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arXiv Machine Learning By Emanuele Albini, Francesca Toni, Saumitra Mishra, Francesco Leofante

Temporal Predictive Multiplicity: Equally Accurate Time Series Models Yield Different Forecast Trajectories

Read the original on arXiv Machine Learning →

The paper introduces temporal predictive multiplicity, a framework that examines how models with nearly identical predictive performance can produce markedly different forecast trajectories in time‑series forecasting. It demonstrates that even when models meet the same performance criteria, they can diverge significantly across the entire forecast horizon, and that limiting multiplicity at individual horizons only partially mitigates this issue. Experiments on 19 neural forecasting architectures across 11 datasets confirm that trajectory‑level disagreement is largely independent of horizon‑wise disagreement, revealing a gap in prior multiplicity studies.

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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