Efficiently Generating Correlated Sample Paths from Multi-step Time Series Foundation Models

• URL: http://arxiv.org/abs/2510.02224v1
• Date: Thu, 02 Oct 2025 17:08:58 GMT
• Title: Efficiently Generating Correlated Sample Paths from Multi-step Time Series Foundation Models
• Authors: Ethan Baron, Boris Oreshkin, Ruijun Ma, Hanyu Zhang, Kari Torkkola, Michael W. Mahoney, Andrew Gordon Wilson, Tatiana Konstantinova,
• Abstract summary: We present a copula-based approach to efficiently generate accurate, correlated sample paths from time series foundation models.<n>Our approach generates correlated sample paths orders of magnitude faster than autoregressive sampling.
• Score: 66.60042743462175
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Many time series applications require access to multi-step forecast trajectories in the form of sample paths. Recently, time series foundation models have leveraged multi-step lookahead predictions to improve the quality and efficiency of multi-step forecasts. However, these models only predict independent marginal distributions for each time step, rather than a full joint predictive distribution. To generate forecast sample paths with realistic correlation structures, one typically resorts to autoregressive sampling, which can be extremely expensive. In this paper, we present a copula-based approach to efficiently generate accurate, correlated sample paths from existing multi-step time series foundation models in one forward pass. Our copula-based approach generates correlated sample paths orders of magnitude faster than autoregressive sampling, and it yields improved sample path quality by mitigating the snowballing error phenomenon.

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