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arXiv · 2610.09994

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

Emanuele Albini·Francesca Toni·Saumitra Mishra·Francesco Leofante
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Abstract

Models with near-identical predictive performance can yield substantially different predictions, a phenomenon known as predictive multiplicity. Prior work has mostly studied this at the level of individual scalar outputs. In time-series forecasting, however, predictions across horizons jointly define a trajectory, and horizon-wise comparisons can hide important differences in predictive behavior. To address this problem, we introduce temporal predictive multiplicity, a framework that characterizes disagreement over complete forecast trajectories among models with near-identical predictive performance. We show that constraining predictive performance alone can still admit a broad range of different trajectories. We further show that constraining multiplicity at individual horizons partially reduces, but does not eliminate, trajectory-level multiplicity. Experiments with 19 neural forecasting architectures on 11 datasets confirm that near-optimal models can exhibit substantial variability in the forecast trajectories they produce, and trajectory-level disagreement is largely unrelated to horizon-wise disagreement. Our framework, therefore, exposes a gap in existing multiplicity studies: models with indistinguishable predictive performance imply fundamentally different temporal trajectories, with consequential downstream effects.

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Emanuele Albini, Francesca Toni, Saumitra Mishra, Francesco Leofante. 2026-10-07. Temporal Predictive Multiplicity: Equally Accurate Time Series Models Yield Different Forecast Trajectories. https://arxiv.org/abs/2610.09994

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