fable-tfeam-2018 | Slides and resources for the fable talk at TFEAM | Awesome List library

ARIMA requires a regular time series, however it will also work in the presence of missing values. You can use tsibble::fill_gaps() to convert implicit missing values to explicit.

Correct, the fable package is currently experimental and changes to the interface are expected to continue. These changes will likely have a relatively minor impact on users. Since the fable TFEAM talk, we now support multiple model columns in a mable. To achieve this, we now use model() to specify models. Previously, if you wanted to model data %>% ETS(log(y) ~ season("A")), this is now data %>% model(ETS(log(y) ~ season("A")).

Automatic model selection (such as forecast::auto.arima()) is contained within the same function in fable (ARIMA()). When estimating a model, if the right-hand-side is left empty, a model will be chosen automatically from the defaults. For ARIMA models, if you used data %>% model(ARIMA(y)), an appropriate model will be automatically chosen (same as forecast::auto.arima()). You can also now estimate an ARIMA(p,0,0)(2,1,Q)[12] model, where p and Q are unknown between 0 and 3. To do this, you would use data %>% model(ARIMA(y ~ pdq(0:3, 0, 0) + PDQ(2, 1, 0:3, period = 12))).

That code looks correct, and should still work. Perhaps you need to update the packages.