Prediction Model for Dynamic Fluctuations in Salary by Integrating Multiple Time Series

In order to improve the accuracy of predicting the dynamic fluctuation trend of employee salaries and reduce errors, this study conducted a research on a salary dynamic fluctuation trend prediction model that integrates multiple time series. This paper first preprocesses the outlier detection, removal, and filling of multivariate time series data on salary fluctuations. Based on this, using segmented aggregation approximation, the trend extraction of multivariate time series of salary is completed to obtain the salary trend series. Finally, based on generative adversarial networks, a dynamic salary fluctuation trend prediction model is constructed. The experimental results show that the proposed method has a prediction error of less than 2.71%, which is better than the comparative method and has greater application value.