A feature-based information-theoretic approach for detecting interpretable, long-timescale pairwise interactions from time series
基于特征与信息论识别可解释的长时间尺度序列交互
Quantifying relationships between components of a complex system is critical to understanding the rich network of interactions that characterize the behavior of the system. Traditional methods for detecting pairwise dependence of time series, such as Pearson correlation, Granger causality, and mutual information, are computed directly in the space of measured time-series values. But for systems in which interactions are mediated by statistical properties of the time series…