Journal
IEEE ACCESS
Volume 9, Issue -, Pages 88674-88684Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/ACCESS.2021.3089839
Keywords
Redundancy; Time series analysis; Time measurement; Principal component analysis; Training; Linear discriminant analysis; Economics; Linear discriminant analysis; dimension reduction; classification; multivariate time series; matrix data
Categories
Funding
- National Natural Science Foundation of China [11761076, 62066023]
- Science Foundation of Yunnan [2019FB002, 2021Y554]
- Small Project Foundation of Yunnan University of Finance and Economics (YNUFE) [2021YUFEYC082]
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This paper proposes a new method for MTS classification based on bidirectional linear discriminant analysis (BLDA), which can utilize label information to reduce redundancy in both time and variable modes simultaneously. The performance of the proposed method is demonstrated through experiments on real MTS datasets.
Multivariate time series (MTS) is a kind of matrix data, typically consisting of multiple variables measured at multiple time points. Due to the high dimensionality of MTS data, many methods for MTS classification have been proposed within the literature to reduce the redundancy in time or variable mode, but there is relatively little work on exploring the redundancy in both modes concurrently. In this paper we propose a new method for MTS classification based on bidirectional linear discriminant analysis (BLDA). The advantage is that BLDA can utilize label information in reducing the redundancy in time and variable modes simultaneously. Moreover, the existing procedures for BLDA suffer from two problems: (i) BLDA cannot be performed when one of the within-class matrices is singular; (ii) the computational burden could be very heavy when one of the data dimensionality is high. A new procedure for BLDA based on pseudo-inverse (PBLDA) and an efficient algorithm for PBLDA are proposed in this paper to overcome the two problems. The performance of our proposed method is illustrated through the experiments on a number of real MTS datasets.
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