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Dependency-Aware Clustering of Time Series and Its Application on Energy Markets

Author

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  • María Del Carmen Ruiz-Abellón

    (Department of Applied Mathematics and Statistics, Universidad Politécnica de Cartagena, Cartagena 30202, Spain)

  • Antonio Gabaldón

    (Department of Electrical Engineering, Universidad Politécnica de Cartagena, Cartagena 30202, Spain)

  • Antonio Guillamón

    (Department of Applied Mathematics and Statistics, Universidad Politécnica de Cartagena, Cartagena 30202, Spain)

Abstract

In this paper, we propose a novel approach for clustering time series, which combines three well-known aspects: a permutation-based coding of the time series, several distance measurements for discrete distributions and hierarchical clustering using different linkages. The proposed method classifies a set of time series into homogeneous groups, according to the degree of dependency among them. That is, time series with a high level of dependency will lie in the same cluster. Moreover, taking into account the nature of the codifying process, the method allows us to detect linear and nonlinear dependences. To illustrate the procedure, a set of fourteen electricity price series coming from different wholesale electricity markets worldwide was analyzed. We show that the classification results are consistent with the characteristics of the electricity markets in the study and with their degree of integration. Besides, we outline the necessity of removing the seasonal component of the price series before the analysis and the capability of the method to detect changes in the dependence level along time.

Suggested Citation

  • María Del Carmen Ruiz-Abellón & Antonio Gabaldón & Antonio Guillamón, 2016. "Dependency-Aware Clustering of Time Series and Its Application on Energy Markets," Energies, MDPI, vol. 9(10), pages 1-22, October.
    • Handle: RePEc:gam:jeners:v:9:y:2016:i:10:p:809-:d:80175
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    References listed on IDEAS

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    1. Davide Ciferri & Maria Chiara D’Errico & Paolo Polinori, 2020. "Integration and convergence in European electricity markets," Economia Politica: Journal of Analytical and Institutional Economics, Springer;Fondazione Edison, vol. 37(2), pages 463-492, July.
    2. Félix Iglesias & Wolfgang Kastner, 2013. "Analysis of Similarity Measures in Times Series Clustering for the Discovery of Building Energy Patterns," Energies, MDPI, vol. 6(2), pages 1-19, January.
    3. Bruno Bosco & Lucia Parisio & Matteo Pelagatti & Fabio Baldi, 2010. "Long-run relations in european electricity prices," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 25(5), pages 805-832.
    4. Böckers, Veit & Heimeshoff, Ulrich, 2014. "The extent of European power markets," Energy Economics, Elsevier, vol. 46(C), pages 102-111.
    5. Rafal Weron, 2006. "Modeling and Forecasting Electricity Loads and Prices: A Statistical Approach," HSC Books, Hugo Steinhaus Center, Wroclaw University of Technology, number hsbook0601.
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