aiqbench analysis
This research introduces SERAF, a multimodal framework for time series forecasting that integrates textual descriptions alongside numerical data to improve retrieval accuracy under non-stationary conditions. By leveraging Retrieval-Augmented Generation (RAG) principles, the method effectively bridges semantic and quantitative views of historical patterns across seven real-world datasets. The approach represents a significant advancement in applying generative AI techniques specifically to predictive modeling challenges within artificial intelligence research.
Semantics-Enhanced Retrieval-Augmented Time Series Forecasting
arXiv:2606.14941v1 Announce Type: new 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.
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.
Artificial Intelligence (cs.AI)
arXiv:2606.14941 [cs.AI]
(or arXiv:2606.14941v1 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2606.14941 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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