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DRCNN:分解剩余卷积神经网络用于时间序列预测

Yuzhen Zhu¹, Shaojie Luo², Di Huang²

¹School of Automation and Electrical Engineering, Zhejiang University of Science and Technology, Hangzhou, 310000, China.

Scientific reports

|September 23, 2023

概括

变压器模型擅长长长期预测,但与小数据集和短序列作斗争. 一个新的分解残留卷积神经网络 (DRCNN) 通过利用数据连续性和多头注意力来提高准确性来改善时间序列预测.

科学领域:

人工智能的人工智能
机器学习机器学习
时间序列分析时间序列分析

背景情况:

变压器模型通过捕捉扩展的依赖关系,在长期时间序列预测中表现出强的表现.
变压器的局限性包括由于诱导偏差不足和对序列连续性的关注不足,导致小型数据集的性能差,阻碍了短期和长期预测.
现有的模型往往忽视了数据连续性在时间序列预测中的重要性.

研究的目的:

解决变压器模型在时间序列预测中的局限性,特别是对于小数据集和捕获序列连续性的局限性.
提出一种高效的基于变压器的设计,以提高预测的准确性.
利用卷积神经网络和变压器架构的优势,改进时间序列预测.

主要方法:

提出了一个分解残余卷积神经网络 (DRCNN),它将时间序列数据分解为残余和趋势组件.
一个专门的卷积块 (DR-Block) 旨在提取特征,灵感来自变压器的结构设计.
引入了模仿变压器中的多头注意力的多头序列方法,以处理更长的输入序列以获得更准确的预测.

主要成果:

拟议的DRCNN模型通过处理分解的剩余和趋势术语,有效地利用数据连续性.
DR-Block在特征提取方面表现出强大优势,与变压器的架构原则保持一致.
多头序列方法使网络能够处理更长的输入,从而提高了预测准确度.
该模型在几个基准数据集中实现了最先进的性能.

结论:

结合多头序列方法,DRCNN为时间序列预测提供了有效的解决方案,克服了标准变压器的局限性.
拟议的方法通过关注数据连续性,提高了捕捉短期和长期依赖的能力.
该模型的最先进的性能验证了它对各种时间序列预测任务的有效性.

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