Time2Graph+: Bridging Time Series and Graph Representation Learning via Multiple Attentions

Abstract

Time series modeling has attracted great research interests in the last decades. Among the literature, shapelet-based models aim to extract representative subsequences, and could offer explanatory insights. In order to capture the shapelet dynamics and evolutions, we propose a novel framework of bridging time series representation learning and graph modeling, with two different implementations. We first formulate the process of extracting time-aware shapelets, then briefly introduce the key idea of transforming time series data into shapelet evolution graphs, to model the shapelet evolutionary patterns. A straightforward solution is to enumerate all possible shapelet transitions among adjacent time series segments, and apply a random-walk-based graph embedding algorithm to learn the time series representations (Time2Graph). We further extend Time2Graph by adopting graph attention mechanism to refine the procedure of modeling shapelet evolutions, namely Time2Graph+. Specifically, we transform each time series data into a unique and unweighted shapelet graph, and use GAT to automatically capture the correlations between shapelets. Experimental results show the significant improvements of Time2Graph+, and extensive observational analysis demonstrate the effectiveness and interpretability brought by attentions. Furthermore, the success of online deployment of Time2Graph+ model in State Grid of China validates the whole framework in the real-world application.