mlsanityData to 7 Oct 2026

Semantics-Enhanced Retrieval-Augmented Time Series Forecasting

arXiv 2606.14941 ↗First posted 12 Jun 2026cs.AIcited by 2

On the map: Retrieval-augmented generation → Time series and agricultural RAG

Abstract (arXiv)

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 Semantics-Enhanced Retrieval-Augmented Time Series Forecasting 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.

Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Placed in Time series and agricultural RAG, one of 15 directions in Retrieval-augmented generation. Builds on 12 of its 17 references that are in the map. First posted on arXiv 12 Jun 2026.

Builds on

12 of 17 references.

Closest ancestors · 3

Same direction, newest first

  1. 2025TS-RAG: Retrieval-Augmented Generation based Time Series Foundation Models are Stronger Zero-Shot Forecaster6 Mar
  2. 2024TimeRAF: Retrieval-Augmented Foundation model for Zero-shot Time Series Forecasting30 Dec
  3. 2024Retrieval Augmented Time Series Forecasting12 Nov

Other references · 9

  1. 2020Retrieval-Augmented Generation for Knowledge-Intensive NLP Taskscited by 9,160
  2. 2022A Time Series is Worth 64 Words: Long-term Forecasting with Transformerscited by 1,981
  3. 2021Autoformer: Decomposition Transformers with Auto-Correlation for Long-Term Series Forecastingcited by 1,654
  4. 2022Are Transformers Effective for Time Series Forecasting?cited by 1,400
  5. 2022TimesNet: Temporal 2D-Variation Modeling for General Time Series Analysiscited by 1,030
  6. 2023Time-LLM: Time Series Forecasting by Reprogramming Large Language Modelscited by 680
  7. 2023One Fits All:Power General Time Series Analysis by Pretrained LMcited by 615
  8. 2024CycleNet: Enhancing Time Series Forecasting through Modeling Periodic Patternscited by 102
  9. 2025TRACE: Grounding Time Series in Context for Multimodal Embedding and Retrievalcited by 5

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2026: 2.

2 papers.

  1. 2026LLM-based Agents for Forecasting and Prediction: Methods, Training, Evaluation, and Applicationscited by 0
  2. 2026Which Histories Matter for Time Series Forecasting? Learning Predictive Relevance with Future Supervisioncited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2026Stationarity-Aware Retrieval-Augmented Time Series Forecasting
  2. 2026TS-RAG: Retrieval Augmented Generation for Time Series Forecasting
  3. 2024TimeRAG: BOOSTING LLM Time Series Forecasting via Retrieval-Augmented Generation
  4. 2026Model-agnostic Retrieval-Augmented Extended Forecasting for time series
  5. 2026Spectral Retrieval-Augmented Time-Series Forecasting
  6. 2025QuiZSF: A Retrieval-Augmented Framework for Zero-Shot Time Series Forecasting
  7. 2026Not All Retrievals are Useful: Cross-Attention for Input-Aware RAG in Time Series Forecasting
  8. 2025Retrieval Augmented Time Series Forecasting