TS-RAG: Retrieval Augmented Generation for Time Series Forecasting

Yixiong Xiao, Congxi Xiao, Jingbo Zhou|August 6, 2026arXiv

Key Takeaway

RAG can boost time series forecasting by retrieving similar past sequences and intelligently fusing them into the model, rather than just appending them like in language models.

Summary

This paper adapts retrieval-augmented generation (RAG) to time series forecasting by retrieving similar historical sequences and using special tokens to blend them with input data.

reasoning efficiency training

Key Terms

rag-pipeline time-series-forecasting reference-attention temporal-dynamics