| Exploring the effect of industrial structure adjustment on interprovincial green development efficiency in China: A novel integrated approach B Zhu, M Zhang, Y Zhou, P Wang, J Sheng, K He, YM Wei, R Xie Energy Policy 134, 110946, 2019 | 332 | 2019 |
| Extreme risk spillover network: application to financial institutions GJ Wang, C Xie, K He, HE Stanley Quantitative Finance 17 (9), 1417-1433, 2017 | 251 | 2017 |
| Measuring the maturity of carbon market in China: An entropy-based TOPSIS approach X Liu, X Zhou, B Zhu, K He, P Wang Journal of cleaner production 229, 94-103, 2019 | 194 | 2019 |
| A novel multiscale nonlinear ensemble leaning paradigm for carbon price forecasting B Zhu, S Ye, P Wang, K He, T Zhang, YM Wei Energy Economics 70, 143-157, 2018 | 189 | 2018 |
| Crude oil price analysis and forecasting using wavelet decomposed ensemble model K He, L Yu, KK Lai Energy 46 (1), 564-574, 2012 | 162 | 2012 |
| A multiscale analysis for carbon price drivers B Zhu, S Ye, D Han, P Wang, K He, YM Wei, R Xie Energy Economics 78, 202-216, 2019 | 154 | 2019 |
| Dependences and volatility spillovers between the oil and stock markets: New evidence from the copula and VAR-BEKK-GARCH models L Yu, R Zha, D Stafylas, K He, J Liu International Review of Financial Analysis 68, 101280, 2020 | 149 | 2020 |
| Forecasting crude oil prices: a deep learning based model Y Chen, K He, GKF Tso Procedia computer science 122, 300-307, 2017 | 141 | 2017 |
| Carbon futures price forecasting based with ARIMA-CNN-LSTM model L Ji, Y Zou, K He, B Zhu Procedia Computer Science 162, 33-38, 2019 | 122 | 2019 |
| Exploring the risk spillover effects among China's pilot carbon markets: A regular vine copula-CoES approach B Zhu, X Zhou, X Liu, H Wang, K He, P Wang Journal of Cleaner Production 242, 118455, 2020 | 106 | 2020 |
| Using SARIMA–CNN–LSTM approach to forecast daily tourism demand K He, L Ji, CWD Wu, KFG Tso Journal of Hospitality and Tourism Management 49, 25-33, 2021 | 99 | 2021 |
| A novel data-characteristic-driven modeling methodology for nuclear energy consumption forecasting L Tang, L Yu, K He Applied Energy 128, 1-14, 2014 | 80 | 2014 |
| Forecasting tourist daily arrivals with a hybrid Sarima–Lstm approach DCW Wu, L Ji, K He, KFG Tso Journal of hospitality & tourism research 45 (1), 52-67, 2021 | 71 | 2021 |
| Oil price forecasting with an EMD-based multiscale neural network learning paradigm L Yu, KK Lai, S Wang, K He Computational Science–ICCS 2007: 7th International Conference, Beijing …, 2007 | 69 | 2007 |
| Price forecasting in the precious metal market: A multivariate EMD denoising approach K He, Y Chen, GKF Tso Resources Policy 54, 9-24, 2017 | 64 | 2017 |
| A novel mode-characteristic-based decomposition ensemble model for nuclear energy consumption forecasting L Tang, S Wang, K He, S Wang Annals of Operations Research 234, 111-132, 2015 | 60 | 2015 |
| Financial time series forecasting with the deep learning ensemble model K He, Q Yang, L Ji, J Pan, Y Zou Mathematics 11 (4), 1054, 2023 | 58 | 2023 |
| A novel grey wave forecasting method for predicting metal prices Y Chen, K He, C Zhang Resources Policy 49, 323-331, 2016 | 57 | 2016 |
| Gold price analysis based on ensemble empirical model decomposition and independent component analysis L Xian, K He, KK Lai Physica A: Statistical Mechanics and its Applications 454, 11-23, 2016 | 57 | 2016 |
| Multi-step-ahead crude oil price forecasting using a hybrid grey wave model Y Chen, C Zhang, K He, A Zheng Physica A: Statistical Mechanics and its Applications 501, 98-110, 2018 | 54 | 2018 |
