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

arXiv: https://arxiv.org/abs/2601.16112 · HTML: https://mlsanity.com/paper/2601.16112/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 40 references that are in the map.
- First posted on arXiv 22 Jan 2026.

## Builds on (most cited first, 7 of 7)

- [Bayesian Context Trees: Modelling and exact inference for discrete time series](https://mlsanity.com/paper/2007.14900/) (2020)
- [Probability Distribution on Full Rooted Trees](https://mlsanity.com/paper/2109.12825/) (2021)
- [Posterior Representations for Bayesian Context Trees: Sampling, Estimation and Convergence](https://mlsanity.com/paper/2202.02239/) (2022)
- [Truly Bayesian Entropy Estimation](https://mlsanity.com/paper/2212.06705/) (2022)
- [Batch Updating of a Posterior Tree Distribution over a Meta-Tree](https://mlsanity.com/paper/2303.09705/) (2023)
- [The Bayesian Context Trees State Space Model for time series modelling and forecasting](https://mlsanity.com/paper/2308.00913/) (2023)
- [Soft Bayesian Context Tree Models for Real-Valued Time Series](https://mlsanity.com/paper/2601.11079/) (2026)

## Abstract (arXiv)

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.