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Averaging methods using dynamic time warping for time series classification

conference contribution
posted on 2020-12-01, 00:00 authored by S Datta, Chandan KarmakarChandan Karmakar, M Palaniswami
© 2020 IEEE. Averaging is an important step in time series classi-fication or clustering, to create representative sequences for each category of data. A global averaging method for Dynamic Time Warping (DTW) based time series analysis is DTW Barycenter Averaging (DBA). In this paper, we propose a recursive tree based implementation of DBA, for faster computation of an average sequence, using the divide-and-conquer strategy. We also propose to automate the termination of DBA using a data-driven approach. The performance of the proposed methods is evaluated using accuracy, precision and recall as performance metrics, in a simple DTW-distance based classification method on ten standard time series datasets. Experimental results demonstrate that the proposed approaches are significantly faster than DBA, while achieving similar performance.

History

Pagination

2794-2798

Location

Online from Canberra, Australia

Start date

2020-12-01

End date

2020-12-04

ISBN-13

9781728125473

Language

eng

Publication classification

E1 Full written paper - refereed

Editor/Contributor(s)

[Unknown]

Title of proceedings

SSCI 2020 : Proceedings of the 2020 IEEE Symposium Series on Computational Intelligence

Event

IEEE Computational Intelligence Society. Symposium (2020 : Online from Canberra, Australia)

Publisher

Institute of Electrical and Electronics Engineers

Place of publication

Piscataway, N.J.

Series

IEEE Computational Intelligence Society Symposium

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