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He Kaijian
He Kaijian
Verified email at hnust.edu.cn
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Cited by
Cited by
Year
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
2632019
Extreme risk spillover network: application to financial institutions
GJ Wang, C Xie, K He, HE Stanley
Quantitative Finance 17 (9), 1417-1433, 2017
2222017
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
1682018
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
1642019
Crude oil price analysis and forecasting using wavelet decomposed ensemble model
K He, L Yu, KK Lai
Energy 46 (1), 564-574, 2012
1552012
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
1302019
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
1282020
Forecasting crude oil prices: a deep learning based model
Y Chen, K He, GKF Tso
Procedia computer science 122, 300-307, 2017
1242017
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
962020
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
962019
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
792021
A novel data-characteristic-driven modeling methodology for nuclear energy consumption forecasting
L Tang, L Yu, K He
Applied Energy 128, 1-14, 2014
782014
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
672007
Price forecasting in the precious metal market: A multivariate EMD denoising approach
K He, Y Chen, GKF Tso
Resources Policy 54, 9-24, 2017
592017
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
562021
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
562016
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
552015
A novel grey wave forecasting method for predicting metal prices
Y Chen, K He, C Zhang
Resources Policy 49, 323-331, 2016
532016
Estimating VaR in crude oil market: A novel multi-scale non-linear ensemble approach incorporating wavelet analysis and neural network
K He, C Xie, S Chen, KK Lai
Neurocomputing 72 (16-18), 3428-3438, 2009
522009
Electricity price forecasting with a BED (Bivariate EMD Denoising) methodology
K He, L Yu, L Tang
Energy 91, 601-609, 2015
512015
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