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arXiv Machine Learning By Pengyu Nie, Chenglang Xu, Yaoshi Chen, Chaogan Ren, Wei Hu, Chao Yang, Jiangong Zhang

Beyond Model Ranking: Regime Diagnosis for Distributional-Statistical Misspecification in Industrial Time-Series Forecasting

Read the original on arXiv Machine Learning →

The paper introduces a regime‑diagnosis framework for industrial time‑series forecasting, highlighting that canonical loss functions embed fixed statistical priors that are violated in real‑world demand regimes such as zero‑inflation, skewness, and high variability. It proposes the Regime‑wise Relative Bias Vector (RBV) as a metric‑agnostic diagnostic that decomposes bias into an intrinsic floor and an excess attributable to training. A large‑scale study across 13 loss objectives and 60,000+ series demonstrates that regime‑aware diagnosis distinguishes optimization‑from‑bias failures and that regime‑aware training can eliminate pooling‑induced bias that mere capacity scaling cannot.

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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