### 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 language | English (US) |
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Title of host publication | SIAM International Conference on Data Mining 2015, SDM 2015 |

Publisher | Society for Industrial and Applied Mathematics Publications |

Pages | 298-306 |

Number of pages | 9 |

ISBN (Print) | 9781510811522 |

State | Published - 2015 |

Event | SIAM International Conference on Data Mining 2015, SDM 2015 - Vancouver, Canada Duration: Apr 30 2015 → May 2 2015 |

### Other

Other | SIAM International Conference on Data Mining 2015, SDM 2015 |
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Country | Canada |

City | Vancouver |

Period | 4/30/15 → 5/2/15 |

### Fingerprint

### ASJC Scopus subject areas

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

### Cite this

*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.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

*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.

}

TY - GEN

T1 - Fast mining of a network of coevolving time series

AU - Cai, Yongjie

AU - Tong, Hanghang

AU - Fan, Wei

AU - Ji, Ping

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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M3 - Conference contribution

SN - 9781510811522

SP - 298

EP - 306

BT - SIAM International Conference on Data Mining 2015, SDM 2015

PB - Society for Industrial and Applied Mathematics Publications

ER -