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.