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Welcome to the Granger-causality wiki!

This page contains the codes for learning the Granger causality in different settings. The codes are written in Matlab and depend on the GLMnet package for performing Lasso.

Lasso-Granger

Lasso-Granger is an efficient algorithm for learning the temporal dependency among multiple time series based on variable selection using Lasso.

Paper: A. Arnold, Y. Liu, and N. Abe. Temporal causal modeling with graphical granger methods. In KDD, 2007.

Code: lassoGranger.m

Copula-Granger

Copula-Granger extends the power of Lasso-Granger to non-linear datasets. It uses the copula technique to separate the marginal properties of the joint distribution from its dependency structure.

Paper: Y. Liu, M. T. Bahadori, and H. Li, Sparse-GEV: Sparse Latent Space Model for Multivariate Extreme Value Time Series Modeling, ICML 2012.

Code: copulaGranger.m

Granger Causality for Irregular Time Series

The Generalized Lasso Granger is designed to discover the Granger causality relationship among irregular time series; times series whose samples are not recorded on regularly spaced timestamps.

Paper: M. T. Bahadori and Yan Liu, Granger Causality Analysis in Irregular Time Series, SDM 2012.

Code: iLasso.m