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在股票市场时间序列上应用了一种基于模式分解的新解读方法: 2LE-CEEMDAN

Zinnet Duygu Akşehir¹, Erdal Kılıç¹

¹Department of Computer Engineering, Ondokuz Mayıs University Samsun, Samsun, Turkey.

PeerJ. Computer science

|March 4, 2024

概括

本研究介绍了基于两级率的完整集体实证模式分解与自适应噪声 (2LE-CEEMDAN),以消除财务时间序列. 新的2LE-CEEMDAN-LSTM-SVR模型通过保留关键的高频组件来提高股票市场预测的准确性.

科学领域:

时间序列分析时间序列分析.
金融预测财务预测
机器学习应用程序机器学习应用程序

背景情况:

时间序列数据通常包含噪声,非线性和非静止性,使预测任务复杂化.
在现有方法中排除高频组件可能会降低预测性能.
有效的denoising对于提高时间序列预测模型的准确性至关重要.

研究的目的:

为时间序列数据提出一种新的否定方法,2LE-CEEMDAN.
为金融时间序列开发一个综合预测模型,2LE-CEEMDAN-LSTM-SVR.
评估保护高频组件对预测准确性的影响.

主要方法:

开发了基于两级率的完整集体实证模式分解与自适应噪声 (2LE-CEEMDAN) 进行时间序列无声化.
集成的2LE-CEEMDAN与长短期内存 (LSTM) 和支持向量回归 (SVR) 进行混合预测模型.
应用该模型来预测金融股票市场指数的每日收盘价值.

主要成果:

2LE-CEEMDAN方法通过保留有价值的高频信息,有效地消除了财务时间序列.
与现有方法相比,2LE-CEEMDAN-LSTM-SVR模型在预测股票市场指数方面表现优越.
使用2LE-CEEMDAN进行denoising显著提高了混合模型的预测准确性.

结论:

拟议的2LE-CEEMDAN拒绝技术对于杂的金融时间序列是有效的.
2LE-CEEMDAN-LSTM-SVR模型为准确的股票市场预测提供了一个先进的方法.
通过先进的消除噪声来保护高频组件对于可靠的时间序列预测至关重要.

关键词:

拒绝这种行为,就是拒绝.Entropy Entropy 股票市场预测股票市场预测时间序列时间序列两个层次的CEEMDAN.

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