. The aim of this study is to evaluate some of
the most commonly used mutual information estimators, i.e.
estimators based on histograms (with fixed or adaptive bin size),
k-nearest neighbors and kernels. We assess the accuracy of the
estimators by Monte-Carlo simulations on time series from
nonlinear dynamical systems of varying complexity. As the true
mutual information is generally unknown, we investigate the
existence and rate of consistency of the estimators (convergence
to a stable value with the increase of time series length), and
the degree of deviation among the estimators. The results show
that the k-nearest neighbor estimator is the most stable and
less affected by the method-specific parameter.
Key words: Mutual Information, Time series
