Semantics-Enhanced Retrieval-Augmented Time Series Forecasting

• URL: http://arxiv.org/abs/2606.14941v1
• Date: Fri, 12 Jun 2026 20:32:10 GMT
• Title: Semantics-Enhanced Retrieval-Augmented Time Series Forecasting
• Abstract summary: Time series forecasting models often benefit from historical patterns.<n>Recent research explored retrieving relevant historical time series segments to enhance forecasting.<n>We propose a multimodal approach: a textbfSemantics-textbfEnhanced textbfRetrieval-textbfAugmented Time Series textbfForecasting framework, SERAF.
• Score: 5.059121667323038
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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