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.
Citation:
I. S. Alimova, V. D. Solovyev, I. Z. Batyrshin, “Comparative analysis of the similarity measures based on the moving approximation transformation in problems of time series classification”, Proceedings of ISP RAS, 28:6 (2016), 207–222
\Bibitem{AliSolBat16}
\by I.~S.~Alimova, V.~D.~Solovyev, I.~Z.~Batyrshin
\paper Comparative analysis of the similarity measures based on the moving approximation transformation in problems of time series classification
\jour Proceedings of ISP RAS
\yr 2016
\vol 28
\issue 6
\pages 207--222
\mathnet{http://mi.mathnet.ru/tisp95}
\crossref{https://doi.org/10.15514/ISPRAS-2016-28(6)-15}
\elib{https://elibrary.ru/item.asp?id=27679181}
Linking options:
https://www.mathnet.ru/eng/tisp95
https://www.mathnet.ru/eng/tisp/v28/i6/p207
This publication is cited in the following 1 articles:
E D Emtseva, P F Kiku, A L Mazelis, “ASSESSMENT OF TEMPORAL TRENDS OF MALIGNANT NEOPLASMS INCIDENCE USING MULTIVARIABLE STATISTICAL ANALYSIS”, Ekologiya cheloveka (Human Ecology), 26:2 (2019), 45
Citation in format AMSBIB
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