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| Auteurs principaux: | , , , , , , , |
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| Format: | Preprint |
| Publié: |
2025
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| Sujets: | |
| Accès en ligne: | https://arxiv.org/abs/2510.02224 |
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| _version_ | 1866912623631532032 |
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| author | Baron, Ethan Oreshkin, Boris Ma, Ruijun Zhang, Hanyu Torkkola, Kari Mahoney, Michael W. Wilson, Andrew Gordon Konstantinova, Tatiana |
| author_facet | Baron, Ethan Oreshkin, Boris Ma, Ruijun Zhang, Hanyu Torkkola, Kari Mahoney, Michael W. Wilson, Andrew Gordon Konstantinova, Tatiana |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_02224 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Efficiently Generating Correlated Sample Paths from Multi-step Time Series Foundation Models Baron, Ethan Oreshkin, Boris Ma, Ruijun Zhang, Hanyu Torkkola, Kari Mahoney, Michael W. Wilson, Andrew Gordon Konstantinova, Tatiana Machine Learning 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. |
| title | Efficiently Generating Correlated Sample Paths from Multi-step Time Series Foundation Models |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2510.02224 |