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Open AccessArticle

A Metric Learning-Based Univariate Time Series Classification Method

1
College of Computer Science and Technology, Harbin Engineering University, Harbin 150000, China
2
Department of Information Engineering, Hulunbuir Vocational Technical College, HulunBuir 021000, China
*
Author to whom correspondence should be addressed.
Information 2020, 11(6), 288; https://doi.org/10.3390/info11060288
Received: 6 May 2020 / Revised: 22 May 2020 / Accepted: 25 May 2020 / Published: 28 May 2020
(This article belongs to the Section Information Applications)
High-dimensional time series classification is a serious problem. A similarity measure based on distance is one of the methods for time series classification. This paper proposes a metric learning-based univariate time series classification method (ML-UTSC), which uses a Mahalanobis matrix on metric learning to calculate the local distance between multivariate time series and combines Dynamic Time Warping(DTW) and the nearest neighbor classification to achieve the final classification. In this method, the features of the univariate time series are presented as multivariate time series data with a mean value, variance, and slope. Next, a three-dimensional Mahalanobis matrix is obtained based on metric learning in the data. The time series is divided into segments of equal intervals to enable the Mahalanobis matrix to more accurately describe the features of the time series data. Compared with the most effective measurement method, the related experimental results show that our proposed algorithm has a lower classification error rate in most of the test datasets. View Full-Text
Keywords: Mahalanobis; metric learning; multivariable; time series; univariate Mahalanobis; metric learning; multivariable; time series; univariate
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Song, K.; Wang, N.; Wang, H. A Metric Learning-Based Univariate Time Series Classification Method. Information 2020, 11, 288.

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