Granger Causality Analysis in Irregular Time Series

Learning temporal causal structures between time series is one of the key tools for analyzing time series data. In many real-world applications, we are confronted with Irregular Time Series, whose observations are not sampled at equally-spaced time stamps. The irregularity in sampling intervals violates the basic assumptions behind many models for structure learning. In this paper, we propose a nonparametric generalization of the Granger graphical models called Generalized Lasso Granger (GLG) to uncover the temporal dependencies from irregular time series. Via theoretical analysis and extensive experiments, we verify the effectiveness of our model. Furthermore, we apply GLG to the application dataset of δO isotope of Oxygen records in Asia and achieve promising results to discover the moisture transportation patterns in a 800-year period.