Variational Bayesian Network with Information Interpretability Filtering for Air Quality Forecasting
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- Wang, Guoyang & Awad, Omar I. & Liu, Shiyu & Shuai, Shijin & Wang, Zhiming, 2020. "NOx emissions prediction based on mutual information and back propagation neural network using correlation quantitative analysis," Energy, Elsevier, vol. 198(C).
- Guangyuan Xing & Er-long Zhao & Chengyuan Zhang & Jing Wu & Giancarlo Consolo, 2021. "A Decomposition-Ensemble Approach with Denoising Strategy for PM2.5 Concentration Forecasting," Discrete Dynamics in Nature and Society, Hindawi, vol. 2021, pages 1-13, April.
- Jianxian Cai & Xun Dai & Li Hong & Zhitao Gao & Zhongchao Qiu, 2020. "An Air Quality Prediction Model Based on a Noise Reduction Self-Coding Deep Network," Mathematical Problems in Engineering, Hindawi, vol. 2020, pages 1-12, May.
- Xue-Bo Jin & Wei-Zhen Zheng & Jian-Lei Kong & Xiao-Yi Wang & Yu-Ting Bai & Ting-Li Su & Seng Lin, 2021. "Deep-Learning Forecasting Method for Electric Power Load via Attention-Based Encoder-Decoder with Bayesian Optimization," Energies, MDPI, vol. 14(6), pages 1-18, March.
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Keywords
multiple factors; time series forecasting; deep learning; interpretability; data filtering; variational Bayesian;All these keywords.
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