Dimensionality reduction for multivariate time-series data mining

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

*此作品的通信作者

研究成果: 雜誌貢獻期刊論文同行評審

摘要

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.

原文英語
頁(從 - 到)9862-9878
頁數17
期刊Journal of Supercomputing
78
發行號7
DOIs
出版狀態已發佈 - 2022 5月

ASJC Scopus subject areas

  • 軟體
  • 理論電腦科學
  • 資訊系統
  • 硬體和架構

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