  We propose a new estimator to measure directed dependencies in time series.
The dimensionality of data is first reduced using a new non-uniform embedding
technique, where the variables are ranked according to a weighted sum of the
amount of new information and improvement of the prediction accuracy provided
by the variables. Then, using a greedy approach, the most informative subsets
are selected in an iterative way. The algorithm terminates, when the highest
ranked variable is not able to significantly improve the accuracy of the
prediction as compared to that obtained using the existing selected subsets. In
a simulation study, we compare our estimator to existing state-of-the-art
methods at different data lengths and directed dependencies strengths. It is
demonstrated that the proposed estimator has a significantly higher accuracy
than that of existing methods, especially for the difficult case, where the
data is highly correlated and coupled. Moreover, we show its false detection of
directed dependencies due to instantaneous couplings effect is lower than that
of existing measures. We also show applicability of the proposed estimator on
real intracranial electroencephalography data.
