Temporal Feature Selection on Networked Time Series

TL;DR

This paper introduces a network regularized feature selection method for networked time series data, effectively capturing both temporal patterns and social relationships to improve classification accuracy.

Contribution

It proposes the NetRLS model that integrates network information into time series feature selection, addressing the limitations of i.i.d. assumptions in existing methods.

Findings

NetRLS outperforms state-of-the-art methods on Twitter and DBLP data.

Incorporating network data improves feature selection for social networked time series.

Experimental results demonstrate enhanced classification performance.

Abstract

This paper formulates the problem of learning discriminative features (\textit{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 \emph{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.