Prediction of employment and reemployment in China based on SARIMA-ARIMAX model

Yingbing Fan; Jinxiu Hu; Xuemei Lu

doi:10.1117/12.2652499

20 October 2022 Prediction of employment and reemployment in China based on SARIMA-ARIMAX model

Yingbing Fan, Jinxiu Hu, Xuemei Lu

Proceedings Volume 12350, 6th International Workshop on Advanced Algorithms and Control Engineering (IWAACE 2022); 123500W (2022) https://doi.org/10.1117/12.2652499
Event: 6th International Workshop on Advanced Algorithms and Control Engineering (IWAACE 2022), 2022, Qingdao, China

Abstract

Employment has an important impact on the social development needs and economic development needs of the country. It is also the fundamental demand for people’s increasingly better life and the basic premise and important way to improve people 's lives. In order to understand the current employment form in China, the time series analysis method is used to predict the number of new employment and re-employment in China. According to the number of new employment and re-employment in cities and towns from the first quarter of 2013 to the fourth quarter of 2021, the number of new employment and re-employment in cities and towns in the first quarter of 2022 is predicted, and the combined prediction model of seasonal time series model and dynamic regression model is constructed. From the prediction results, most of the combined prediction accuracy is concentrated in less than 3%. Compared with the single prediction model, the prediction accuracy has been significantly improved. The number of predicted people is 237.7535 million, and the prediction results are credible. It can provide valuable reference for the problems existing in the employment situation.

Citation Download Citation

Yingbing Fan, Jinxiu Hu, and Xuemei Lu "Prediction of employment and reemployment in China based on SARIMA-ARIMAX model", Proc. SPIE 12350, 6th International Workshop on Advanced Algorithms and Control Engineering (IWAACE 2022), 123500W (20 October 2022); https://doi.org/10.1117/12.2652499

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KEYWORDS

Autoregressive models

Statistical modeling

Data modeling

Error analysis

Statistical analysis

Scientific research

Safety

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