Jianhua Hao1, Fangai Liu2

  • 1School of Information Science and Engineering, Shandong Normal University, Jinan, 250358, Shandong, China.

Scientific reports
|January 19, 2024
PubMed
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.

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