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arXiv Machine Learning By Seunghan Lee, Jaehoon Lee, Jun Seo, Sungdong Yoo, Minjae Kim, Tae Yoon Lim, Dongwan Kang, Hwanil Choi, SoonYoung Lee, Wonbin Ahn

Not All Retrievals are Useful: Cross-Attention for Input-Aware RAG in Time Series Forecasting

Read the original on arXiv Machine Learning →

arXiv:2603. 14709v2 Announce Type: replace Abstract: Retrieval-augmented generation (RAG) enhances zero-shot time series (TS) forecasting by leveraging external knowledge bases, yet existing approaches overlook input-level relevance when fusing retrieved samples with the query.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

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