2026-06-16原文2 min readUpdated: 2026-06-16

Semantics-Enhanced Retrieval-Augmented Time Series Forecasting

Existing retrieval-augmented time series forecasting methods rely solely on numerical similarity, often failing under non-stationarity. This paper proposes SERAF, which performs dual retrieval over time series and their self-generated textual descriptions, effectively combining numerical and semantic information. Experiments on seven real-world datasets show superiority over baselines.

SourcearXiv AIAuthor: Shiqiao Zhou, Zipeng Wu, Holger Sch\"oner, Edouard Fouch\'e, IAG Wilson, Shuo Wang

[2606.14941] Semantics-Enhanced Retrieval-Augmented Time Series Forecasting

[Submitted on 12 Jun 2026]

Title:Semantics-Enhanced Retrieval-Augmented Time Series Forecasting

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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.

Comments: Accepted to the ICML 2026 Workshop on Forecasting as a New Frontier of Intelligence

Subjects:

Artificial Intelligence (cs.AI)

Cite as: arXiv:2606.14941 [cs.AI]

(or arXiv:2606.14941v1 [cs.AI] for this version)

https://doi.org/10.48550/arXiv.2606.14941

arXiv-issued DOI via DataCite (pending registration)

Submission history

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

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