A Competitive Measure to Assess the Similarity between Two Time Series
Time series are ubiquitous, and a measure to assess their similarity is a core part of many systems, including case-based reasoning systems. Although several proposals have been made, still the more robust and reliable time series similarity measures are the classical ones, introduced long time ago. In this paper 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 computationally simple, we foresee its application as an alternative off-the-shelf tool to be used in many case-based reasoning systems dealing with time series.
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