fable.intermittent：面向间歇性时间序列的概率预测方法基准测试

View PDF HTML (experimental)

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 fable$.$intermittent, 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 fable$.$intermittent on four datasets, also released in the package.

Comments:	Submitted to the International Journal of Forecasting
Subjects:	Machine Learning (cs.LG)
Cite as:	arXiv:2609.28607 [cs.LG]
	(or arXiv:2609.28607v3 [cs.LG] for this version)
	https://doi.org/10.48550/arXiv.2609.28607 arXiv-issued DOI via DataCite

Submission history

From: Stefano Damato [view email]
[v1] Wed, 23 Sep 2026 16:37:33 UTC (384 KB)
[v2] Tue, 29 Sep 2026 18:59:17 UTC (379 KB)
[v3] Thu, 1 Oct 2026 03:18:50 UTC (379 KB)