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.
Cite
@article{arxiv.2601.11079,
title = {Soft Bayesian Context Tree Models for Real-Valued Time Series},
author = {Shota Saito and Yuta Nakahara and Toshiyasu Matsushima},
journal= {arXiv preprint arXiv:2601.11079},
year = {2026}
}