Demand Analysis of Science and Technology Talents Based on Time Series - BP Neural Network Model

DOI

10.2991/978-94-6463-042-8_37How to use a DOI?

Keywords

Science and Technology Talents; Demand Analysis; Granger Causality Test; GM(1,1) Model; Time Series Model; BP Neural Network Model

Abstract

Using Eviews7 and SPSS25, Granger causality test and stepwise regression analysis were carried out on the statistical data of China Statistical Yearbook, Shaanxi Statistical Yearbook and Xi'an Statistical Yearbook from 2010 to 2020. On this basis, a time series-BP neural network combined prediction model was constructed, and MATLAB software was used to train BP neural network for relevant data. Accordingly, the demand for scientific and technological talents in Shaanxi Province from 2021 to 2025 was predicted. The following conclusions were drawn: the total output value of industrial enterprises in Shaanxi Province can effectively predict the demand for scientific and technological talents; compared with the GM(1,1) model, the time series model has higher prediction accuracy for the gross industrial output value of industrial enterprises on the specification; the demand for science and technology talents in Shaanxi Province is estimated to increase exponentially.

Copyright

© 2023 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Jing Luo
AU  - Jingwen Qu
PY  - 2022
DA  - 2022/12/29
TI  - Demand Analysis of Science and Technology Talents Based on Time Series - BP Neural Network Model
BT  - Proceedings of the 2022 International Conference on mathematical statistics and economic analysis (MSEA 2022)
PB  - Atlantis Press
SP  - 248
EP  - 254
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6463-042-8_37
DO  - 10.2991/978-94-6463-042-8_37
ID  - Luo2022
ER  -