{"ID":2861604,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2510.02224","arxiv_id":"2510.02224","title":"Efficiently Generating Correlated Sample Paths from Multi-step Time Series Foundation Models","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.","short_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 dis...","url_abs":"https://arxiv.org/abs/2510.02224","url_pdf":"https://arxiv.org/pdf/2510.02224v1","authors":"[\"Ethan Baron\",\"Boris Oreshkin\",\"Ruijun Ma\",\"Hanyu Zhang\",\"Kari Torkkola\",\"Michael W. Mahoney\",\"Andrew Gordon Wilson\",\"Tatiana Konstantinova\"]","published":"2025-10-02T17:08:58Z","proceeding":"cs.LG","tasks":"[\"cs.LG\",\"stat.ML\"]","methods":"[]","has_code":false}