TY - GEN
T1 - A feature extraction method for multivariate time series classification using temporal patterns
AU - Zhou, Pei Yuan
AU - Chan, Chun Chung
PY - 2015/1/1
Y1 - 2015/1/1
N2 - 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.
AB - 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.
KW - Inter-temporal pattern
KW - Intra-temporal patterns
KW - Multivariate time series
KW - Time series classification
UR - http://www.scopus.com/inward/record.url?scp=84945545347&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-18032-8_32
DO - 10.1007/978-3-319-18032-8_32
M3 - Conference article published in proceeding or book
SN - 9783319180311
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 409
EP - 421
BT - Advances in Knowledge Discovery and Data Mining - 19th Pacific-Asia Conference, PAKDD 2015, Proceedings
PB - Springer Verlag
T2 - 19th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2015
Y2 - 19 May 2015 through 22 May 2015
ER -