A feature-based information-theoretic method for detecting interpretable, long-timescale pairwise interactions from time series.
Original Paper 📕
Nguyen et al., Physical Review Research (2025). "A feature-based information-theoretic approach for detecting interpretable, long-timescale pairwise interactions from time series". Link.
This figure illustrates our method for detecting cases in which a target process, Y , is influenced by a statistical property of a recent time window of a source process, X. We contrast it to conventional MI, estimated directly from the signal-space of the variables, which we denote as MIs. (a) MIs is computed based on the observed time-series values of process X and Y. (b) MIf iterates through time-series segments of length l of process X and reduces each window to a single real-valued summary statistic zt. MI is then computed between feature variable Zt and the target variable Yt+1.
Public code repository (R)
R code is available , which includes code for implementing our method and for reproducing all results in our paper
