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arXiv · 2510.19345

Foundation Model Forecasts: Form and Function

Alvaro Perez-Diaz·James C. Loach·Danielle E. Toutoungi·Lee Middleton
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Abstract

Time-series foundation models (TSFMs) achieve strong forecast accuracy, yet accuracy alone does not determine practical value. The form of a forecast -- point, quantile, parametric, or trajectory ensemble -- fundamentally constrains which operational tasks it can support. We survey recent TSFMs and find that two-thirds produce only point or parametric forecasts, while many operational tasks require trajectory ensembles that preserve temporal dependence. We establish when forecast types can be converted and when they cannot: trajectory ensembles convert to simpler forms via marginalization without additional assumptions, but the reverse requires imposing temporal dependence through copulas or conformal methods. We prove that marginals cannot determine path-dependent event probabilities -- infinitely many joint distributions share identical marginals but yield different answers to operational questions. We map six fundamental forecasting tasks to minimal sufficient forecast types and provide a task-aligned evaluation framework. Our analysis clarifies when forecast type, not accuracy, differentiates practical utility.

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Alvaro Perez-Diaz, James C. Loach, Danielle E. Toutoungi, Lee Middleton. 2025-10-22. Foundation Model Forecasts: Form and Function. https://arxiv.org/abs/2510.19345

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