A feature extraction method for multivariate time series classification using temporal patterns

Pei Yuan Zhou, Chun Chung Chan

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

11 Citations (Scopus)


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.
Original languageEnglish
Title of host publicationAdvances in Knowledge Discovery and Data Mining - 19th Pacific-Asia Conference, PAKDD 2015, Proceedings
PublisherSpringer Verlag
Number of pages13
ISBN (Print)9783319180311
Publication statusPublished - 1 Jan 2015
Event19th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2015 - Ho Chi Minh City, Viet Nam
Duration: 19 May 201522 May 2015

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference19th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2015
Country/TerritoryViet Nam
CityHo Chi Minh City


  • Inter-temporal pattern
  • Intra-temporal patterns
  • Multivariate time series
  • Time series classification

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

Cite this