Variable Splitting Binary Tree Models Based on Bayesian Context Tree Models for Time Series Segmentation

• URL: http://arxiv.org/abs/2601.16112v1
• Date: Thu, 22 Jan 2026 16:58:34 GMT
• Title: Variable Splitting Binary Tree Models Based on Bayesian Context Tree Models for Time Series Segmentation
• Authors: Yuta Nakahara, Shota Saito, Kohei Horinouchi, Koshi Shimada, Naoki Ichijo, Manabu Kobayashi, Toshiyasu Matsushima,
• Abstract summary: We propose a variable splitting binary tree (VSBT) model based on Bayesian context tree (BCT) models for time series segmentation.<n>By adjusting logistic regression coefficients, our model can represent split positions at arbitrary locations within each interval.<n>For simultaneous estimation of both split positions and tree depth, we develop an effective inference algorithm.
• Score: 5.637202202042452
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We propose a variable splitting binary tree (VSBT) model based on Bayesian context tree (BCT) models for time series segmentation. Unlike previous applications of BCT models, the tree structure in our model represents interval partitioning on the time domain. Moreover, interval partitioning is represented by recursive logistic regression models. By adjusting logistic regression coefficients, our model can represent split positions at arbitrary locations within each interval. This enables more compact tree representations. For simultaneous estimation of both split positions and tree depth, we develop an effective inference algorithm that combines local variational approximation for logistic regression with the context tree weighting (CTW) algorithm. We present numerical examples on synthetic data demonstrating the effectiveness of our model and algorithm.

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