  The main motivation of this paper is to introduce the permutation
Jensen-Shannon distance, a symbolic tool able to quantify the degree of
similarity between two arbitrary time series. This quantifier results from the
fusion of two concepts, the Jensen-Shannon divergence and the encoding scheme
based on the sequential ordering of the elements in the data series. The
versatility and robustness of this ordinal symbolic distance for characterizing
and discriminating different dynamics are illustrated through several numerical
and experimental applications. Results obtained allow us to be optimistic about
its usefulness in the field of complex time series analysis. Moreover, thanks
to its simplicity, low computational cost, wide applicability and less
susceptibility to outliers and artifacts, this ordinal measure can efficiently
handle large amounts of data and help to tackle the current big data
challenges.
