Variable Splitting Binary Tree Models Based on Bayesian Context Tree Models for Time Series Segmentation
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arXiv
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| Hauptverfasser: | , , , , , , |
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| Format: | Preprint |
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2026
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| _version_ | 1866915747835412480 |
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| author | Nakahara, Yuta Saito, Shota Horinouchi, Kohei Shimada, Koshi Ichijo, Naoki Kobayashi, Manabu Matsushima, Toshiyasu |
| author_facet | Nakahara, Yuta Saito, Shota Horinouchi, Kohei Shimada, Koshi Ichijo, Naoki Kobayashi, Manabu Matsushima, Toshiyasu |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_16112 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Variable Splitting Binary Tree Models Based on Bayesian Context Tree Models for Time Series Segmentation Nakahara, Yuta Saito, Shota Horinouchi, Kohei Shimada, Koshi Ichijo, Naoki Kobayashi, Manabu Matsushima, Toshiyasu Machine Learning 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. |
| title | Variable Splitting Binary Tree Models Based on Bayesian Context Tree Models for Time Series Segmentation |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2601.16112 |