Detecting Long-Timescale Pairwise Interactions Between Time Series Using Feature-Based Information Theory

A feature-based information-theoretic approach can outperform traditional signal-based methods in detecting long-timescale pairwise interactions between time series, especially in scenarios with short time-series lengths, high noise levels, and long interaction timescales.