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预测真正的卫星一氧化碳数据与集体实证模式分解,奇数值分解和移动平均线的预测

Sameer Poongadan^1,2, M C Lineesh¹

¹Department of Mathematics, National Institute of Technology Calicut, Calicut, Kerala, India.

Journal of applied statistics

|November 15, 2024

概括

这项研究引入了一种新的EMD-SVD-MA模型,用于准确预测一氧化碳. 拟议的技术有效预测大气中的一氧化碳水平,优于现有方法.

科学领域:

环境科学环境科学
大气化学大气化学
数据科学数据科学数据科学

背景情况:

一氧化碳 (CO) 是一个重要的大气污染物,与严重的健康问题有关.
准确预测大气中的二氧化碳对于环境监测和公共卫生至关重要.
现有的时间序列模型与非线性和非静止的大气数据作斗争.

研究的目的:

开发和评估一种新的时间序列预测技术,用于大气一氧化碳.
为应对非线性和非静止CO数据所带来的挑战.
将拟议方法的有效性与既有预测模型进行比较.

主要方法:

这项研究提出了一个集体实证模式分解 (EEMD),单数值分解 (SVD) 和移动平均 (MA) 技术 (EEMD-SVD-MA).
EEMD将时间序列数据分解为内在模式函数 (IMF).
使用SVD进行国际货币基金组织的撤销,MA则预测每个撤销的国际货币基金组织组件的未来价值.

主要成果:

在预测大气一氧化碳数据方面,EEMD-SVD-MA模型表现出卓越的性能.
两个变体,EEMD-SVD-MA(3) 和EEMD-SVD-MA(4),进行了测试,并显示出高效率.
拟议的模型优于其他技术,包括LSTM,EMD-LSTM,EMD-MA,EMD-MA和CEEMDAN-MA.

结论:

EEMD-SVD-MA技术是预测非线性和非静止大气一氧化碳数据的有效方法.
与现有的预测模型相比,这种方法提供了更高的准确性和效率.
这些发现对大气污染监测和健康风险评估有重大影响.

关键词:

15A18 它们是什么?37M1010 这样就好了.68T07 这是一个很好的例子.内在模式功能内在模式功能经验模式分解分解整体实证模式分解组合长期短期内存网络内存网络内存移动平均线是移动平均线.单一价值分解分解的方法

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