Abstract：Objective By constructing combination forecast models to improve the prediction accuracy, and to provide the basis for the rational allocation of health resources. Methods ARIMA and BP neural network were combined to establish an ARIMA-BP neural network model. ARIMA model is used to predict the linear part of the data of health human resource, and the residual of the model is nonlinear, and then the BP neural network model is used to forecast the nonlinear residual sequence. The prediction of the two models is added as the forecast value of the sequence, and the random error generated by the BP neural network model is used as the prediction error of the ARIMA-BP neural network model, and the final prediction result is obtained. Finally the data of the number of health technical personnel in Shandong Povince from 1995 to 2012 were applied to verify the model, and to forecast the number of health technical personnel in Shandong Province from 2013 to 2015. Results The verification of the number of health technical personnel data of Shandong Povince from 1995 to 2012 showed that the combination forecast model which was formed by using ARIMA model to predict the linear data, and using BP neural network to predict the error of ARIMA, reduced the ARIMA model prediction error and improved the accuracy of prediction. The combined model forecast the numbers of health technicians in 2013, 2014 and 2015 in Shandong Province were 581 297, 635 013 and 686 465 respectively. Conclusion The combination forecasting model can make use of a variety of sample information to a large extent, and consider the problem more systematically than a single comprehensive predictive model to improve prediction accuracy. Therefore, it may be applied in human resources for health.
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