Dimensionality reduction for multivariate time-series data mining

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 addre...

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Bibliographic Details
Published inThe Journal of supercomputing Vol. 78; no. 7; pp. 9862 - 9878
Main Authors Wan, Xiaoji, Li, Hailin, Zhang, Liping, Wu, Yenchun Jim
Format Journal Article
LanguageEnglish
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
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Summary: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.
ISSN:0920-8542
1573-0484
DOI:10.1007/s11227-021-04303-4