Soft Bayesian Context Tree Models for Real-Valued Time Series
This work addresses time series analysis for domains like finance or signal processing, but it is incremental as it builds on existing Bayesian context tree models.
The paper tackled modeling real-valued time series by proposing the soft Bayesian context tree model (Soft-BCT), which uses probabilistic splits instead of deterministic ones, and showed it achieves similar or better performance than previous methods on real-world datasets.
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. For some real-world datasets, the Soft-BCT demonstrates almost the same or superior performance to the previous BCT.