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A new PM2.5 concentration forecasting system based on AdaBoost-ensemble system with deep learning approach 期刊论文
JOURNAL OF FORECASTING, 2022, 页码: 22
作者:  Li, Zhongfei;  Gan, Kai;  Sun, Shaolong;  Wang, Shouyang
收藏  |  浏览/下载：64/0  |  提交时间：2023/02/07
AdaBoost-ensemble  deep learning  hybrid data preprocessing-analysis strategy  LSTM  
Forecasting daily tourism volume: a hybrid approach with CEMMDAN and multi-kernel adaptive ensemble 期刊论文
CURRENT ISSUES IN TOURISM, 2022, 页码: 20
作者:  Zhao, Erlong;  Du, Pei;  Azaglo, Ernest Young;  Wang, Shouyang;  Sun, Shaolong
收藏  |  浏览/下载：165/0  |  提交时间：2022/04/29
Daily tourism volume forecasting  decomposition ensemble approach  sample entropy  kernel extreme learning machine  multi-kernel adaptive strategy  
Multi-step metal prices forecasting based on a data preprocessing method and an optimized extreme learning machine by marine predators algorithm 期刊论文
RESOURCES POLICY, 2021, 卷号: 74, 页码: 10
作者:  Du, Pei;  Guo, Ju'e;  Sun, Shaolong;  Wang, Shouyang;  Wu, Jing
收藏  |  浏览/下载：145/0  |  提交时间：2022/04/02
Metal prices forecasting  Data processing method  Optimized extreme learning machine  Hybrid forecasting model  
A New Hybrid VMD-ICSS-BiGRU Approach for Gold Futures Price Forecasting and Algorithmic Trading 期刊论文
IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL SYSTEMS, 2021, 卷号: 8, 期号: 6, 页码: 1357-1368
作者:  Li, Yuze;  Wang, Shouyang;  Wei, Yunjie;  Zhu, Qing
收藏  |  浏览/下载：109/0  |  提交时间：2022/04/02
Gold  Forecasting  Autoregressive processes  Predictive models  Signal resolution  Deep learning  Mathematical model  Algorithmic trading  bidirectional gated recurrent unit (BiGRU)  gold futures price forecasting  variational mode decomposition (VMD)  
Forecasting hourly PM2.5 based on deep temporal convolutional neural network and decomposition method 期刊论文
APPLIED SOFT COMPUTING, 2021, 卷号: 113, 页码: 15
作者:  Jiang, Fuxin;  Zhang, Chengyuan;  Sun, Shaolong;  Sun, Jingyun
收藏  |  浏览/下载：115/0  |  提交时间：2022/04/29
PM2.5 concentration forecasting  Complete ensemble empirical mode  decomposition with adaptive noise  Temporal convolutional  Data patterns  Deep learning