提出软贝叶斯上下文树模型，提升连续时间序列建模精度
Soft Bayesian Context Tree Models for Real-Valued Time Series
- 用概率分割替代硬分割，更灵活建模上下文空间
- 在多个数据集上优于传统贝叶斯上下文树模型
- 适合需要精细建模的连续时间序列分析任务
本文提出一种用于实值时间序列的软贝叶斯上下文树模型（Soft-BCT），该模型采用概率性（软）方式分割上下文空间，而非以往方法中的确定性（硬）分割。基于变分推断设计了相应的学习算法。实验结果表明，在部分数据集上，Soft-BCT相比先前的贝叶斯上下文树模型表现更优。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。