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 [1]. 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.
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