The Performance of Grey Model and Auto-Regressive Integrated Moving Average for Human Resources Prediction in China

The human resource of planning involves using any number of sophisticated statistical procedures based on the analysis. At a more practical level, prediction demand involves determining the numbers and kinds of personnel that an organization will need at some point in the future. This research compared the forecasting performance between GM (1, 1), ARIMA, and GMARIMA to determine the best method to predict the number of Human Resources in China. The GM-ARIMA model is to establish a GM (1,1) model concerning the ARIMA model residual value and add the resulting residual prediction value to the original predicted value of the ARIMA model to compensate for the original predicted value and obtain GMARIMA model of the forecast results. Experimental results present that GM-ARIMA can be an effective way to improve prediction accuracy achieved by either of the models used separately.