ArXiv

TS-RAG: Retrieval Augmented Generation for Time Series Forecasting

2026-08-06 · 16:12 UTC ·Yixiong Xiao, Congxi Xiao, Jingbo Zhou ·1 min read
Authors
Yixiong Xiao, Congxi Xiao, Jingbo Zhou
Categories
cs.AI, cs.LG
arXiv
https://arxiv.org/abs/2608.06223v1
PDF
https://arxiv.org/pdf/2608.06223v1

Brief

TS-RAG adapts retrieval-augmented generation to time-series forecasting by retrieving similar past sequences and injecting them via learned reference tokens that fuse retrieved and input dynamics. Designed to mitigate limited data and small model-scale issues, the approach—presented by Xiao et al. (arXiv:2608.06223v1, 2026-08-06)—yields consistent state-of-the-art results across multiple real-world forecasting benchmarks; consult the full paper for quantitative metrics.

Why it matters

TS-RAG (Yixiong Xiao, Congxi Xiao, Jingbo Zhou; arXiv:2608.06223v1, 2026-08-06) applies retrieval-augmented generation to time-series forecasting by retrieving similar historical sequences and introducing specially designed reference tokens to fuse retrieved and input information.

Key details

  • The authors identify key challenges: time-series models often have limited training data, smaller parameter scales, and lack LLM-style generative capacity; they show naive concatenation of reference sequences is ineffective for forecasting.
  • TS-RAG reportedly achieves consistent state-of-the-art performance across several real-world forecasting benchmarks; the paper presents experimental comparisons (see full text for dataset-specific metrics).
Source evidence

Abstract

While deep learning models, particularly transformer-based architectures, have shown impressive performance in time series forecasting, the application of retrieval-augmented generation (RAG) in this domain remains limited. Since RAG has proven effective in enhancing the capabilities of large language models by incorporating relevant external information, retrieving similar time series sequences as references might also improve accuracy in time series forecasting tasks. However, most time series models are constrained by limited training data, smaller parameter scales, and a lack of the extensive generative capabilities found in large language models. Simply concatenating reference sequences into the prompt, as done in language models, may not yield the expected results. To address these challenges, we propose a novel approach, TS-RAG, which leverages RAG to enhance forecasting performance. The framework introduces specially designed reference tokens to effectively fuse information from the input sequence with that from retrieved similar sequences, enabling a more robust capture of complex temporal dynamics. Experimental results demonstrate that TS-RAG achieves consistent state-of-the-art performance across several real-world forecasting benchmarks.