Semantics-Enhanced Retrieval-Augmented Time Series Forecasting

[Submitted on 12 Jun 2026]

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Abstract:Time series forecasting models often benefit from historical patterns. Inspired by Retrieval-Augmented Generation (RAG), recent research explored retrieving relevant historical time series segments to enhance forecasting. However, relying solely on time series similarity is often insufficient for retrieval under non-stationarity. To address this, we propose a multimodal approach: a textbf{S}emantics-textbf{E}nhanced textbf{R}etrieval-textbf{A}ugmented Time Series textbf{F}orecasting framework, SERAF. Unlike mainstream approaches that depend only on time series similarity, SERAF conducts dual retrieval over the time series and their self-generated textual descriptions. It retrieves two complementary sets of historical patterns and corresponding futures, which are selectively and jointly used to guide future predictions. Experiments across seven real-world datasets demonstrate the effectiveness of SERAF in bridging numerical and semantic views of time series compared with state-of-the-art baselines.

Submission history

From: Shiqiao Zhou [view email]
[v1] Fri, 12 Jun 2026 20:32:10 UTC (173 KB)