Artikel mit Open-Access-Mandaten - Lean YuWeitere Informationen
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A deep learning ensemble approach for crude oil price forecasting
Y Zhao, J Li, L Yu
Energy Economics 66, 9-16, 2017
Mandate: National Natural Science Foundation of China
Online big data-driven oil consumption forecasting with Google trends
L Yu, Y Zhao, L Tang, Z Yang
International Journal of Forecasting 35 (1), 213-223, 2019
Mandate: Chinese Academy of Sciences, National Natural Science Foundation of China
Carbon emissions trading scheme exploration in China: A multi-agent-based model
L Tang, J Wu, L Yu, Q Bao
Energy Policy 81, 152-169, 2015
Mandate: National Natural Science Foundation of China
A novel decomposition ensemble model with extended extreme learning machine for crude oil price forecasting
L Yu, W Dai, L Tang
Engineering Applications of Artificial Intelligence 47, 110-121, 2016
Mandate: National Natural Science Foundation of China
A decomposition–ensemble model with data-characteristic-driven reconstruction for crude oil price forecasting
L Yu, Z Wang, L Tang
Applied Energy 156, 251-267, 2015
Mandate: National Natural Science Foundation of China
Linear and nonlinear Granger causality investigation between carbon market and crude oil market: A multi-scale approach
L Yu, J Li, L Tang, S Wang
Energy Economics 51, 300-311, 2015
Mandate: National Natural Science Foundation of China
A DBN-based resampling SVM ensemble learning paradigm for credit classification with imbalanced data
L Yu, R Zhou, L Tang, R Chen
Applied Soft Computing 69, 192-202, 2018
Mandate: National Natural Science Foundation of China
Complexity testing techniques for time series data: A comprehensive literature review
L Tang, H Lv, F Yang, L Yu
Chaos, Solitons & Fractals 81, 117-135, 2015
Mandate: National Natural Science Foundation of China
A non-iterative decomposition-ensemble learning paradigm using RVFL network for crude oil price forecasting
L Tang, Y Wu, L Yu
Applied Soft Computing 70, 1097-1108, 2018
Mandate: National Natural Science Foundation of China
A novel CEEMD-based EELM ensemble learning paradigm for crude oil price forecasting
L Tang, W Dai, L Yu, S Wang
International Journal of Information Technology & Decision Making 14 (01 …, 2015
Mandate: National Natural Science Foundation of China
Carbon allowance auction design of China's emissions trading scheme: A multi-agent-based approach
L Tang, J Wu, L Yu, Q Bao
Energy Policy 102, 30-40, 2017
Mandate: National Natural Science Foundation of China
Fuzzy multi-period portfolio selection with different investment horizons
S Guo, L Yu, X Li, S Kar
European Journal of Operational Research 254 (3), 1026-1035, 2016
Mandate: National Natural Science Foundation of China
Economic and environmental influences of coal resource tax in China: A dynamic computable general equilibrium approach
L Tang, J Shi, L Yu, Q Bao
Resources, Conservation and Recycling 117, 34-44, 2017
Mandate: National Natural Science Foundation of China
Trajectory prediction for heterogeneous traffic-agents using knowledge correction data-driven model
X Xu, W Liu, L Yu
Information Sciences 608, 375-391, 2022
Mandate: National Natural Science Foundation of China
A novel data-characteristic-driven modeling methodology for nuclear energy consumption forecasting
L Tang, L Yu, K He
Applied Energy 128, 1-14, 2014
Mandate: National Natural Science Foundation of China
LSSVR ensemble learning with uncertain parameters for crude oil price forecasting
L Yu, H Xu, L Tang
Applied Soft Computing 56, 692-701, 2017
Mandate: National Natural Science Foundation of China
Ensemble forecasting for complex time series using sparse representation and neural networks
L Yu, Y Zhao, L Tang
Journal of Forecasting 36 (2), 122-138, 2017
Mandate: National Natural Science Foundation of China
A hybrid grid-GA-based LSSVR learning paradigm for crude oil price forecasting
L Yu, W Dai, L Tang, J Wu
Neural computing and applications 27, 2193-2215, 2016
Mandate: National Natural Science Foundation of China
A randomized-algorithm-based decomposition-ensemble learning methodology for energy price forecasting
L Tang, Y Wu, L Yu
Energy 157, 526-538, 2018
Mandate: National Natural Science Foundation of China
A novel multistage deep belief network based extreme learning machine ensemble learning paradigm for credit risk assessment
L Yu, Z Yang, L Tang
Flexible Services and Manufacturing Journal 28, 576-592, 2016
Mandate: National Natural Science Foundation of China
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