Soft Bayesian Context Tree Models for Real-Valued Time Series

TL;DR

This paper introduces Soft-BCT, a probabilistic extension of Bayesian context trees for real-valued time series, utilizing variational inference to improve modeling flexibility and performance.

Contribution

It presents a novel soft split approach in Bayesian context trees and a variational inference algorithm, advancing time series modeling techniques.

Findings

Soft-BCT outperforms previous BCT models on certain datasets.

The proposed model effectively captures complex patterns in real-valued time series.

Experimental results demonstrate the superiority of Soft-BCT.

Abstract

This paper proposes the soft Bayesian context tree model (Soft-BCT), which is a novel BCT model for real-valued time series. The Soft-BCT considers soft (probabilistic) splits of the context space, instead of hard (deterministic) splits of the context space as in the previous BCT for real-valued time series. A learning algorithm of the Soft-BCT is proposed based on the variational inference. The results of experiments demonstrate the superiority of the Soft-BCT compared to the previous BCT for some datasets.