Granger Causal Structure Reconstruction from Heterogeneous Multivariate Time Series

Yunfei Chu, Xiaowei Wang, Chunyan Feng, Jianxin Ma, Jingren Zhou, Hongxia Yang

TL;DR: We propose a network architecture that inductively reconstructs Granger causality via a prototypical Granger causal attention mechanism.

Abstract: Granger causal structure reconstruction is an emerging topic that can uncover 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 heterogeneous individuals with sharing commonalities, however there are ongoing concerns regarding its applicability in such large scale complex scenarios, presenting both challenges and opportunities for Granger causal reconstruction. To bridge this gap, we propose a Granger cAusal StructurE Reconstruction (GASER) framework for inductive Granger causality learning and common causal structure detection on heterogeneous multivariate time series. In particular, we address the problem through a novel attention mechanism, called prototypical Granger causal attention. Extensive experiments, as well as an online A/B test on an E-commercial advertising platform, demonstrate the superior performances of GASER.

Keywords: causal inference, Granger causality, time series, inductive, LSTM, attention