组合模型下陕西科技人才需求数量预测

基于陕西省科技人才发展现状，运用统计年鉴动态数据，结合Granger因果关系检验和逐步回归分析构建时间序列-BP神经网络组合预测模型，预测2021—2025年陕西省科技人才需求数量。研究表明：与灰色GM（1,1）模型相比，时间序列模型对规上工业企业工业总产值的预测精度更高；陕西省规上工业企业总产值能够有效预测陕西省科技人才需求数量；陕西省科技人才需求数量将呈指数趋势显著上升，到2025年达26.7万人。以此从关注陕西省重点产业、完善陕西省科技人才管理方案和充分利用陕西省科技人才3个方面提出相关建议，充分激发科技人才创新潜能。

Forecasting the Aemand Quantity of Science and Technology Talents in Shaanxi Province under the Combination Model

Based on the development status of scientific and technological talents in Shaanxi Province, using the dynamic data of statistical yearbook, combined with Granger causality test and stepstep regression analysis, the prediction model of time series and BP neural network is established to predict the demand for scientific and technological talents in Shaanxi Province from 2021 to 2025. The results show that compared with the grey GM (1,1) model, the time series model can predict the total industrial output value more accurately. The total output value of regulated industrial enterprises in Shaanxi province can effectively predict the demand for scientific and technological talents in Shaanxi Province. The demand for scientific and technological talents in Shaanxi province will increase exponentially and reach 267 000 by 2025. In order to fully stimulate the innovation potential of scientific and technological talents, suggestions are put forward from three aspects: paying attention to key industries in Shaanxi Province, improving the management plan of scientific and technological talents in Shaanxi Province and making full use of them.