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Using Cholesky Decomposition to Explore Individual Differences in Longitudinal Relations between Reading Skills
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Model-based clustering of time-dependent observations with common structural changes.

Riccardo Corradin1, Luca Danese1, Wasiur R KhudaBukhsh2

  • 1Department of Economics, Management and Statistics, University of Milano-Bicocca, Milano, 20136 Italy.

Statistics and Computing
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Summary
This summary is machine-generated.

This study introduces a new clustering method for time series data. It groups data based on simultaneous structural changes, applicable to epidemiological modeling like COVID-19 spread in the EU.

Keywords:
COVID-19Change pointsModel-based clusteringTime series

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Area of Science:

  • Statistics
  • Computational Biology
  • Epidemiology

Background:

  • Clustering time series data is challenging.
  • Existing methods often fail to capture synchronized behavioral changes.
  • Identifying simultaneous structural shifts is crucial for comparative analysis.

Purpose of the Study:

  • To develop a novel model-based clustering approach for time series.
  • To group observations based on the timing of structural changes.
  • To apply this method to epidemiological data, specifically COVID-19 spread in the European Union.

Main Methods:

  • Utilizing a latent representation of structural changes within time series.
  • Employing random orders to identify and synchronize these changes.
  • Developing a general modeling strategy adaptable to various time-dependent models.

Main Results:

  • Demonstrated a novel approach for clustering time series data.
  • Successfully identified countries with similar COVID-19 spreading dynamics based on synchronized structural changes.
  • The model-based approach offers flexibility and can be integrated with existing time-dependent models.

Conclusions:

  • The proposed method effectively clusters time series by synchronicity of structural changes.
  • This approach provides valuable insights for epidemiological analysis and policy-making.
  • The general framework allows for broad applications in analyzing time-dependent data.