Efficient computation and statistical assessment of transfer entropy

The analysis of complex systems frequently poses the challenge to distinguish correlation from causation. Statistical physics has inspired very promising approaches to search for correlations in time series; the transfer entropy in particular (Hlavackova-Schindler et al., 2007). Now, methods from computational statistics can quantitatively assign significance to such correlation measures. In this study, we propose and apply a procedure to statistically assess transfer entropies by one-sided tests. We introduce to null models of vanishing correlations for time series with memory. We implemented them in an OpenMP-based, parallelized C++ package for multi-core CPUs. Using template meta-programming, we enable a compromise between memory and run time efficiency.