Short-Term Forecasting for Energy Consumption through Stacking Heterogeneous Ensemble Learning Model
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- Crone, Sven F. & Hibon, Michèle & Nikolopoulos, Konstantinos, 2011.
"Advances in forecasting with neural networks? Empirical evidence from the NN3 competition on time series prediction,"
International Journal of Forecasting, Elsevier, vol. 27(3), pages 635-660.
- Crone, Sven F. & Hibon, Michèle & Nikolopoulos, Konstantinos, 2011. "Advances in forecasting with neural networks? Empirical evidence from the NN3 competition on time series prediction," International Journal of Forecasting, Elsevier, vol. 27(3), pages 635-660, July.
- De Gooijer, Jan G. & Hyndman, Rob J., 2006. "25 years of time series forecasting," International Journal of Forecasting, Elsevier, vol. 22(3), pages 443-473.
- Rafal Weron & Florian Ziel, 2018.
"Electricity price forecasting,"
HSC Research Reports
HSC/18/08, Hugo Steinhaus Center, Wroclaw University of Technology.
- Katarzyna Maciejowska & Rafal Weron, 2019. "Electricity price forecasting," HSC Research Reports HSC/19/01, Hugo Steinhaus Center, Wroclaw University of Technology.
- Hsiao, Cheng & Wan, Shui Ki, 2014. "Is there an optimal forecast combination?," Journal of Econometrics, Elsevier, vol. 178(P2), pages 294-309.
- Hansen, Bruce E., 2006. "Interval forecasts and parameter uncertainty," Journal of Econometrics, Elsevier, vol. 135(1-2), pages 377-398.
- Ekonomou, L., 2010. "Greek long-term energy consumption prediction using artificial neural networks," Energy, Elsevier, vol. 35(2), pages 512-517.
- Bianco, Vincenzo & Manca, Oronzio & Nardini, Sergio & Minea, Alina A., 2010. "Analysis and forecasting of nonresidential electricity consumption in Romania," Applied Energy, Elsevier, vol. 87(11), pages 3584-3590, November.
- 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.
- Simmons, L. F., 1990. "Time-series decomposition using the sinusoidal model," International Journal of Forecasting, Elsevier, vol. 6(4), pages 485-495, December.
- Ebrahimpour, Reza & Nikoo, Hossein & Masoudnia, Saeed & Yousefi, Mohammad Reza & Ghaemi, Mohammad Sajjad, 2011.
"Mixture of MLP-experts for trend forecasting of time series: A case study of the Tehran stock exchange,"
International Journal of Forecasting, Elsevier, vol. 27(3), pages 804-816, July.
- Ebrahimpour, Reza & Nikoo, Hossein & Masoudnia, Saeed & Yousefi, Mohammad Reza & Ghaemi, Mohammad Sajjad, 2011. "Mixture of MLP-experts for trend forecasting of time series: A case study of the Tehran stock exchange," International Journal of Forecasting, Elsevier, vol. 27(3), pages 804-816.
- Ediger, Volkan S. & Akar, Sertac, 2007. "ARIMA forecasting of primary energy demand by fuel in Turkey," Energy Policy, Elsevier, vol. 35(3), pages 1701-1708, March.
- Ebru Turanoglu & Ozlem Senvar & Cengiz Kahraman, 2012. "Oil Consumption Forecasting in Turkey using Artificial Neural Network," International Journal of Energy Optimization and Engineering (IJEOE), IGI Global, vol. 1(4), pages 89-105, October.
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Keywords
time series forecasting; ensemble learning; heterogeneous models; SMLE; oil consumption;All these keywords.
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