A STIMULI-RELEVANT DIRECTED DEPENDENCY INDEX FOR TIME SERIES

Transfer entropy can to a certain degree assess the direction in addition to the strength of the couplings within dynamic time series. The greater the transfer entropy, the greater the strength of the dependency between time series. In this work, we are interested in quantifying the effect that a given time series (e.g., an external stimuli) has upon the coupling strength between other time series. Towards that end, we define a directed dependency index based on the difference of two causally conditioned transfer entropies. We then provide a lower bound for the dependency index, and demonstrate on synthetic data that this lower bound can be efficiently computed