Fast mining of a network of coevolving time series

Yongjie Cai, Hanghang Tong, Wei Fan, Ping Ji

Research output: Chapter in Book/Report/Conference proceedingConference contribution

13 Citations (Scopus)

Abstract

Coevolving multiple time series are ubiquitous and naturally appear in a variety of high-impact applications, ranging from environmental monitoring, computer network traffic monitoring, motion capture, to physiological signal in health care and many more. In many scenarios, the multiple time series data is often accompanied by some contextual information in the form of networks. In this paper, we refer to such multiple time series, together with its embedded network as a network of coevolving time series. In order to unveil the underlying patterns of a network of coevolving time series, we propose DCMF, a dynamic contextual matrix factorization algorithm. The key idea is to find the latent factor representation of the input time series and that of its embedded network simultaneously. Our experimental results on several real datasets demonstrate that our method (1) outperforms its competitors, especially when there are lots of missing values; and (2) enjoys a linear scalability w.r.t. the length of time series.

Original languageEnglish (US)
Title of host publicationSIAM International Conference on Data Mining 2015, SDM 2015
PublisherSociety for Industrial and Applied Mathematics Publications
Pages298-306
Number of pages9
ISBN (Print)9781510811522
StatePublished - 2015
EventSIAM International Conference on Data Mining 2015, SDM 2015 - Vancouver, Canada
Duration: Apr 30 2015May 2 2015

Other

OtherSIAM International Conference on Data Mining 2015, SDM 2015
CountryCanada
CityVancouver
Period4/30/155/2/15

Fingerprint

Time series
Monitoring
Computer networks
Factorization
Health care
Scalability

ASJC Scopus subject areas

  • Computational Theory and Mathematics
  • Computer Vision and Pattern Recognition
  • Software

Cite this

Cai, Y., Tong, H., Fan, W., & Ji, P. (2015). Fast mining of a network of coevolving time series. In SIAM International Conference on Data Mining 2015, SDM 2015 (pp. 298-306). Society for Industrial and Applied Mathematics Publications.

Fast mining of a network of coevolving time series. / Cai, Yongjie; Tong, Hanghang; Fan, Wei; Ji, Ping.

SIAM International Conference on Data Mining 2015, SDM 2015. Society for Industrial and Applied Mathematics Publications, 2015. p. 298-306.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Cai, Y, Tong, H, Fan, W & Ji, P 2015, Fast mining of a network of coevolving time series. in SIAM International Conference on Data Mining 2015, SDM 2015. Society for Industrial and Applied Mathematics Publications, pp. 298-306, SIAM International Conference on Data Mining 2015, SDM 2015, Vancouver, Canada, 4/30/15.
Cai Y, Tong H, Fan W, Ji P. Fast mining of a network of coevolving time series. In SIAM International Conference on Data Mining 2015, SDM 2015. Society for Industrial and Applied Mathematics Publications. 2015. p. 298-306
Cai, Yongjie ; Tong, Hanghang ; Fan, Wei ; Ji, Ping. / Fast mining of a network of coevolving time series. SIAM International Conference on Data Mining 2015, SDM 2015. Society for Industrial and Applied Mathematics Publications, 2015. pp. 298-306
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