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Found 4 papers, 1 papers with code
The Bayesian Context Trees State Space Model for time series modelling and forecasting
The utility of the general framework is illustrated in two particular instances: When autoregressive (AR) models are used as base models, resulting in a nonlinear AR mixture model, and when conditional heteroscedastic (ARCH) models are used, resulting in a mixture model that offers a powerful and systematic way of modelling the well-known volatility asymmetries in financial data.
Change-point Detection and Segmentation of Discrete Data using Bayesian Context Trees
A new Bayesian modelling framework is introduced for piece-wise homogeneous variable-memory Markov chains, along with a collection of effective algorithmic tools for change-point detection and segmentation of discrete time series.
Context-tree weighting for real-valued time series: Bayesian inference with hierarchical mixture models
At the bottom level, a different real-valued time series model is associated with each context-state, i. e., with each leaf of the tree.
Bayesian Context Trees: Modelling and exact inference for discrete time series
We develop a new Bayesian modelling framework for the class of higher-order, variable-memory Markov chains, and introduce an associated collection of methodological tools for exact inference with discrete time series.
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