README.md

In JSzitas/fable.tbats: Fable Wrapper for TBATS

fable.tbats

fable.tbats is a wrapper around the implementation of tbats originally from the forecast package, though it now works without this dependency.

Installation

pak::pkg_install("JSzitas/fable.tbats")

Usage

Used just like any model in fable:

library(tsibbledata)
library(fable)
library(fable.tbats)
library(dplyr)

# fit models to the pelt dataset until 1930:
train <- pelt %>% 
  filter(Year < 1930)
test <- pelt %>% 
  filter(Year >= 1930)

models <- train %>% 
  model( ets = ETS(Lynx),
         bats = BATS(Lynx),
         tbats = TBATS(Lynx)
         ) 
# generate forecasts on the test set
forecasts <- forecast(models, test)
# visualize
autoplot(forecasts, pelt)

Similarly, accuracy calculation works:

train_accuracies <- accuracy(models)
knitr::kable(train_accuracies)

| .model | .type | ME | RMSE | MAE | MPE | MAPE | MASE | RMSSE | ACF1 | |:-------|:---------|-----------:|----------:|---------:|-----------:|---------:|----------:|----------:|----------:| | ets | Training | -77.59902 | 12891.891 | 9824.778 | -20.073965 | 52.20456 | 0.9934890 | 0.9948210 | 0.5352087 | | bats | Training | 1768.39519 | 8540.088 | 6105.791 | -2.816831 | 27.18703 | 0.6174222 | 0.6590080 | 0.1656173 | | tbats | Training | 1653.48581 | 7955.411 | 5577.862 | -3.003260 | 26.36744 | 0.5640376 | 0.6138906 | 0.0367109 |

test_accuracies <- accuracy(forecasts, test)
knitr::kable(test_accuracies)

| .model | .type | ME | RMSE | MAE | MPE | MAPE | MASE | RMSSE | ACF1 | |:-------|:------|-----------:|----------:|----------:|----------:|---------:|-----:|------:|----------:| | bats | Test | -10363.643 | 16253.210 | 10829.331 | -40.43004 | 43.33877 | NaN | NaN | 0.4460149 | | ets | Test | 1061.473 | 10669.984 | 9770.000 | -36.63239 | 71.41690 | NaN | NaN | 0.5558575 | | tbats | Test | -1898.560 | 3444.412 | 3208.959 | -16.86434 | 24.23634 | NaN | NaN | 0.1667398 |

As does refitting:

models <- refit( models, pelt )

Of the functionality available in the forecast package, only forecast::tbats.components() is missing.

Performance note

Fitting bats/tbats to a few long series can (potentially) be slower using this wrapper than using the forecast package. This is due to the fact that the internal tbats/bats algorithm always executes sequentially (i.e. with use.parallel = FALSE ) to prevent issues with nested parallelism (as the fabletools::model function is taken to be responsible for handling parallelization).

This should never be a problem on many time series, but does lead to a significant slow-down if you are only modelling a single/few time series. Nonetheless, in those cases the fasster package might be much better suited for your use case anyways.

JSzitas/fable.tbats documentation built on April 18, 2023, 1:55 a.m.