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

Title: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 this http URL, an R package that implements several probabilistic forecasting methods for intermittent series within the 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 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 this http URL on four datasets, also released in the package.

Machine Learning (cs.LG)

arXiv:2609.28607 [cs.LG]

(or arXiv:2609.28607v1 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2609.28607 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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