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Multiway clustering with time-varying parameters.

Roy Cerqueti1,2,3, Raffaele Mattera1, Germana Scepi4

  • 1Department of Social and Economic Sciences, Sapienza University of Rome, Rome, Italy.

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|November 7, 2022
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Summary
This summary is machine-generated.

This study introduces a new clustering method for multivariate time series data with changing parameters. The approach is validated using simulations and real-world air quality data analysis.

Keywords:
Air qualityDynamic Conditional ScoreGeneralized Autoregressive ScoreMultiway dataTime series clusteringtime-varying parameters

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Area of Science:

  • Statistics
  • Data Science
  • Environmental Science

Background:

  • Clustering of time series is well-established for static distributions.
  • Methods for time-varying parameters are emerging but scarce for multivariate data.

Purpose of the Study:

  • To propose a novel clustering approach for multivariate time series with time-varying parameters.
  • To address the gap in existing methods for this specific data type.

Main Methods:

  • Developed a multiway framework for distribution-based clustering.
  • Incorporated time-varying parameter estimation within the clustering procedure.

Main Results:

  • Demonstrated the effectiveness of the proposed clustering method.
  • Validated the approach through a simulation study.

Conclusions:

  • The proposed multiway clustering approach is effective for multivariate time series with time-varying parameters.
  • The method shows practical applicability with real air quality data.