Jianlong Li

• Decomposition-Based Dynamic Inductive Graph Embedding Learning Method to Forecast Stock Trends

  Open Access•Qing Zhu, Jianlong Li et al.•ARTICLE•IEEE Transactions on Computational…•2025

  Añadir item

  The stock market is a profit-oriented, chaotic, and nonlinear market game platform. Because price changes are directly related to investors’ returns, the accurate prediction of the short-term trend of stock prices has garnered significant attention. Recently, the application of a graph neural network (GNN) has become a research hotspot because of its ability to mine the momentum spillover effect between asset prices to achieve more effective fore…

• Quantitative assessment of carbon sequestration reduction induced by disturbances in temperate Eurasian steppe

  Open Access•Yizhao Chen, Weimin Ju et al.•ARTICLE•Environmental Research Letters•2017

  Añadir item

  The temperate Eurasian steppe (TES) is a region where various environmental, social, and economic stresses converge. Multiple types of disturbance exist widely across the landscape, and heavily influence carbon cycling in this region. However, a current quantitative assessment of the impact of disturbances on carbon sequestration is largely lacking. In this study, we combined the boreal ecosystem productivity simulator (BEPS), the Shiyomi grazing…

• Quantitative assessment of carbon sequestration reduction induced by disturbances in temperate Eurasian steppe

  Open Access•Yizhao Chen, Weimin Ju et al.•ARTICLE•Environmental Research Letters•2017

  Añadir item

  The temperate Eurasian steppe (TES) is a region where various environmental, social, and economic stresses converge. Multiple types of disturbance exist widely across the landscape, and heavily influence carbon cycling in this region. However, a current quantitative assessment of the impact of disturbances on carbon sequestration is largely lacking. In this study, we combined the boreal ecosystem productivity simulator (BEPS), the Shiyomi grazing…

• Decomposition-Based Dynamic Inductive Graph Embedding Learning Method to Forecast Stock Trends

  Open Access•Qing Zhu, Jianlong Li et al.•ARTICLE•IEEE Transactions on Computational…•2025

  Añadir item

  The stock market is a profit-oriented, chaotic, and nonlinear market game platform. Because price changes are directly related to investors’ returns, the accurate prediction of the short-term trend of stock prices has garnered significant attention. Recently, the application of a graph neural network (GNN) has become a research hotspot because of its ability to mine the momentum spillover effect between asset prices to achieve more effective fore…

Agroforestry (1 obras) · Artificial Intelligence (1 obras) · Atmospheric sciences (1 obras) · Biology (1 obras) · Boreal (1 obras) · Carbon cycle (1 obras) · Carbon dioxide (1 obras) · Carbon fibers (1 obras) · Carbon sequestration (1 obras) · Chemistry (1 obras)