The DOI matches its Crossref record; 10 of the 10 references it cites were found in a registry or an index.
A Survey on Data Mining Techniques Applied to Electricity-Related Time Series Forecasting
Open accessAbstract
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.
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Metadata
- Authors
- Martínez-Álvarez, F., Troncoso, A., Asencio-Cortés, G., & Riquelme, J.
- Venue
- Energies
- Publisher
- MDPI AG
- Published
- 2015-11-19
- Volume
- 8
- Issue
- 11
- Pages
- 13162-13193
- ISSN
- 1996-1073 (ISSN-L)
- OpenAlex
- W2178310074
- Cited by
- 158
- References
- 10
- FWCI
- 6.58
- Type
- journal article
- Language
- en
- Open access
- gold
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- Martínez-Álvarez, F., Troncoso, A., Asencio-Cortés, G., & Riquelme, J. (2015). A Survey on Data Mining Techniques Applied to Electricity-Related Time Series Forecasting. Energies. https://doi.org/10.3390/en81112361
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