Combining Fourier Fractional GM(1,1|sin) Model with Rat Swarm Optimizer for Employment Rate Prediction

Accurate prediction of employment rate of graduated students can greatly help education authorities to make informed decisions as well as for universities to adjust their teaching plans. Unfortunately, prediction of the employment rate of graduated students is still a difficult problem because the historical employment rate data exhibits fluctuations. In this paper, a new method is proposed for employment rate prediction using fractional gray GM(1,1|sin) model, which aims to alleviate the effect of data fluctuation on prediction accuracy and increase the contribution of new data in the prediction procedure. To further decrease prediction error, a Fourier series is adopted to model the residual series. The proposed model, called Fourier Fractional GM(1,1|sin) Model(FFGMsin), is used to predict the employment rate of graduated students of Yansh an University from 2010 to 2019. Results show that the proposed method can obtain more accurate prediction results than GM(1,1) model and its variants.(c) 2024 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC.