基于 LASSO 方法和混沌群算法 - 逆向传播神经网络 - ARIMA 模型的重庆GDP预测
1School of Mathematics and Big Data, Chongqing University of Arts and Sciences, Chongqing, 402160, China.
Scientific reports
|September 11, 2023
概括
本研究引入了一个先进的模型用于重庆的国内生产总值 (GDP) 预测,实现了高准确度. 创新的CWOA-BP-ARIMA模型显著提高了对现有方法的预测,帮助经济规划.
科学领域:
- 计量经济学 计量经济学
- 宏观经济预测 宏观经济预测
- 计算经济学的计算经济学
背景情况:
- 准确的国内生产总值 (GDP) 预测对于有效的经济政策和战略决策至关重要.
- 现有的预测模型可能无法完全捕捉影响区域GDP的复杂动态.
研究的目的:
- 开发和验证用于重庆GDP预测的创新方法.
- 通过强大的特征选择,确定影响重庆GDP的关键经济指标.
- 评估拟议模型的预测性能与已建立的方法对比.
主要方法:
- 使用皮尔森相关性和拉索回归来确定关键经济指标的特征选择.
- 开发一个优化的CWOA-BP-ARIMA (黑猩猩优化算法-反向传播-自行回归集成移动平均线) 模型.
- 与随机森林,MLP (多层感知器),GA-BP (遗传算法逆向传播) 和CWOA-BP模型进行比较分析.
主要成果:
- CWOA-BP-ARIMA模型显示出卓越的预测准确性,实现了平均绝对误差 (MAE) 和根平均平方误差 (RMSE) 的显著降低.
- 与随机森林相比,MAE和RMSE的减少分别为95%和94.2%,并且与MLP和GA-BP相比观察到大幅度的减少.
- 与CWOA-BP模型相比,该模型实现了MAE的30.7%和RMSE的20.46%的减少,这表明性能有所提高.
- 重庆的国内生产总值预测显示出积极的增长,并提供了2022,2023和2024年的预测.
结论:
- CWOA-BP-ARIMA模型与LASSO特征选择相结合,为区域GDP预测提供了强大而准确的工具.
- 确定的主要经济指标为政策制定和经济规划提供了有价值的见解.
- 该模型的表现支持其在战略决策和宏观经济政策制定中的应用.
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