Early classification on multivariate time series

Multivariate time series (MTS) classification is an important topic in time series data mining, and has attracted great interest in recent years. However, early classification on MTS data largely remains a challenging problem. To address this problem without sacrificing the classification performance, we focus on discovering hidden knowledge from the data for early classification in an explainable way. At first, we introduce a method MCFEC (Mining Core Feature for Early Classification) to obtain distinctive and early shapelets as core features of each variable independently. Then, two methods are introduced for early classification on MTS based on core features. Experimental results on both synthetic and real-world datasets clearly show that our proposed methods can achieve effective early classification on MTS.