Analysis and Prediction of Skill Types in Human Resource Planning

Abstract

The forecasting of skill types is an important part of human resource planning. In consideration of the data complexity and nonlinearity, we develop four mathematical methods to explore demands for skill types. We focus on the data analysis and compare the four models. Numerical results show that the neural network model based on sliding window (NN-SW) achieves the best prediction performance