Dimensionality reduction for multivariate time-series data mining

• Published in: The Journal of supercomputing Vol. 78; no. 7; pp. 9862 - 9878
• Main Authors: Wan, Xiaoji, Li, Hailin, Zhang, Liping, Wu, Yenchun Jim
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.05.2022; Springer Nature B.V
• Subjects: Algorithms; Central processing units; Compilers; Computer Science; Covariance matrix; CPUs; Data analysis; Data mining; Data Mining for IoT in Mobile Edge computing; Information management; Interpreters; Multivariate analysis; Principal components analysis; Processor Architectures; Programming Languages; Reduction; Sequences; Time series; Piecewise representation; Time-series data mining; Covariance matrix; Principal component analysis
• ISSN: 0920-8542; 1573-0484
• DOI: 10.1007/s11227-021-04303-4

A multivariate time series is one of the most important objects of research in data mining. Time and variables are two of its distinctive characteristics that add the complication of the algorithms applied to data mining. Reduction in the dimensionality is often regarded as an effective way to address these issues. In this paper, we propose a method based on principal component analysis (PCA) to effectively reduce the dimensionality. We call it “piecewise representation based on PCA” (PPCA), which segments multivariate time series into several sequences, calculates the covariance matrix for each of them in terms of the variables, and employs PCA to obtain the principal components in an average covariance matrix. The results of the experiments, including retained information analysis, classification, and a comparison of the central processing unit time consumption, demonstrate that the PPCA method used to reduce the dimensionality in multivariate time series is superior to the prior methods.