fable.intermittent：间歇性时间序列的概率预测方法基准测试
fable.intermittent: benchmarking probabilistic forecasting methods for intermittent time series
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中文总结 AI 辅助
本文介绍 R 包 fable.intermittent，统一实现多种间歇性时间序列概率预测方法，提出 TWEES 模型及加速的 tweedieDistr 包，并在四个数据集上评估。
中文摘要 AI 辅助
间歇性时间序列常见于备件需求和零售销售中。由于预测误差的成本通常是不对称的，库存控制等决策需要完整的预测分布，而非点预测。已有许多概率预测方法被提出，但其实现分散在不同的软件框架中，难以进行系统性比较。我们介绍了 this http URL，一个在 fable 框架内实现多种间歇性序列概率预测方法的 R 包。该包允许通过单一、简单的预测流程，对一组时间序列拟合和评估多个模型。我们还引入了 TWEES，一种具有 Tweedie 预测分布的新型指数平滑模型。拟合 TWEES 需要重复计算计算量大的 Tweedie 密度。我们还发布了 R 包 tweedieDistr，其对 Tweedie 分布的实现在保持相同数值精度的同时，速度显著快于现有实现。我们在四个数据集上评估了 this http URL 中实现的方法，这些数据集也随包发布。
英文摘要
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.
发表机构
- SUPSI, Istituto Dalle Molle di Studi sull’Intelligenza Artificiale (IDSIA)（瑞士南部应用科学与艺术大学，达勒莫勒人工智能研究所）
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