AI RESEARCH
Stationarity-Aware Retrieval-Augmented Time Series Forecasting
arXiv CS.LG
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ArXi:2606.04135v1 Announce Type: new Time series forecasting relies on historical patterns, but real-world series often exhibit non-stationarity and regime shifts that challenge fully parametric forecasters. Inspired by Retrieval-Augmented Generation (RAG), recent work augments forecasters by retrieving relevant historical segments and using them as external evidence at inference time.