Short-term wind speed forecasting based on a hybrid model. - Wenyu Zhang; Jujie Wang; Jianzhou Wang; Zengbao Zhao; Meng Tian (Appl. Soft Comput., 2013)

Forecasting wind speed using empirical mode decomposition and Elman neural network. - Jujie Wang; Wenyu Zhang; Yaning Li; Jianzhou Wang; Zhangli Dang (Appl. Soft Comput., 2014)

A novel hybrid approach for wind speed prediction. - Jujie Wang; Wenyu Zhang; Jianzhou Wang; Tingting Han; Lingbin Kong (Inf. Sci., 2014)

Short-Term Wind Speed Prediction Using Signal Preprocessing Technique and Evolutionary Support Vector Regression. - Jujie Wang; Yaning Li (Neural Process. Lett., 2018)

An innovative hybrid approach for multi-step ahead wind speed prediction. - Jujie Wang; Yaning Li (Appl. Soft Comput., 2019)

Stock index prediction and uncertainty analysis using multi-scale nonlinear ensemble paradigm of optimal feature extraction, two-stage deep learning and Gaussian process regression. - Jujie Wang; Junjie He; Chunchen Feng; Liu Feng; Yang Li (Appl. Soft Comput., 2021)

Deep Nonlinear Ensemble Framework for Stock Index Forecasting and Uncertainty Analysis. - Jujie Wang; Liu Feng; Yang Li; Junjie He; Chunchen Feng (Cogn. Comput., 2021)

Adaboost-based Integration Framework Coupled Two-stage Feature Extraction with Deep Learning for Multivariate Exchange Rate Prediction. - Jujie Wang; Yu Chen (Neural Process. Lett., 2021)

Optimized decomposition and two-step nonlinear integration model with error correction strategy coupled interval prediction for digital currency price forecast. - Jujie Wang; Shiyao Qiu (Artif. Intell. Rev., 2022)

Depth feature extraction-based deep ensemble learning framework for high frequency futures price forecasting. - Jujie Wang; Yu Chen; Shuzhou Zhu; Wenjie Xu (Digit. Signal Process., 2022)

Asian stock markets closing index forecast based on secondary decomposition, multi-factor analysis and attention-based LSTM model. - Jujie Wang; Quan Cui; Xin Sun; Maolin He (Eng. Appl. Artif. Intell., 2022)

A multi-factor two-stage deep integration model for stock price prediction based on intelligent optimization and feature clustering. - Jujie Wang; Shuzhou Zhu (Artif. Intell. Rev., 2023)

A Novel Stock Index Direction Prediction Based on Dual Classifier Coupling and Investor Sentiment Analysis. - Jujie Wang; Shuzhou Zhu (Cogn. Comput., 2023)

An XGBoost-based multivariate deep learning framework for stock index futures price forecasting. - Jujie Wang; Qian Cheng; Ying Dong 004 (Kybernetes, 2023)

A time series attention mechanism based model for tourism demand forecasting. - Yunxuan Dong; Ling Xiao; Jiasheng Wang; Jujie Wang (Inf. Sci., 2023)

A deep learning-based nonlinear ensemble approach with biphasic feature selection for multivariate exchange rate forecasting. - Jujie Wang; Maolin He; Wenjie Xu; Feng Jing (Multim. Tools Appl., 2023)

Stock Trading Strategy of Reinforcement Learning Driven by Turning Point Classification. - Jujie Wang; Feng Jing; Maolin He (Neural Process. Lett., 2023)

An optimized deep nonlinear integrated framework for wind speed forecasting and uncertainty analysis. - Jujie Wang; Dongming Gao; Zhenzhen Zhuang (Appl. Soft Comput., 2023)

A novel multifactor clustering integration paradigm based on two-stage feature engineering and improved bidirectional deep neural networks for exchange rate forecasting. - Jujie Wang; Ying Dong 004; Jing Liu (Digit. Signal Process., 2023)

An interpretable deep learning multi-dimensional integration framework for exchange rate forecasting based on deep and shallow feature selection and snapshot ensemble technology. - Jujie Wang; Ying Dong 004 (Eng. Appl. Artif. Intell., 2024)

An enhanced interval-valued decomposition integration model for stock price prediction based on comprehensive feature extraction and optimized deep learning. - Jujie Wang; Jing Liu; Weiyi Jiang (Expert Syst. Appl., 2024)

A deterministic and probabilistic hybrid model for wind power forecasting based improved feature screening and optimal Gaussian mixed kernel function. - Jujie Wang; Xudong Tang; Weiyi Jiang (Expert Syst. Appl., 2024)

Two-Stage Deep Ensemble Paradigm Based on Optimal Multi-scale Decomposition and Multi-factor Analysis for Stock Price Prediction. - Jujie Wang; Jing Liu (Cogn. Comput., 2024)

Causal carbon price interval prediction using lower upper bound estimation combined with asymmetric multi-objective evolutionary algorithm and long short-term memory. - Jujie Wang; Maolin He; Weiyi Jiang (Expert Syst. Appl., 2024)

A novel Gaussian process regression-based stock index interval forecasting model integrating optimal variables screening with bidirectional long short-term memory. - Jujie Wang; Qian Cheng; Xin Sun (Soft Comput., 2024)

A Transfer Learning Combination Model for Annual National Electricity Consumption Forecasting in China. - Ling Liu; Jujie Wang (IEEE Access, 2025)

A coupling deterministic and probabilistic wind energy prediction based on information leakage prevention and distinctive deep learning network. - Jujie Wang; Yafen Liu; Shuqin Shu; Xuecheng He (Eng. Appl. Artif. Intell., 2025)

An optimal multi-scale and multi-factor two-stage integration paradigm coupled with investor sentiment for carbon price prediction. - Jujie Wang; Xuecheng He (Inf. Process. Manag., 2025)

An interval-valued carbon price prediction model based on improved multi-scale feature selection and optimal multi-kernel support vector regression. - Yuxuan Lu 009; Jujie Wang; Qian Li (Inf. Sci., 2025)

An interpretable interval-valued wind power prediction system based on multi-objective feature extraction and base model selection with dynamic ensemble. - Jujie Wang; Yuxuan Lu 009; Qian Li (Swarm Evol. Comput., 2025)

An optimized decomposition integration framework for carbon price prediction based on multi-factor two-stage feature dimension reduction. - Wenjie Xu; Jujie Wang; Yue Zhang; Jianping Li; Lu Wei (Ann. Oper. Res., 2025)

Combining Interpretable Embedded Multicriteria Feature Cross-Selection Engineering and Machine Learning to Mimic the Brain for Stock Trading Signal Prediction. - Jujie Wang; Ying Dong 004 (Cogn. Comput., 2025)