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Knowledge and Information Systems
Erschienen in: Knowledge and Information Systems 2/2019

05.09.2018 | Regular Paper

Similarity measures for time series data classification using grid representation and matrix distance

verfasst von: Yanqing Ye, Jiang Jiang, Bingfeng Ge, Yajie Dou, Kewei Yang

Erschienen in: Knowledge and Information Systems | Ausgabe 2/2019

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Abstract

Two similarity measures are proposed that can successfully capture both the numerical and point distribution characteristics of time series. More specifically, a novel grid representation for time series is first presented, with which a time series is segmented and compiled into a matrix format. Based on the proposed grid representation, two matrix matching algorithms, matrix-based Euclidean distance (GMED) and matrix-based dynamic time warping (GMDTW), are adapted to measure the similarity of matrix-like time series. Last, to assess the effectiveness of the proposed similarity measures, 1NN classification and K-means experiments are conducted using 22 online datasets from the UCR time series datasets Web site. In general, the results indicate that GMDTW measure is apparently superior to most current measures in accuracy, while the GMED can achieve much higher efficiency than dynamic time warping algorithm with equivalent performance. Furthermore, effects of the parameters in the proposed measures are analyzed and a way to determine the values of the parameters has been given.
Vorheriger Artikel Related families-based methods for updating reducts under dynamic object sets
Nächster Artikel Introducing time series chains: a new primitive for time series data mining

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Metadaten
Titel
Similarity measures for time series data classification using grid representation and matrix distance
verfasst von
Yanqing Ye
Jiang Jiang
Bingfeng Ge
Yajie Dou
Kewei Yang
Publikationsdatum
05.09.2018
Verlag
Springer London
Erschienen in
Knowledge and Information Systems / Ausgabe 2/2019
Print ISSN: 0219-1377
Elektronische ISSN: 0219-3116
DOI
https://doi.org/10.1007/s10115-018-1264-0

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