Granulation-based symbolic representation of time series and semi-supervised classification

Authors:
Jun Meng;LiXia Wu;XiuKun Wang;TsauYoung Lin
Affiliations:
School of Computer Science and Technology, Dalian University of Technology, Dalian 116023, China and Department of Computer Science, San Jose State University, San Jose, CA 95192, USA;School of Computer Science and Technology, Dalian University of Technology, Dalian 116023, China;School of Computer Science and Technology, Dalian University of Technology, Dalian 116023, China;Department of Computer Science, San Jose State University, San Jose, CA 95192, USA
Venue:
Computers & Mathematics with Applications
Year:
2011

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Abstract

We present a semi-supervised time series classification method based on co-training which uses the hidden Markov model (HMM) and one nearest neighbor (1-NN) as two learners. For modeling time series effectively, the symbolization of time series is required and a new granulation-based symbolic representation method is proposed in this paper. First, a granule for each segment of time series is constructed, and then the segments are clustered by spectral clustering applied to the formed similarity matrix. Using four time series datasets from UCR Time Series Data Mining Archive, the experimental results show that proposed symbolic representation works successfully for HMM. Compared with the supervised method, the semi-supervised method can construct accurate classifiers with very little labeled data available.