Combination forecast of labour population participation rate in China based on ANN-GM ( 1,1 ) model

Jinhui Xiao

doi:10.1117/12.2635970

6 May 2022 Combination forecast of labour population participation rate in China based on ANN-GM ( 1,1 ) model

Jinhui Xiao

Proceedings Volume 12256, International Conference on Electronic Information Engineering, Big Data, and Computer Technology (EIBDCT 2022); 122562N (2022) https://doi.org/10.1117/12.2635970
Event: 2022 International Conference on Electronic Information Engineering, Big Data and Computer Technology, 2022, Sanya, China

Abstract

Accurate prediction and judgment of the future labor force participation rate will be conducive to the scientific formulation of future employment policy, actual population policy and social security policy by the authorities. In order to propose a suitable labor participation rate prediction model, this paper selects 12 indicators to build the model from four aspects of population structure, economic development, social environment and living conditions based on the analysis of China's social environment. The combination prediction model of neural network model (ANN) and grey model GM (1,1) is constructed to empirically analyze the labor participation rate of China's population in the past 20 years. The average absolute error (MAE), root mean square error (RMSE) and mean absolute percentage error (MAPE) are used as application evaluation index to compare the accuracy of the prediction model. The results show that : (1) The RMSE and MAE error parameters of the combined model based on ANN-GM(1,1) are smaller than those of single prediction. (2) The ANN-GM(1,1) combination forecasting model with weights of ( 0.982,0.018 ) has the best prediction effect.

Citation Download Citation

Jinhui Xiao "Combination forecast of labour population participation rate in China based on ANN-GM ( 1,1 ) model", Proc. SPIE 12256, International Conference on Electronic Information Engineering, Big Data, and Computer Technology (EIBDCT 2022), 122562N (6 May 2022); https://doi.org/10.1117/12.2635970

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