Granger Causality for Heterogeneous Processes

摘要：

　　Discovery of temporal structures and finding causal interactions among time series have recently attracted attention of the data mining community.Among various causal notions graphical Granger causality is well-known due to its intuitive interpretation and computational simplicity.Most of the current graphical approaches are designed for homogeneous datasets i.e.the interacting processes are assumed to have the same data distribution.Since many applications generate heterogeneous time series,the question arises how to leverage graphical Granger models to detect temporal causal dependencies among them.Profiting from the generalized linear models,we propose an efficient Heterogeneous Graphical Granger Model(HGGM)for detecting causal relation among time series having a distribution from the exponential family which includes a wider common distributions e.g.Poisson,gamma.To guarantee the consistency of our algorithm we employ adaptive Lasso as a variable selection method.Extensive experiments on synthetic and real data confirm the effectiveness and efficiency of HGGM.

作者: Sahar Behzadi Kate(r)ina Hlavá(c)ková-Schindler Claudia Plant

作者单位: Faculty of Computer Science,Data Mining,University of Vienna,Vienna,Austria Faculty of Computer Science,Data Mining,University of Vienna,Vienna,Austria;ds:UniVie,University of

会议类型: 国际会议

会议名称: The 23rd Pacific-Asia Conference on Knowledge Discovery and Data Mining (第23届亚太知识发现和数据挖掘国际会议(PAKDD2019)

会议地点: 澳门

会议语种:英文

页码: 463-475

在线出版日期: 2019-04-14（万方平台首次上网日期，不代表论文的发表时间）