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arXiv Machine Learning By Yong-Hoon Choi, Kwang-Hyun Park, Youngjin Cho

Which Histories Matter for Time Series Forecasting? Learning Predictive Relevance with Future Supervision

Read the original on arXiv Machine Learning →

The paper investigates which historical examples are most useful for time‑series forecasting by defining predictive relevance as the expected future utility conditioned on inference‑time information. It introduces a two‑stage approach: a normalized‑pattern retriever generates a coarse candidate set, and a lightweight MLP reranks these candidates using future‑supervised relevance while keeping inference strictly past‑only. Experiments on six benchmarks show that this reranker improves pattern retrieval and outperforms a matched‑protocol baseline, revealing that historical relevance is structured, domain‑dependent, and not governed by a single universal retrieval rule.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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