基于经验模式分解的时间序列预测中的信息泄露研究
Xinyi Yang1,2, Jingyi Li1, Xuchu Jiang3,4
1School of Statistics and Mathematics, Zhongnan University of Economics and Law, Wuhan, 430073, China.
Scientific reports
|November 16, 2024
概括
本研究引入了三种新的分解方法,以防止时间序列预测中的信息泄露,显著提高了水质预测的准确性. 新方法通过解决当前分解技术中常见的"过度装配"问题来增强预测模型.
科学领域:
- 环境科学 环境科学
- 数据科学数据科学数据科学
- 工程 工程师 工程师 工程师
背景情况:
- 时间序列分析对于预测各种领域的未来趋势至关重要,包括金融,经济和环境监测.
- 传统的分解-整合-预测策略经常遭受"信息泄漏",测试集数据影响分解,导致虚幻的准确性.
- 现有的方法在模型训练和测试期间与非静止序列和过拟合作斗争.
研究的目的:
- 提出和评估三个改进的分解策略:滑动窗体实证模式分解 (SW-EMD),单次训练和多次分解 (STMP-EMD) 和多次训练和多次分解 (MTMP-EMD).
- 为解决与当前时间序列预测分解技术固有的"信息泄漏"问题.
- 提高水质预测模型的准确性和可靠性.
主要方法:
- 开发了三个改进的类EMD分解方法:SW-EMD,STMP-EMD和MTMP-EMD.
- 将这些方法与CEEMDAN-MSBTCN-BiLSTM-DMAttention模型结构集成,其中包含基于等号相似性的依赖矩阵.
- 应用了新型模型来预测水质指标:pH,溶解氧 (DO) 和甲 (KMnO4).
主要成果:
- 提出的模型在预测pH,DO和KMnO4水平方面表现良好.
- 与主流LSTM模型相比,精度提高了1.958% (RMSE) 和0.853% (MAPE).
- 改进的分解方法有效地缓解了"信息泄漏",并减少了模型过度装配.
结论:
- 提出的三个分解改进策略成功地解决了时间序列预测中的"信息泄露"问题.
- 创新的CEEMDAN-MSBTCN-BiLSTM-DMAttention模型结构,结合改进的分解,为水质预测提供了一个强大的解决方案.
- 这项研究为水质预测提供了有效的实验框架,提高了模型的可靠性,并解决了过度拟合的挑战.
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