Proceedings of ISP RAS


Comparative analysis of the similarity measures based on the moving approximation transformation in problems of time series classification

I.S. Alimova (KFU, Kazan, Russia)
V.D. Solovyev (KFU, Kazan, Russia)
I.Z. Batyrshin (Instituto Politecnico Nacional, Mexico, Mexico)

Abstract

One of the major issues dealing with time-series classification problem is the choice of similarity measure. This article presents a comparative analysis of the similarity measure for time series based on moving approximations transform (MAP transforms) with other two most useful measures: Algorithm Dynamic Transformation and Euclidean distance for  classification task. In addition, algorithm, that improves the precision of the measure for time series, that have similar values, but shifted relative to each other on the axis X, where coordinate on the X axis represents the time unit, is proposed.

Keywords

time series, classification, similarity measure, MAP transform, Moving Approximation Transform

Edition

Proceedings of the Institute for System Programming, vol. 28, issue 6, 2016, pp. 207-222.

ISSN 2220-6426 (Online), ISSN 2079-8156 (Print).

DOI: 10.15514/ISPRAS-2016-28(6)-15

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