Dimensionality reduction for multivariate time-series data mining

Xiaoji Wan, Hailin Li, Liping Zhang, Yenchun Jim Wu*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

6 Citations (Scopus)

Abstract

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.

Original languageEnglish
Pages (from-to)9862-9878
Number of pages17
JournalJournal of Supercomputing
Volume78
Issue number7
DOIs
Publication statusPublished - 2022 May

Keywords

  • Covariance matrix
  • Piecewise representation
  • Principal component analysis
  • Time-series data mining

ASJC Scopus subject areas

  • Software
  • Theoretical Computer Science
  • Information Systems
  • Hardware and Architecture

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