Granger causal modeling is an emerging topic that can uncover Granger causal relationship behind multivariate time series data. In many real-world systems, it is common to encounter a large amount of multivariate time series data collected from different individuals with sharing commonalities. However, there are ongoing concerns regarding Granger causality's applicability in such large scale complex scenarios, presenting both challenges and opportunities for Granger causal structure reconstruction. Existing methods usually train a distinct model for each individual, suffering from inefficiency and over-fitting issues. To bridge this gap, we propose an Inductive GRanger cAusal modeling (InGRA) framework for inductive Granger causality learning and common causal structure detection on multivariate time series, which exploits the shared commonalities underlying the different individuals. In particular, we train one global model for individuals with different Granger causal structures through a novel attention mechanism, called prototypical Granger causal attention. The model can detect common causal structures for different individuals and infer Granger causal structures for newly arrived individuals. Extensive experiments, as well as an online A/B test on an E-commercial advertising platform, demonstrate the superior performances of InGRA.
翻译:Granger causaal模型框架(InGRAA)用于引入Granger 因果关系学习和在多变时间序列上进行常见的A/B测试,以展示不同个人的共同性能,特别是我们通过新关注机制为不同Granger因果结构的个人培训一种全球模型,其中要求注意原型Granger因果因素。该模型可以发现不同个人的共同因果结构,并为新抵达的个人进行推推引Granger因果结构。广泛实验以及电子商业广告平台的在线A/B测试,展示了InGRA的高级性能。