Learning Shapelet Patterns from Network-Based Time Series

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 represented as a time series. The discriminative segments are often referred to as shapelets in a time series. Extracting shapelets for time-series analysis has been widely studied. However, existing works on shapelet selection assume that the time series are independent and identically distributed. 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 novel network regularized least squares (NetRLS) feature selection model that combines typical time-series data and user network data for analysis. Experiments on real-world Twitter, Weibo, and DBLP networked time-series data demonstrate the performance of the proposed method. NetRLS performs better than the representative baselines on four evaluation criteria, namely classification accuracy, area under the curve (AUC), F1-score, and statistical significance analysis. NetRLS also has competitive running time as the baselines.