  Here, we propose a new tool to estimate the complexity of a time series: the
entropy of difference (ED). The method is based solely on the sign of the
difference between neighboring values in a time series. This makes it possible
to describe the signal as efficiently as prior proposed parameters such as
permutation entropy (PE) or modified permutation entropy (mPE), but (1) reduces
the size of the sample that is necessary to estimate the parameter value, and
(2) enables the use of the Kullback-Leibler divergence to estimate the distance
between the time series data and random signals.
