Parallel Gradient Boosting based Granger Causality Learning

Abstract

Granger causality and its learning algorithms have been widely used in many disciplines to study cause-effect relationship among time series variables. In this paper, we address computing challenges of state-of-art Granger causality learning algorithms, specially when facing increasing dimensionality of available datasets. We study how to leverage gradient boosting meta machine learning techniques to achieve accurate causality discovery and big data parallel techniques for efficient causality discovery from large temporal datasets. We propose two main algorithms for gradient boosting based causality learning, and parallel gradient boosting based causality learning. Our experiments show our proposed algorithms can achieve efficient learning in distributed environments with good learning accuracy.