Xiong Sihan, Fu Yiwei, Ray Asok
Department of Mechanical Engineering, Pennsylvania State University, University Park, PA 16802-1412, USA.
Department of Mathematics, Pennsylvania State University, University Park, PA 16802-1412, USA.
Entropy (Basel). 2018 May 23;20(6):396. doi: 10.3390/e20060396.
This paper presents a nonparametric regression model of categorical time series in the setting of conditional tensor factorization and Bayes network. The underlying algorithms are developed to provide a flexible and parsimonious representation for fusion of correlated information from heterogeneous sources, which can be used to improve the performance of prediction tasks and infer the causal relationship between key variables. The proposed method is first illustrated by numerical simulation and then validated with two real-world datasets: (1) experimental data, collected from a swirl-stabilized lean-premixed laboratory-scale combustor, for detection of thermoacoustic instabilities and (2) publicly available economics data for causal inference-making.
本文提出了一种在条件张量分解和贝叶斯网络设置下的分类时间序列非参数回归模型。开发底层算法是为了提供一种灵活且简洁的表示，用于融合来自异构源的相关信息，可用于提高预测任务的性能并推断关键变量之间的因果关系。所提出的方法首先通过数值模拟进行说明，然后用两个真实世界的数据集进行验证：（1）从旋流稳定贫预混实验室规模燃烧器收集的实验数据，用于热声不稳定性检测；（2）用于因果推断的公开可用经济数据。
