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arXiv · 2601.11079

Soft Bayesian Context Tree Models for Real-Valued Time Series

Shota Saito·Yuta Nakahara·Toshiyasu Matsushima
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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. The results of experiments demonstrate the superiority of the Soft-BCT compared to the previous BCT for some datasets.

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Shota Saito, Yuta Nakahara, Toshiyasu Matsushima. 2026-01-16. Soft Bayesian Context Tree Models for Real-Valued Time Series. https://arxiv.org/abs/2601.11079

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