Macroeconomic Early Warning Method Based on Support Vector Machine under Multi-Sensor Data Fusion Technology

Abstract

This article focuses on the macroeconomic early warning based on support vector machines under multi-sensor data fusion technology. The economic crisis has always been a topic of great concern to the entire world, and there has never been a lack of research and prevention of it in the development process. At present, the economic early warning system that is frequently mentioned includes statistical models and artificial intelligence models. Economic early warning is not just a topic for discussion on a national basis. In the personal field, a thorough enough understanding of economic early warning can also better control investment incomes such as stocks. Analyzing the direction of macroeconomic policies has also become an indispensable ability for investors. In order not to be affected by the economic crisis, early warning is a crucial link. Therefore, in this article, a multi-sensor data fusion macroeconomic early warning model based on support vectors is proposed. In addition, this article also discusses each subpart separately. First, this article mentions the multi-sensor data fusion model. It conducts reasonable collection and control of data information in multiple sensors and uses computers and smart devices to improve system performance and make data clearer. At the same time, this article shows the JDL model of data fusion. It can optimize signals and processes and targets and conduct situation assessment and effect assessment. Then, this article analyzes the support vector machine economic early warning system. It discusses the four parts of linear support vector machine, nonlinear support vector machine, SVC macroeconomic early warning principle, and support vector classification macroeconomic early warning system. By gradually analyzing the macroeconomic early warning process of support vector machines, this study uses the optimal hyperplane to optimize toward the direction of minimizing economic risks. Then, this article talked about the macroeconomic early warning method based on neural network. The difficult points of traditional early warning models can be discarded. It more easily handles the complex algorithms, qualitative indicators, and quantitative indicators of highly nonlinear models, and it demonstrates the macroeconomic early warning system that optimizes the BP neural network with genetic algorithms to solve the various shortcomings of the macroeconomic early warning process. Finally, this study conducts the multi-sensor data fusion macroeconomic early warning model experiment based on support vector machine. It is divided into three parts. In the first part, the model, design system, and application of 60 sets of sensors are compared with traditional weighted least-squares filtering, and it is concluded that the accuracy and trustworthiness prediction effect of this model is better. The second part uses the data of listed companies to conduct experiments, which verify the performance of the model with different data sets. It can be obtained that its prediction effect is better. The third part is to compare the performance with several traditional models, and it is concluded that the convergence effect is good and the error is small. Its average accuracy is 5.63% higher than the average accuracy of the highest precision warning model in the traditional model. This article discusses and concludes that this model has a good future in macroeconomic early warning.