TempShap reveals when and how much a feature matters in time-series forecasting by treating input cells as players in a retrospective Shapley framework
Read the original at arxiv.org→arXiv:2610.33330v1 Announce Type: new Abstract: Attribution for a time-series forecaster should answer two questions that a feature-level scalar cannot: when in the input window a feature acts, and how much its...
Original headline: "TempShap: When and How Much a Feature Matters in Time-Series Forecasting"
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- Oct 27, 04:00 UTC arXiv cs.AI lead source TempShap: When and How Much a Feature Matters in Time-Series Forecasting