Innovative time series forecasting: auto regressive moving average vs deep networks
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Abstract
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DOI: 10.9770/jesi.2017.4.3S(4)
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References listed on IDEAS
- Wang, H.Z. & Wang, G.B. & Li, G.Q. & Peng, J.C. & Liu, Y.T., 2016. "Deep belief network based deterministic and probabilistic wind speed forecasting approach," Applied Energy, Elsevier, vol. 182(C), pages 80-93.
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Cited by:
- Muhammad Waseem Ahmad & Anthony Mouraud & Yacine Rezgui & Monjur Mourshed, 2018. "Deep Highway Networks and Tree-Based Ensemble for Predicting Short-Term Building Energy Consumption," Energies, MDPI, vol. 11(12), pages 1-21, December.
- Demirhan, Haydar & Renwick, Zoe, 2018. "Missing value imputation for short to mid-term horizontal solar irradiance data," Applied Energy, Elsevier, vol. 225(C), pages 998-1012.
- Wang, Jun-Cheng & Wang, Fa-Hui & Wang, Ya-Xiong & Chen, Shi-An, 2023. "Analysis of real-time energy losses of electric vehicle caused by non-stationary road irregularity," Energy, Elsevier, vol. 282(C).
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More about this item
Keywords
sustainability; buildings; time series forecasting; Auto Regressive Moving Average (ARMA); deep networks;All these keywords.
JEL classification:
- C45 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Neural Networks and Related Topics
- C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
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