基于神经网络的 SARIMA 模型对教师需求和培训进行匹配预测

IF 0.6 Q4 COMPUTER SCIENCE, INFORMATION SYSTEMS

International Journal of Information Technology and Web Engineering Pub Date : 2023-11-15 DOI:10.4018/ijitwe.333637

Jianliu Zhu

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引用次数: 0

摘要

本研究引入 "SARIMA改进模型+皮尔逊相关系数 "方法，对2016年1月至2019年12月江苏省学校大数据岗位需求进行预测。同时，还探讨了高校需求与供给之间的匹配性。该模型具有容错性强、预测速度快等特点，解决了高校人才培养与师资需求脱节的问题。SARIMA-BP 模型预测了江苏省大数据教师需求趋势。该模型虽未经过招聘数据预测的检验，但在大型数据库的支持下，均方根误差达到了 7.66，显示了较高的精度和可靠性。基于匹配研究和江苏省本地大数据教育产业，在 "一体两翼一尾 "框架下提出了对策建议。这一简明扼要的总结突出了研究的核心内容和目标。

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查看原文本刊更多论文

Matching Prediction of Teacher Demand and Training Based on SARIMA Model Based on Neural Network

This study introduces the ‘SARIMA Improved Model + Pearson Correlation Coefficient' approach to predict the demand for big data jobs in Jiangsu Province schools from January 2016 to December 2019. It also explores the matching between demand and supply in universities. The model is fault-tolerant, offers fast predictions, and addresses the disconnect between college talent training and teacher demand. The SARIMA-BP model predicts the trend of big data teacher demand in Jiangsu Province. The model, though untested in recruitment data prediction, with a large database, achieves root mean square error of 7.66, indicating high precision and reliability. Based on matching research and the local big data education industry in Jiangsu Province, countermeasures and suggestions are presented under the “one body, two wings, and one tail” framework. This concise summary highlights the research's core components and objectives.

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来源期刊

International Journal of Information Technology and Web Engineering COMPUTER SCIENCE, INFORMATION SYSTEMS-

CiteScore

2.60

自引率

0.00%

发文量

期刊介绍： Organizations are continuously overwhelmed by a variety of new information technologies, many are Web based. These new technologies are capitalizing on the widespread use of network and communication technologies for seamless integration of various issues in information and knowledge sharing within and among organizations. This emphasis on integrated approaches is unique to this journal and dictates cross platform and multidisciplinary strategy to research and practice.