Energy Consumption Forecasting Using Seasonal ARIMA with Artificial Neural Networks Models
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- Abhishek Singh & G. C. Mishra, 2015. "Application of Box-Jenkins method and Artificial Neural Network procedure for time series forecasting of prices," Statistics in Transition new series, Główny Urząd Statystyczny (Polska), vol. 16(1), pages 83-96, May.
- Zhang, Guoqiang & Eddy Patuwo, B. & Y. Hu, Michael, 1998. "Forecasting with artificial neural networks:: The state of the art," International Journal of Forecasting, Elsevier, vol. 14(1), pages 35-62, March.
- Junttila, Juha, 2001. "Structural breaks, ARIMA model and Finnish inflation forecasts," International Journal of Forecasting, Elsevier, vol. 17(2), pages 203-230.
- Abhishek Singh & G. C. Mishra, 2015. "Application Of Box-Jenkins Method And Artificial Neural Network Procedure For Time Series Forecasting Of Prices," Statistics in Transition New Series, Polish Statistical Association, vol. 16(1), pages 83-96, March.
- Polterovich, Victor & Popov, Vladimir, 2006. "Эволюционная Теория Экономической Политики: Часть I: Опыт Быстрого Развития [An Evolutionary Theory of Economic Policy: Part I: The Experience of Fast Development]," MPRA Paper 22168, University Library of Munich, Germany.
- Karin Kandananond, 2011. "Forecasting Electricity Demand in Thailand with an Artificial Neural Network Approach," Energies, MDPI, vol. 4(8), pages 1-12, August.
- Bodyanskiy, Yevgeniy & Popov, Sergiy, 2006. "Neural network approach to forecasting of quasiperiodic financial time series," European Journal of Operational Research, Elsevier, vol. 175(3), pages 1357-1366, December.
- Flores, Juan J. & Graff, Mario & Rodriguez, Hector, 2012. "Evolutive design of ARMA and ANN models for time series forecasting," Renewable Energy, Elsevier, vol. 44(C), pages 225-230.
- Oscar Claveria & Enric Monte & Salvador Torra, 2014.
"“A multivariate neural network approach to tourism demand forecasting”,"
AQR Working Papers
201410, University of Barcelona, Regional Quantitative Analysis Group, revised May 2014.
- Oscar Claveria & Enric Monte & Salvador Torra, 2014. "“A multivariate neural network approach to tourism demand forecasting”," IREA Working Papers 201417, University of Barcelona, Research Institute of Applied Economics, revised May 2014.
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JEL classification:
- R00 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - General - - - General
- Z0 - Other Special Topics - - General
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