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arXiv Machine Learning By Zong-Han Bai, Po-Yen Chu

When Does Pooling Pay? Credibility and Resolution under Forgetting in Intermittent-Demand Forecasting

Read the original on arXiv Machine Learning →

The paper investigates when pooling information across intermittent-demand series is beneficial, focusing on the trade‑off between forgetting past data and borrowing strength from other series. By applying a single exponential recency operator to both item‑ and group‑level statistics in a hierarchical empirical‑Bayes hurdle model, the authors derive a diagnostic that quantifies credibility and resolution, and use it to screen for regimes where refinement offers no advantage. Empirical tests on five public panels of over 19,000 series show that the diagnostic accurately predicts when shared priors improve accuracy, and the resulting forecaster (EBB) matches specialist Gaussian‑process models within a few percent while being far cheaper to run.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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