Abstract
Subspace classification of multivariate time series (MTS) data is currently a challenging problem, due to the difficulties in defining a meaningful similarity measure for distinguishing between the series features. In this paper, a new representation model called Sequence-As-Feature (SAF) is proposed, where each MTS in the new representation is a vector of sequential attributes, each being an univariate time series, such that the common similarity measures can be readily adapted. An attribute-weighted measure is then defined using a conditional probability distribution (CPD) modeling method. Based on these, a prototype classifier is derived to classify the test MTS in a linear time complexity, with the MTS prototype optimized according to the CPD model of sequential attributes. Experimental results on MTS data sets from three real-world domains are given to demonstrate the performance of the proposed methods.
Supported by the National Natural Science Foundation of China under Grant No. 61672157, and the Program of Probability and Statistics: Theory and Application (No. IRTL1704).
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Yuan, L., Chen, L., Xie, R., Hsu, H. (2018). Sequence-As-Feature Representation for Subspace Classification of Multivariate Time Series. In: U, L., Xie, H. (eds) Web and Big Data. APWeb-WAIM 2018. Lecture Notes in Computer Science(), vol 11268. Springer, Cham. https://doi.org/10.1007/978-3-030-01298-4_4
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DOI: https://doi.org/10.1007/978-3-030-01298-4_4
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