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arXiv AI By Yuchen He, Yueyang Cang, Zhiyuan Ning, Ningyu Wang, Li Shi

RATL: Learning from Retrieved Residuals for Robust Multivariate Time-Series Forecasting

Read the original on arXiv AI →

RATL is a plug‑in method for multivariate time‑series forecasting that uses a frozen base forecaster to build a memory of its historical forecast residuals. During inference, RATL retrieves residual trajectories from similar past contexts and employs a set‑aware router to combine them, providing learned feedback correction. Experiments demonstrate that this residual‑retrieval approach improves the performance of the base forecaster across various benchmarks and backbones.

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 AI.

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