Affiliations: [a]
Beijing Normal University, Zhuhai, Zhuhai City, Guangdong Province, China
| [b] Faculty Business, City University of Macau, Macau, China
| [c] School of Business, Macau University of Science and Technology, Macau, China | [d]
Huizhou University, Huizhou City, Guangdong Province, China
Correspondence:
[*]
Corresponding author. Zhuohang Li, Faculty Business, City University of Macau, Macau, China. E-mail: WilhelminaaDbV@yahoo.com.
Abstract: In order to improve the ability of automatic estimation and prediction of economic trend index, an intelligent prediction model of economic trend index based on rough set support vector machine is proposed. The statistical analysis of intelligent prediction of economic trend index is carried out by using the equivalent approximate linear model, and the regression analysis model of intelligent prediction of economic trend index is established. Combining with the rough set support vector machine big data fusion technology, the feature extraction and information mining are carried out in the process of intelligent prediction of economic trend index, and the statistical time analysis series of economic trend index is constructed. The spatial distribution of economic trend index distribution series is reconstructed, and the economic trend is evaluated and predicted in the high dimensional economic trend index forecast series distribution space. The principal component characteristic analysis and fuzzy closeness analysis of economic trend index are carried out by using fuzzy relational degree scheduling method. Taking economic cost, economic development prospect and economic growth rate as constraint indexes, the method of multi-factor joint estimation is adopted. Realize economic trend index intelligent forecast. The simulation results show that the accuracy of fast estimation of economic trend index is high, the time cost is small, and the ability of intelligent prediction is stronger.
