Temporal feature selection on networked time series

Haishuai Wang, Jia Wu, Peng Zhang, Chengqi Zhang

Research output: Contribution to journalArticlepeer-review


This paper formulates the problem of learning discriminative features (i.e., segments) from networked time series data considering the linked information among time series. For example, social network users are considered to be social sensors that continuously generate social signals (tweets) represented as a time series. The discriminative segments are often referred to as shapelets in a time series. Extracting shapelets for time series classification has been widely studied. However, existing works on shapelet selection assume that the time series are independent and identically distributed (i.i.d.). This assumption restricts their applications to social networked time series analysis, since a user's actions can be correlated to his/her social affiliations. In this paper we propose a new Network Regularized Least Squares (NetRLS) feature selection model that combines typical time series data and user network data for analysis. Experiments on real-world networked time series Twitter and DBLP data demonstrate the performance of the proposed method. NetRLS performs better than LTS, the state-of-the-art time series feature selection approach, on real-world data.
Original languageEnglish
Number of pages7
JournalarXiv.org e-Print archive
Publication statusSubmitted - 22 Dec 2016
Externally publishedYes


Dive into the research topics of 'Temporal feature selection on networked time series'. Together they form a unique fingerprint.

Cite this