TempShap: When and How Much a Feature Matters in Time-Series Forecasting
This story is from 2026-10-27. It is preserved in the archive; the latest stories are on the live feed.
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 influence changes across the forecast horizon. Most existing Shapley explainers treat ea…
Read the full story at arXiv cs.AI ↗
Timeline · 1 report
- 2026-10-27 04:00 · arXiv cs.AI
TempShap: When and How Much a Feature Matters in Time-Series Forecasting