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.

翻译：本文提出了一种用于实值时间序列的新型软贝叶斯上下文树模型（Soft-BCT）。与先前实值时间序列的BCT模型采用上下文空间的硬（确定性）划分不同，Soft-BCT考虑了上下文空间的软（概率性）划分。基于变分推断，本文提出了Soft-BCT的学习算法。在多个真实数据集上的实验表明，Soft-BCT的性能与先前BCT模型相当或更优。