Artículo
A Survey on Data Mining Techniques Applied to Energy Time Series Forecasting
Autor/es | Martínez Álvarez, Francisco
Troncoso Lora, Alicia Asencio Cortés, G. Riquelme Santos, José Cristóbal |
Departamento | Universidad de Sevilla. Departamento de Lenguajes y Sistemas Informáticos |
Fecha de publicación | 2015 |
Fecha de depósito | 2016-07-15 |
Publicado en |
|
Resumen | 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 ... 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. |
Identificador del proyecto | info:eu-repo/grantAgreement/MINECO/TIN2014-55894-C2-R
P12- TIC-1728 APPB813097 |
Cita | 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. |
Ficheros | Tamaño | Formato | Ver | Descripción |
---|---|---|---|---|
A survey on data mining techni ... | 455.6Kb | [PDF] | Ver/ | |