基于贝叶斯网络的时间序列因果关系学习

贝叶斯网络是研究变量之间因果关系的有力工具，基于贝叶斯网络的因果关系学习包括结构学习与参数学习两部分，其中，结构学习是核心.目前，贝叶斯网络主要用于发现非时间序列数据中所蕴含的因果关系（非时间序列因果关系），从数据中学习得到的也均是一般变量之间的因果关系.针对这些情况，结合时间序列预处理、时间序列变量排序、转换数据集构建和局部贪婪打分-搜索等进行时间序列的因果关系学习；再将包括分段在内的时间序列预处理、时间序列段的因果关系结构学习、因果关系结构数据集构建、因果关系变量排序和局部贪婪打分-搜索等相结合，来进行元因果关系（因果关系变量之间的因果关系）学习，从而实现两个层次的时间序列因果关系学习，为进一步的量化因果分析奠定了基础.分别使用模拟、UCI和金融时间序列数据进行实验与分析，实验结果显示，基于贝叶斯网络能够有效地进行时间序列的因果关系和元因果关系学习.

Learning Causal Relationship from Time Series Based on Bayesian Network

Bayesian network is a powerful tool for studying the causal relationship between variables. Causal learning, based on Bayesian network, consists of two parts:structure learning and parameter learning, while structural learning is the core of causal learning. At present, Bayesian network is mainly used to discover the causality in non-time series data (non-time series causality) and what is learned from the data is the causal relationship between general variables. In this study, the causality of time series is learned by time series preconditioning, time series variable sorting, construction of transformation data set, local greedy search-scoring, and so on. Combining the time series preconditioning including segmentation, the structure learning of causal relationship for time series segments, the construction of causality structure data set, the variable sorting of causal relationship, local greedy search-scoring, maximum likelihood parameter estimation, etc., meta causal relationship (used to study the randomness of causal relationship) is established. Thus, two levels of causality learning can be realized, and the foundation is laid for further quantitative causal analysis. Experiments and analyses are carried out by using simulation, UCI, and finance time series, the results verify the validity, reliability, and practicability of learning causal relationship and Meta causality based on Bayesian network.