Keywords:

Abstract:

Time series are ubiquitous, and a measure to assess their similarity is a core part of many computational systems. Classical time series similarity measures based on dynamic time warping or the edit distance remain as the best general-purpose options, but their accuracies still leave much room for improvement. Here we propose a new approach to time series similarity based on the costs of iteratively jumping (or moving) between the sample values of two time series. We show that this approach can be very competitive when compared against the aforementioned classical measures. In fact, extensive experiments show that it can be statistically significantly superior for a number of data sources. Since the approach is also intuitive and computationally simple, we foresee its application as an alternative off-the-shelf tool to be used in time series retrieval, clustering, or classification systems.

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