arXiv Machine Learning By Qipeng Qian, Yuntao Qian

From Recoverability to Functional Use: Certifying Temporal Reports in Time-Series Forecasting

Read the original on arXiv Machine Learning →

arXiv:2608. 10433v2 Announce Type: replace Abstract: Temporal reports are increasingly emitted alongside numerical forecasts and are often interpreted as statements about the computation producing those forecasts.

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arXiv AI
Aug 12

Retrieval-Corrected Conformal Prediction for Time Series

arXiv:2608. 10553v1 Announce Type: cross Abstract: Conformal prediction (CP) provides distribution-free prediction intervals for fixed forecasters, but its standard calibration procedure is often inefficient for time series data, where forecast errors are temporally dependent and change across time and operating conditions.

By Sangjin Jin, Kangmin Kim, Junhyeong Lee, Yongjae Lee
arXiv Machine Learning
Sep 14

Explaining Time Series Forecasting with Horizon-Resolved Attribution

The paper introduces Horizon-Resolved eXplanation (HRX), a framework that adds a horizon axis to time‑series forecasting explanations, allowing each forecast step to have its own importance map. HRX operates as a plug‑in for any differentiable forecaster, includes an evaluation protocol that tests the impact of removing top‑ranked inputs, and a rank criterion to decide when horizon resolution is beneficial. Experiments across multiple backbones and datasets demonstrate that incorporating the horizon axis improves explanation quality and that the step‑wise dependence is low‑dimensional, requiring only a few shared maps regardless of forecast length.

By Seunghan Lee, Jun Seo, Jaehoon Lee, Junhyeok Kang, Sangjun Han, Sungdong Yoo, Minjae Kim, Tae Yoon Lim, Dongwan Kang, Hwanil Choi, Soonyoung Lee, Wonbin Ahn