Journal
NEUROCOMPUTING
Volume 149, Issue -, Pages 777-787Publisher
ELSEVIER
DOI: 10.1016/j.neucom.2014.07.056
Keywords
Multivariate time series; Early classification; Feature selection
Categories
Funding
- Natural Science Foundation of Hubei Province of China [2011CDB462]
- National Natural Science Foundation of China [61272275, 61170026]
- National High Technology Research and Development Program of China [2013AA12A206]
- Key Technologies R&D Program of Wuhan [201212521826]
- Program of Introducing Talents of Discipline to Universities [B07037]
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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. (C) 2014 Elsevier B.V. All rights reserved.
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