丁松 博士生导师  硕士生导师

学科教学所属单位:经济学院

教师英文名称:DingSong

教师拼音名称:dingsong

电子邮箱:

办公地点:图书馆517,
经济学院6-310

性别:男

联系方式:dings1129@zufe.edu.cn

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论文成果

  • 丁松,神兴傲,蔡智建. Data-driven multi-step solar photovoltaic predictions with limited and uncertain information: Insights from a collaboratively-optimized nonlinear grey Bernoulli model,Expert Systems with Applications,258, 125170.:2024-12-15
  • 丁松,叶俊涛,蔡智建. Multi-step carbon emissions forecasting using an interpretable framework of new data preprocessing techniques and improved grey multivariable convolution model,Technological Forecasting and Social Change,208,123720:2024-11-01
  • 丁松,蔡智建,吴彦祖,张华汉,神兴傲. FNNGM: A neural-driven fractional-derivative multivariate fusion model for interpretable real-time CPI forecasts,Knowledge-Based Systems,304, 112591:2024-11-25
  • 丁松,魏思月,张华汉,胡佳琦. Promotion or inhibition? Unveiling the heterogeneous effects and mechanism of the digital economy on carbon emission efficiency,Energy Economics,149, 108803.:2025-09-01
  • Xu Zhang,Yaoguo Dang,丁松,Haowei Wang,Feipeng Ding. Multi-output discrete grey model tailored for electricity consumption forecast,Applied Mathematical Modelling,139,115822:2025-03-01
  • 周伟杰,Jiaxin Chang,Huiming Jiang,丁松,Rongrong Jiang,Xupeng Guo. A novel grey seasonal model with time power for energy prediction,Expert Systems with Applications,259,125356:2025-01-01
  • 张骁雄,丁松,彭锐,伍国华,刘忠. 基于强化学习的多阶段资源分配对策模型,同济大学学报(自然科学版),2025年06期:985-992,8:2025-06-01
  • 丁松,蔡智建,叶俊涛. Multi-step carbon emissions forecasting using an interpretable framework of new data preprocessing techniques and improved grey multivariable convolution model,Technological Forecasting and Social Change(208):2024-01-01
  • 丁松,神兴傲,张华汉,吴彦祖,蔡智建. FNNGM: A neural-driven fractional-derivative multivariate fusion model for interpretable real-time CPI forecasts,Knowledge-Based Systems(304):2024-01-01
  • 丁松,蔡智建,秦兴欢,神兴傲. Comparative assessment and policy analysis of forecasting quarterly renewable energy demand: Fresh evidence from an innovative seasonal approach with superior matching algorithms,Applied Energy,367,123386:2024-08-01
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