #>  4 M21655 ESN    2015 Jul
    #>  5 M21655 ESN    2015 Aug
    #>  6 M21655 ESN    2015 Sep
    #>  7 M21655 ESN    2015 Okt
    #>  8 M21655 ESN    2015 Nov
    #>  9 M21655 ESN    2015 Dez
    #> 10 M21655 ESN    2016 Jan
    #> # ℹ 62 more rows
    #> # ℹ 2 more variables: value <dist>, .mean <dbl>

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