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

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...

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Bibliographic Details
Published in:Труды Института системного программирования РАН
Main Authors: I. S. Alimova, V. D. Solovyev, I. Z. Batyrshin
Format: Article
Language:English
Published: Russian Academy of Sciences, Ivannikov Institute for System Programming 2018-10-01
Subjects:
временной ряд
классификация
мера сходства
сап трансформ
преобразование скользящих аппроксимаций
Online Access:https://ispranproceedings.elpub.ru/jour/article/view/214
Description
Summary: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.
ISSN:2079-8156
2220-6426

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