The new forecastHybrid package for R by David Shaub and myself provides convenient access to ensemble time series forecasting methods, in any combination of up to five of the modelling approaches from Hyndman’s forecast package. These are auto-selected autoregressive integrated moving average; exponential smoothing state space (both ets and tbats); feed forward neural network with a single hidden layer and lagged inputs; and forecasts of loess-based seasonal decomposition.
Background and motivation
In an earlier post I explored ways that might improve on standard methods for prediction intervals from univariate time series forecasting. One of the tools I used was a convenience function to combine forecasts from Rob Hyndman’s ets and auto.arima functions. David Shaub (with a small contribution from myself) has now built and published an R package forecastHybrid that expands on this idea to create ensembles from other forecasting methods from Hyndman’s forecast package.
The motivation is to make it easy to improve forecasts, both their point estimates and their prediction intervals. It has been well known for many years that taking the average of rival forecast methods improves the performance of forecasts. This new R package aims to make it as easy for people to do this as to fit the individual models in the first place.