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  • 标题:Enterprise Power Consumption Data and GDP Forecasting Based on Ensemble Algorithms
  • 本地全文:下载
  • 作者:Yibin Xu ; Lu He ; Ying Liang
  • 期刊名称:E3S Web of Conferences
  • 印刷版ISSN:2267-1242
  • 电子版ISSN:2267-1242
  • 出版年度:2021
  • 卷号:233
  • 页码:1030
  • DOI:10.1051/e3sconf/202123301030
  • 出版社:EDP Sciences
  • 摘要:This paper focuses on the development of regional GDP and proposes a method proposed for forecast of enterprise power consumption data and GDP based on ensemble algorithms. The enterprise power consumption data are used as independent variables and GDP data as dependent variables. A multiple linear regression model is selected as the primary learner for training and its outputs will be sorted into a new dataset of input features to train a secondary learner. The forecast of GDP is thus realized through ensemble learning.
  • 其他摘要:This paper focuses on the development of regional GDP and proposes a method proposed for forecast of enterprise power consumption data and GDP based on ensemble algorithms. The enterprise power consumption data are used as independent variables and GDP data as dependent variables. A multiple linear regression model is selected as the primary learner for training and its outputs will be sorted into a new dataset of input features to train a secondary learner. The forecast of GDP is thus realized through ensemble learning.
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