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Eigen-entropy based time series signatures to support multivariate time series classification.

Abhidnya Patharkar1,2, Jiajing Huang1,2, Teresa Wu3,4

  • 1School of Computing and Augmented Intelligence, Arizona State University, Tempe, AZ, 85281, USA.

Scientific Reports
|July 11, 2024
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Summary
This summary is machine-generated.

This study introduces Eigen-entropy-based Time Series Signatures to classify multivariate time series by capturing inter-variable correlations. The novel approach significantly improves classification recall across multiple datasets, outperforming existing methods.

Keywords:
Correlation coefficientDense multi scale entropyEigen-entropyEigenvalueMultivariate time series classificationTime series signatures

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Area of Science:

  • Data Science
  • Machine Learning
  • Time Series Analysis

Background:

  • Current multivariate time series classification algorithms often neglect inter-variable correlations.
  • This limitation hinders accurate classification, especially in dynamic and temporal datasets.

Purpose of the Study:

  • To propose a novel framework, Eigen-entropy-based Time Series Signatures (ETSS), for multivariate time series classification.
  • To effectively capture temporal dynamics and correlations among different time series variables.

Main Methods:

  • Utilized Eigen-entropy and a cumulative moving window to derive time series signatures.
  • Employed dense multi-scale entropy for preprocessing to manage dataset dynamics.
  • Developed ETSS to enumerate correlations while preserving temporal and dynamic aspects.

Main Results:

  • ETSS demonstrated superior performance in classification recall across seven out of eight diverse datasets.
  • Outperformed baseline algorithms including dependent dynamic time warping and multivariate multi-scale permutation entropy.
  • Validated on University of East Anglia datasets, a gait dataset, and a Mayo Clinic sepsis dataset.

Conclusions:

  • The proposed Eigen-entropy-based Time Series Signatures framework effectively captures multivariate time series correlations.
  • ETSS offers a robust and improved approach for time series classification tasks.
  • The method shows significant potential for applications requiring accurate analysis of complex time series data.