变压器:一种倾向于挖掘隐喻层次信息的变压器
Bo Peng1,2, Yuanming Ding1,2, Wei Kang1,2
1Communication and Network Laboratory, Dalian University, Dalian 116622, China.
Sensors (Basel, Switzerland)
|June 10, 2023
概括
这项研究为时间序列预测中的变压器模型引入了一种新的层次注意力机制. 它增强了功能多样性并减少了冗余性,在基准数据集上表现优于基线模型.
科学领域:
- 机器学习 机器学习
- 人工智能的人工智能
- 数据科学数据科学数据科学
背景情况:
- 变压器模型对机器学习产生了重大影响,特别是在时间序列预测方面.
- 目前在变压器中的多头注意力机制可能会导致特征冗余和有限的多样性.
- 需要改善变压器注意力机制中的特征提取和信息交互.
研究的目的:
- 为时间序列预测中的变压器模型提出一种新的层次注意力机制.
- 解决传统的多头注意力在功能多样性和信息冗余性方面的局限性.
- 增强变压器从多个角度捕获信息的能力.
主要方法:
- 引入一个层次化的注意力机制,以改善特征多样性.
- 整合图形网络用于全球特征聚合,以减轻诱导偏差.
- 在四个基准时间序列数据集上的实验验证.
主要成果:
- 建议的层次注意力机制与基线模型相比,表现优越.
- 该模型有效地增加了捕获的特征的多样性.
- 观察到减少了信息冗余和计算浪费.
结论:
- 新的等级注意力机制为时间序列预测提供了比传统的多头注意力显著的改进.
- 拟议的方法提高了特征表示和模型效率.
- 这项工作为推进基于变压器的时间序列预测提供了一个有希望的方向.
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