TY - GEN
T1 - Fast mining of a network of coevolving time series
AU - Cai, Yongjie
AU - Tong, Hanghang
AU - Fan, Wei
AU - Ji, Ping
N1 - Funding Information:
Acknowledgment: This material is supported by the National Science Foundation under Grant No. IIS1017415 and CNS-0904901, by the Army Research Laboratory under Cooperative Agreement Number W911NF-09-2-0053, by Defense Advanced Research Projects Agency (DARPA) under Contract Number W911NF-11-C-0200 and W911NF-12-C-0028, by National Institutes of Health under the grant number R01LM011986, Region II University Transportation Center under the project number 49997-33 25.
Publisher Copyright:
Copyright © SIAM.
PY - 2015
Y1 - 2015
N2 - 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.
AB - 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.
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U2 - 10.1137/1.9781611974010.34
DO - 10.1137/1.9781611974010.34
M3 - Conference contribution
AN - SCOPUS:84961905516
T3 - SIAM International Conference on Data Mining 2015, SDM 2015
SP - 298
EP - 306
BT - SIAM International Conference on Data Mining 2015, SDM 2015
A2 - Venkatasubramanian, Suresh
A2 - Ye, Jieping
PB - Society for Industrial and Applied Mathematics Publications
T2 - SIAM International Conference on Data Mining 2015, SDM 2015
Y2 - 30 April 2015 through 2 May 2015
ER -