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Variable grouping in multivariate time series via correlation.

A Tucker1, S Swift, X Liu

  • 1Sch. of Comput. Sci. & Inf. Syst., London Univ.

IEEE Transactions on Systems, Man, and Cybernetics. Part B, Cybernetics : a Publication of the IEEE Systems, Man, and Cybernetics Society
|February 5, 2008
PubMed
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This study introduces 15 novel methods for grouping variables in high-dimensional multivariate time series (MTS). These techniques leverage variable correlations to simplify complex data, offering insights into their effectiveness.

Area of Science:

  • Data Science
  • Statistics
  • Machine Learning

Background:

  • Decomposing high-dimensional multivariate time series (MTS) is challenging due to numerous potential variable dependencies.
  • Identifying underlying structures in MTS is crucial for effective analysis and modeling.

Purpose of the Study:

  • To systematically investigate the problem of variable groupings in MTS.
  • To explore novel methods for utilizing variable correlations to achieve decomposition.

Main Methods:

  • Developed and proposed 15 distinct methods for variable grouping in MTS.
  • Utilized information regarding correlations among MTS variables as a basis for grouping.
  • Applied these methods to six diverse datasets with mixed variable groupings.

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Main Results:

  • The study reports extensive experimental results from applying the 15 suggested methods.
  • Demonstrated the application of correlation-based techniques for MTS decomposition.
  • Identified strengths and weaknesses of the proposed grouping methods across different datasets.

Conclusions:

  • The proposed correlation-based methods offer a new approach to the variable grouping problem in MTS.
  • The findings provide valuable insights into the practical performance and limitations of these novel techniques.
  • This systematic study contributes to the field of time series analysis by addressing a fundamental challenge in high-dimensional data.