关键词: 数据融合, 经济状态, 经济关联, 机器学习

Abstract: Big data and machine learning are changing the research paradigm and methods of economic statistics. As a statistical product, macroeconomic data can describe economic status and economic connections in a certain range. Similar to integrated utilization of multi-source heterogeneous micro-data, macroeconomic data also has the potential for fusion, with its better data quality assurance. Based on an overview of data fusion methods of machine learning, this paper indicates a type of macroeconomic data fusion tasks, and proposes a data fusion approach, which aims to improve the prediction ability. Specifically, the potential merging characteristics of economic status data and economic connection data are demonstrated and a model representation method for extracting common feature information of these data is given. Then a fusion model is proposed, which can uniformly handle the task of unsupervised learning, supervised learning and semi-supervised learning on the basis of data fusion. An alternative iterative algorithm is followed for this model. The application of fusing the data from China Statistical Yearbooks (2017), Input-output Tables of China (2017), and China’s economic prosperity monthly reports (2017—2018) proves that our algorithm of data fusion has a better prediction accuracy compared with the non-fusion methods.

Key words: Data Fusion, Economic Status, Economic Connections, Machine Learning