TITLE

A Survey on Data Mining Techniques Applied to Electricity-Related Time Series Forecasting

AUTHOR(S)
Martínez-Álvarez, Francisco; Troncoso, Alicia; Asencio-Cortés, Gualberto; Riquelme, José C.
PUB. DATE
November 2015
SOURCE
Energies (19961073);2015, Vol. 8 Issue 11, p13162
SOURCE TYPE
Academic Journal
DOC. 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.
ACCESSION #
111218008

 

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