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The main motivation of this paper is to introduce the permutationJensen-Shannon distance, a symbolic tool able to quantify the degree ofsimilarity between two arbitrary time series. This quantifier results from thefusion of two concepts, the Jensen-Shannon divergence and the encoding schemebased on the sequential ordering of the elements in the data series. Theversatility and robustness of this ordinal symbolic distance for characterizingand discriminating different dynamics are illustrated through several numericaland experimental applications. Results obtained allow us to be optimistic aboutits usefulness in the field of complex time series analysis. Moreover, thanksto its simplicity, low computational cost, wide applicability and lesssusceptibility to outliers and artifacts, this ordinal measure can efficientlyhandle large amounts of data and help to tackle the current big datachallenges.

Permutation Jensen-Shannon distance: A versatile and fast symbolic tool for complex time series analysis