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Optimization of mixture models on time series networks encoded by visibility graphs: an analysis of the US electricity market

Author

Listed:
  • Carlo Mari

    (University of Chieti-Pescara)

  • Cristiano Baldassari

    (University of Chieti-Pescara)

Abstract

We propose a fully unsupervised network-based methodology for estimating Gaussian Mixture Models on financial time series by maximum likelihood using the Expectation-Maximization algorithm. Visibility graph-structured information of observed data is used to initialize the algorithm. The proposed methodology is applied to the US wholesale electricity market. We will demonstrate that encoding time series through Visibility Graphs allows us to capture the behavior of the time series and the nonlinear interactions between observations well. The results reveal that the proposed methodology outperforms more established approaches.

Suggested Citation

  • Carlo Mari & Cristiano Baldassari, 2023. "Optimization of mixture models on time series networks encoded by visibility graphs: an analysis of the US electricity market," Computational Management Science, Springer, vol. 20(1), pages 1-23, December.
    • Handle: RePEc:spr:comgts:v:20:y:2023:i:1:d:10.1007_s10287-023-00460-4
    DOI: 10.1007/s10287-023-00460-4
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    References listed on IDEAS

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    3. Carlo Mari & Cristiano Baldassari, 2021. "Ensemble Methods for Jump-Diffusion Models of Power Prices," Energies, MDPI, vol. 14(8), pages 1-17, April.
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    Full references (including those not matched with items on IDEAS)

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