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arXiv Machine Learning By Tong Guan, Tianxiang Zhan, Shirui Pan, Ming Jin

Rethinking Agentic Model Orchestration for Time Series Forecasting

Read the original on arXiv Machine Learning →

The paper introduces FairAMO, a controlled protocol to evaluate agentic model orchestration (AMO) for time‑series forecasting by freezing candidate forecasts. Experiments on GIFT‑Eval show that while AMO can improve accuracy, its gains are limited by the quality and complementarity of the underlying TSFMs, with reductions in MASE below 6% relative to the best single model. Adding more candidates yields diminishing returns, and redundant members can negate benefits, indicating that historical evidence saturates quickly and that orchestration’s contribution is modest compared to the candidate models themselves.

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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