一个改进的GM(1,1) 模型基于加权的MSE和最佳加权背景值及其应用
Won-Chol Yang1, Song-Chol Ri2, Kyong-Su Ri2
1Kim Chaek University of Technology, Pyongyang, Democratic People's Republic of Korea. ywch71912@star-co.net.kp.
Scientific reports
|December 3, 2024
概括
这项研究引入了一种改进的GM(1,1) 模型,OB-WMSE-GM(1,1),该模型显著提高了预测准确性. 在各种模拟和现实应用中,新型号的性能优于传统的GM{1,1}.
科学领域:
- * 数学建模和预测
- * 时间序列分析和预测.
背景情况:
- *GM(1,1) 模型因其最小的数据要求,低计算复杂性和缺乏统计假设而受欢迎.
- * 现有的GM(1,1) 模型的一个主要局限性是它们的预测准确性不足,尽管它们的适配准确性令人满意.
研究的目的:
- * 开发一个改进的GM(1,1) 模型,具有更高的预测准确度.
- * 解决传统GM模型中预测性能差的常见缺点.
主要方法:
- * 提出了一个优化的GM(1,1) 模型,包含加权平均二次误差 (MSE) 和一个最佳加权背景值.
- * 开发了OB-WMSE-GM(1,1) 模型,以提高预测能力.
主要成果:
- * OB-WMSE-GM(1,1) 模型在模拟和现实实例中 (LCD电视生产,原油加工) 与典型的GM(1,1) 模型相比,显示了明显较低的拟合和预测错误 (MSE,WMSE,MAPE).
- *例如,在指数函数模拟中,拟议模型的预测MSE为0.015485,远远超过典型的GM(1,1) 的1.144524.
- * 在年度液晶电视产量和原油加工量预测中也观察到类似的改善.
结论:
- * 拟议的OB-WMSE-GM(1,1) 模型在预测准确性方面比传统的GM(1,1) 模型有了显著的改进.
- *通过将拟议的方法与残余建模和优化初始条件等技术相结合,可以进一步提高性能.
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