通过基于季节性趋势分解的二维时间卷积密集网络改进长期多变量时间序列预测
1School of Information Science and Engineering, Shandong Normal University, Jinan, 250358, Shandong, China.
Scientific reports
|January 19, 2024
概括
本研究介绍了一种基于LOESS (STL) 和二维时间卷积密度网络 (2DTCDN) 模型的新型季节趋势分解,用于准确的长期多变量时间序列预测. 拟议的STL-2DTCDN有效地捕捉复杂的依赖关系和时间特征,优于现有的方法.
科学领域:
- 时间序列分析时间序列分析
- 机器学习 机器学习
- 深度学习 (Deep Learning) 是一种深度学习.
背景情况:
- 基于变压器的模型在长期多变量时间序列预测中往往表现不佳于简单的线性模型.
- 现有的方法难以捕捉复杂的相互依存关系和季节性和趋势等时间特征.
研究的目的:
- 提出一种新型模型,STL-2DTCDN,它解决了当前长期多变量时间序列预测的局限性.
- 通过有效利用时间特征和系列间的依赖关系来提高预测的准确性.
主要方法:
- 纳入基于LOESS (STL) 的季节性趋势分解来提取趋势和季节性组件.
- 设计一个二维时间卷积密集网络 (2DTCDN) 以模拟多变量时间序列之间的复杂相互依赖.
- 在六个不同的数据集上评估拟议的STL-2DTCDN模型.
主要成果:
- 与现有方法相比,STL-2DTCDN模型在长期多变量时间序列预测方面表现出更好的性能.
- 该模型成功地利用了季节性趋势特征和复杂的相互依赖性,以提高准确性.
结论:
- 拟议的STL-2DTCDN在长期多变量时间序列预测方面取得了重大进展.
- STL-2DTCDN通过将分解技术与先进的深度学习架构集成,为准确预测复杂时间序列数据提供了强大的框架.
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