Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/35671
Title: A feature extraction method for multivariate time series classification using temporal patterns
Authors: Zhou, PY
Chan, KCC 
Keywords: Multivariate time series
Time series classification
Intra-temporal patterns
Inter-temporal pattern
Issue Date: 2015
Publisher: Springer
Source: In T Cao, EP Lim, ZH Zhou, TB Ho, D Cheung & H Motoda (Eds.), Advances in knowledge discovery and data mining : 19th Pacific-Asia Conference, PAKDD 2015, Ho Chi Minh City, Vietnam, May 19-22, 2015, proceedings, p. 409-421. Cham : Springer, 2015 How to cite?
Series/Report no.: Lecture notes in computer science ; v. 9078
Abstract: Multiple variables and high dimensions are two main challenges for classification of Multivariate Time Series (MTS) data. In order to overcome these challenges, feature extraction should be performed before performing classification. However, the existing feature extraction methods lose the important correlations among the variables while reducing high dimensions of MTS. Hence, in this paper, we propose a new feature extraction method combined with different classifiers to provide a general classification strategy for MTS data which can be applied for different area problems of MTS data. The proposed algorithm can handle data of high feature dimensions efficiently with unequal length and discover the relationship within the same and between different component univariate time series for MTS data. Hence, the proposed feature extraction method is application-independent and therefore does not depend on domain knowledge of relevant features or assumption about underling data models. We evaluate the algorithm on one synthetic dataset and two real-world datasets. The comparison experimental result shows that the proposed algorithm can achieve higher classification accuracy and F-measure value.
URI: http://hdl.handle.net/10397/35671
ISBN: 978-3-319-18032-8
978-3-319-18031-1
ISSN: 0302-9743
DOI: 10.1007/978-3-319-18032-8_32
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