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A Survey on Data Mining Techniques Applied to Energy Time Series Forecasting

 

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Author: Martínez Álvarez, Francisco
Troncoso Lora, Alicia
Asencio Cortés, G.
Riquelme Santos, José Cristóbal
Department: Universidad de Sevilla. Departamento de Lenguajes y Sistemas Informáticos
Date: 2015
Published in: Energies, 8 (11), 13162-13193.
Document type: Article
Abstract: Data mining has become an essential tool during the last decade to analyze large sets of data. The variety of techniques it includes and the successful results obtained in many application fields, make this family of approaches powerful and widely used. In particular, this work explores the application of these techniques to time series forecasting. Although classical statistical-based methods provides reasonably good results, the result of the application of data mining outperforms those of classical ones. Hence, this work faces two main challenges: (i) to provide a compact mathematical formulation of the mainly used techniques; (ii) to review the latest works of time series forecasting and, as case study, those related to electricity price and demand markets.
Cite: Martínez Álvarez, F., Troncoso Lora, A., Asencio Cortés, G. y Riquelme Santos, J.C. (2015). A Survey on Data Mining Techniques Applied to Energy Time Series Forecasting. Energies, 8 (11), 13162-13193.
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URI: http://hdl.handle.net/11441/43669

DOI: http://dx.doi.org/10.3390/en81112361

This work is under a Creative Commons License: 
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