A novel auto-regressive fractionally integrated moving average--least-squares support vector machine model for electricity spot prices prediction
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DOI: 10.1080/02664763.2013.847068
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References listed on IDEAS
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Cited by:
- Jiang, Ping & Nie, Ying & Wang, Jianzhou & Huang, Xiaojia, 2023. "Multivariable short-term electricity price forecasting using artificial intelligence and multi-input multi-output scheme," Energy Economics, Elsevier, vol. 117(C).
- Hongyue Guo & Xiaodong Liu & Zhubin Sun, 2016. "Multivariate time series prediction using a hybridization of VARMA models and Bayesian networks," Journal of Applied Statistics, Taylor & Francis Journals, vol. 43(16), pages 2897-2909, December.
- Weron, Rafał, 2014.
"Electricity price forecasting: A review of the state-of-the-art with a look into the future,"
International Journal of Forecasting, Elsevier, vol. 30(4), pages 1030-1081.
- Rafal Weron, 2014. "Electricity price forecasting: A review of the state-of-the-art with a look into the future," HSC Research Reports HSC/14/07, Hugo Steinhaus Center, Wroclaw University of Technology.
- Ghimire, Sujan & Deo, Ravinesh C. & Casillas-Pérez, David & Salcedo-Sanz, Sancho, 2024. "Two-step deep learning framework with error compensation technique for short-term, half-hourly electricity price forecasting," Applied Energy, Elsevier, vol. 353(PA).
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