Variable Splitting Binary Tree Models Based on Bayesian Context Tree Models for Time Series Segmentation
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.
Keywords
Cite
@article{arxiv.2601.16112,
title = {Variable Splitting Binary Tree Models Based on Bayesian Context Tree Models for Time Series Segmentation},
author = {Yuta Nakahara and Shota Saito and Kohei Horinouchi and Koshi Shimada and Naoki Ichijo and Manabu Kobayashi and Toshiyasu Matsushima},
journal= {arXiv preprint arXiv:2601.16112},
year = {2026}
}