fable.intermittent: benchmarking probabilistic forecasting methods for intermittent time series

Abstract

Intermittent time series are common in spare-parts demand and retail sales. Since the cost of forecast errors is typically asymmetric, decisions such as inventory control require the full predictive distribution rather than a point forecast. Many probabilistic forecasting methods have been proposed; their implementations, however, are scattered across different software frameworks, making it difficult to compare them systematically. We introduce $\textbf{fable.intermittent}$, an R package that implements several probabilistic forecasting methods for intermittent series within the $\textbf{fable}$ framework. The package allows several models to be fitted and evaluated on a collection of time series through a single, simple forecasting pipeline. We also introduce TWEES, a new exponential smoothing model with a Tweedie predictive distribution. Fitting TWEES requires repeated evaluation of the computationally demanding Tweedie density. We also release the R package $\textbf{tweedieDistr}$, whose implementation of the Tweedie distribution is substantially faster than the existing one while preserving the same numerical accuracy. We evaluate the methods implemented in $\textbf{fable.intermittent}$ on four datasets, also released in the package.