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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2023
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2310.01327 |
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| _version_ | 1866914727030947840 |
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| author | Ashok, Arjun Marcotte, Étienne Zantedeschi, Valentina Chapados, Nicolas Drouin, Alexandre |
| author_facet | Ashok, Arjun Marcotte, Étienne Zantedeschi, Valentina Chapados, Nicolas Drouin, Alexandre |
| contents | We introduce a new model for multivariate probabilistic time series prediction, designed to flexibly address a range of tasks including forecasting, interpolation, and their combinations. Building on copula theory, we propose a simplified objective for the recently-introduced transformer-based attentional copulas (TACTiS), wherein the number of distributional parameters now scales linearly with the number of variables instead of factorially. The new objective requires the introduction of a training curriculum, which goes hand-in-hand with necessary changes to the original architecture. We show that the resulting model has significantly better training dynamics and achieves state-of-the-art performance across diverse real-world forecasting tasks, while maintaining the flexibility of prior work, such as seamless handling of unaligned and unevenly-sampled time series. Code is made available at https://github.com/ServiceNow/TACTiS. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2310_01327 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | TACTiS-2: Better, Faster, Simpler Attentional Copulas for Multivariate Time Series Ashok, Arjun Marcotte, Étienne Zantedeschi, Valentina Chapados, Nicolas Drouin, Alexandre Machine Learning Artificial Intelligence We introduce a new model for multivariate probabilistic time series prediction, designed to flexibly address a range of tasks including forecasting, interpolation, and their combinations. Building on copula theory, we propose a simplified objective for the recently-introduced transformer-based attentional copulas (TACTiS), wherein the number of distributional parameters now scales linearly with the number of variables instead of factorially. The new objective requires the introduction of a training curriculum, which goes hand-in-hand with necessary changes to the original architecture. We show that the resulting model has significantly better training dynamics and achieves state-of-the-art performance across diverse real-world forecasting tasks, while maintaining the flexibility of prior work, such as seamless handling of unaligned and unevenly-sampled time series. Code is made available at https://github.com/ServiceNow/TACTiS. |
| title | TACTiS-2: Better, Faster, Simpler Attentional Copulas for Multivariate Time Series |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2310.01327 |