预测教育支出,使用一个通用的符合分数顺序非线性灰色系统模型
Caixia Liu1,2, Zhenguo Xu3, Keyun Zhao3
1College of Intelligent Education, Jiangsu Normal University, Xuzhou, China.
这项研究引入了一种新的非线性灰色预测模型,使用通用的符合分数计算来预测教育支出. 先进的模型通过适应复杂的时间序列数据来提高准确性.
科学领域:
- 计量经济学 计量经济学
- 时间序列分析时间序列分析
- 分数微积分的计算.
背景情况:
- 教育支出对于人力资本发展至关重要.
- 教育支出数据显示了由经济和社会因素影响的复杂的非线性结构.
- 传统的时间序列模型与教育支出的非线性动态作斗争.
研究的目的:
- 提出一种新的通用可符合的分数顺序非线性灰色预测模型.
- 加强复杂非线性系统的建模,特别是教育支出.
- 提高教育支出预测的准确性.
主要方法:
- 开发了一种通用的符合分数顺序非线性灰色预测模型.
- 引入了通用化的符合分数积累作为一个新的积累发生器.
- 利用了误差最小化原则和优化的模型顺序和累积生成运营商.
主要成果:
- 拟议的模型有效地捕捉了教育支出数据的非线性结构.
- 该模型通过调整其顺序和操作员来证明它对各种时间序列的适应性.
- 与现有模型相比,实现了更高的预测准确度.
结论:
- 概括的符合分数顺序非线性灰色预测模型是教育支出预测的强大工具.
- 这种新的方法为复杂的经济时间序列提供了更好的准确性和适应性.
- 该模型为教育发展规划中的物质保障提供了一个强大的方法.
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