A security monitoring and warning method for economic growth and unemployment in financial social networks

Abstract

Accurately predicting economic growth and unemployment rate is an important prerequisite for macroeconomic regulation in financial social networks. Traditional security monitoring and warning methods often rely on lagging official statistical data, making it difficult to capture nonlinear correlations and sudden signals in economic dynamics in real time. Therefore, this paper proposes a Boruta-SHAP and Transformer-XL (BST-XL) security monitoring and warning model based on Transformer-XL. The framework first constructs a Boruta-SHAP (BS) two-stage feature selection method based on ensemble learning framework and Shapley Additive Explanations (SHAP) interpretability analysis. By identifying important features related to economic time series task from a given feature set, focusing on economic growth and unemployment rate, the problem of feature redundancy in economic time series data is effectively solved. Secondly, by using Transformer-XL to measure the impact of historical economic sequences on recent economic dynamics, the latest state characteristics of economic dynamics can be obtained. This is beneficial for accurately predicting the dynamic changes of economic growth and unemployment rates. Experimental analysis shows that BST-XL performs well in predicting economic growth and unemployment rate, with higher security monitoring accuracy. It is suitable as a predictive model for economic growth and unemployment rate in financial social networks.