Beyond Model Ranking: Predictability-Aligned Evaluation for Time Series Forecasting

arXiv:2509.23074v3 Announce Type: replace Abstract: In the era of increasingly complex AI models for time series forecasting, progress is often measured by marginal improvements on benchmark leaderboards. However, this approach suffers from a fundamental flaw: standard evaluation metrics conflate a model's performance with the data's intrinsic unpredictability. To address this pressing challenge, we introduce a novel, predictability-aligned diagnostic framework grounded in spectral coherence. Our framework makes two primary contributions: the Spectral Coherence Predictability (SCP), a computat

Source: arXiv cs.LG — read the full report at the original publisher.

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