forecast package v9

It’s been nearly nine years since the last major release of the forecast package, as we have been focusing on developing fable and related packages as a tidyverse-based alternative for time series forecasting in R.

However, version 9.0 of the forecast package has now been released on CRAN. Thanks to Maximilian Muecke for helping with this release, and for modernising some of the old code.

Here are the main new features and changes.

New forecasting models

The usual workflow with the forecast package is to fit a model to a time series, and then forecast from the model. For example

USAccDeaths |> ets() |> forecast()
USAccDeaths |> auto.arima() |> forecast()

But there have always been a few functions that produce forecasts directly from a time series, without the need to explicitly fit a model first. For example,

USAccDeaths |> snaive()

produces seasonal naive forecasts.

But now you can obtain the same forecasts by first fitting a model and then forecasting from it:

USAccDeaths |> rw_model(lag = 12) |> forecast()

Similarly, you can now fit and forecast from mean models, cubic spline models, theta models, and Croston’s method using the new mean_model(), spline_model(), theta_model(), and croston_model() functions. The existing naive(), snaive(), rwf, meanf(), splinef(), thetaf(), and croston() functions are still available for backward compatibility, but are now merely wrappers around the modelling and forecast functions.

This should make it easier to set up forecasting workflows involving many different models. For example, suppose you wanted to compute forecasts from five different models applied to the USAccDeaths time series.

library(purrr)
model_fns <- list(ets, auto.arima, rw_model, rw_model, mean_model)
args <- vector("list", length(model_fns))
args[[3]] <- list(lag = 12)
args[[4]] <- list(lag = 12, drift = TRUE)
models <- map2(model_fns, args, ~ do.call(.x, c(list(y=USAccDeaths), .y)))
names(models) <- map(models, function(u) u$method)
forecasts <- map(models, forecast)

Note that rw_model is included twice, with different arguments, allowing the calculation of seasonal naive, and seasonal naive with drift, forecasts.

Forecasting with multiple models is easy to do using the fable package (and was one of the key considerations in its design). Now it is also possible to do it using the forecast package, with this more consistent interface.

Missing values in ETS models

I wrote the first version of the ETS code (more than 25 years ago) for a consulting project where there were no missing values, so I didn’t write the code to handle them. But obviously missing values occur a lot in many applications, and I’ve now belatedly added support for them. If there are gaps in a time series, ets() can be applied, and it computes the likelihood and fitted values correctly using all available data. (Previously, it would fit a model to the largest contiguous segment of the data.)

This change has also been made to the ETS() function in the fable package.

Bootstrapped prediction intervals

The default prediction intervals in the forecast package are based on the (usually reasonable) assumption that forecast errors are Gaussian. Bootstrapped prediction intervals allow for non-Gaussian forecast errors. These have long been available using ETS, ARIMA, and other models; they are now also available using ARFIMA and TBATS models.

Other changes

• Added parallelization for nnetar().
• More consistent handling of the biasadj argument across models.
• More consistent interface for simulated and bootstrapped prediction intervals for all models.
• Bug fixes and performance improvements.
• Documentation improvements.

Future of the forecast package

I would still encourage people to use the fable package for new projects, as it provides a more modern and consistent interface for time series forecasting in R. However, the forecast package will continue to be maintained, with bug fixes and minor improvements as needed.