Soft Bayesian Context Tree Models for Real-Valued Time Series

Shota Saito, Yuta Nakahara, Toshiyasu Matsushima
1/16/2026
cs.LG

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Abstract

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. For some real-world datasets, the Soft-BCT demonstrates almost the same or superior performance to the previous BCT.

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Cite this paper

@article{saito2026soft,
  title  = {Soft Bayesian Context Tree Models for Real-Valued Time Series},
  author = {Shota Saito and Yuta Nakahara and Toshiyasu Matsushima},
  year   = {2026},
  eprint = {2601.11079},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2601.11079v1}
}

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