{"slug": "semantics-enhanced-retrieval-augmented-time-series-forecasting", "title": "Semantics-Enhanced Retrieval-Augmented Time Series Forecasting", "summary": "Researchers propose SERAF, a semantics-enhanced retrieval-augmented time series forecasting framework that retrieves both historical time series segments and their self-generated textual descriptions to improve prediction accuracy under non-stationarity. Experiments on seven real-world datasets show SERAF outperforms state-of-the-art baselines by bridging numerical and semantic views.", "body_md": "arXiv:2606.14941v1 Announce Type: new\nAbstract: 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.", "url": "https://wpnews.pro/news/semantics-enhanced-retrieval-augmented-time-series-forecasting", "canonical_source": "https://arxiv.org/abs/2606.14941", "published_at": "2026-06-16 04:00:00+00:00", "updated_at": "2026-06-16 04:20:30.146396+00:00", "lang": "en", "topics": ["machine-learning", "large-language-models", "generative-ai", "natural-language-processing", "ai-research"], "entities": ["SERAF"], "alternates": {"html": "https://wpnews.pro/news/semantics-enhanced-retrieval-augmented-time-series-forecasting", "markdown": "https://wpnews.pro/news/semantics-enhanced-retrieval-augmented-time-series-forecasting.md", "text": "https://wpnews.pro/news/semantics-enhanced-retrieval-augmented-time-series-forecasting.txt", "jsonld": "https://wpnews.pro/news/semantics-enhanced-retrieval-augmented-time-series-forecasting.jsonld"}}