  Time lag between variables is a key characteristics of dynamical systems in
different fields and identifying such time lag is an important problem in
complex systems with many applications. Transfer Entropy (TE) was proposed as a
tool for time lag identification recently. Unfortunately, estimating TE has
been a notoriously difficult problem. Copula Entropy (CE) is a measure of
statistical independence and it was proved that TE can be represented with only
CE. Therefore, a non-parametric estimator of TE based on CE was proposed
according to such representation recently. In this paper we propose to use the
CE-based estimator of TE to identify time lag in dynamical systems. Both
simulated and real data are used to verify the effectiveness of the proposed
method in the experiments. Experimental results show that the proposed method
can identify the time lags in the four simulated systems. The real data
experiment with the data on power consumption of the Tetouan city also
demonstrates that our method can identify the pattern of time lags through the
estimated TE from the weather factors to the power consumption of the city.
