Soft Bayesian Context Tree Models for Real-Valued Time Series
Fuente:
arXiv
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| Auteurs principaux: | , , |
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| Format: | Preprint |
| Publié: |
2026
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| _version_ | 1866911703908745216 |
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| author | Saito, Shota Nakahara, Yuta Matsushima, Toshiyasu |
| author_facet | Saito, Shota Nakahara, Yuta Matsushima, Toshiyasu |
| contents | This paper proposes the soft Bayesian context tree model (Soft-BCT), which is a novel BCT model for real-valued time series. The Soft-BCT considers soft (probabilistic) splits of the context space, instead of hard (deterministic) splits of the context space as in the previous BCT for real-valued time series. A learning algorithm of the Soft-BCT is proposed based on the variational inference. The results of experiments demonstrate the superiority of the Soft-BCT compared to the previous BCT for some datasets. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_11079 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Soft Bayesian Context Tree Models for Real-Valued Time Series Saito, Shota Nakahara, Yuta Matsushima, Toshiyasu Machine Learning This paper proposes the soft Bayesian context tree model (Soft-BCT), which is a novel BCT model for real-valued time series. The Soft-BCT considers soft (probabilistic) splits of the context space, instead of hard (deterministic) splits of the context space as in the previous BCT for real-valued time series. A learning algorithm of the Soft-BCT is proposed based on the variational inference. The results of experiments demonstrate the superiority of the Soft-BCT compared to the previous BCT for some datasets. |
| title | Soft Bayesian Context Tree Models for Real-Valued Time Series |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2601.11079 |