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Energies 2016, 9(10), 809; doi:10.3390/en9100809

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

1
Department of Applied Mathematics and Statistics, Universidad Politécnica de Cartagena, Cartagena 30202, Spain
2
Department of Electrical Engineering, Universidad Politécnica de Cartagena, Cartagena 30202, Spain
*
Author to whom correspondence should be addressed.
Academic Editor: José Riquelme
Received: 23 May 2016 / Revised: 26 September 2016 / Accepted: 28 September 2016 / Published: 11 October 2016
(This article belongs to the Special Issue Energy Time Series Forecasting)
View Full-Text   |   Download PDF [1655 KB, uploaded 11 October 2016]   |  

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. View Full-Text
Keywords: time series clustering; entropy; information theory; electricity markets time series clustering; entropy; information theory; electricity markets
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Ruiz-Abellón, M.C.; Gabaldón, A.; Guillamón, A. Dependency-Aware Clustering of Time Series and Its Application on Energy Markets. Energies 2016, 9, 809.

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