具有局部和季节性适应的稀疏变压器用于多变量时间序列预测
Yifan Zhang1,2, Rui Wu3, Sergiu M Dascalu4
1Department of Computer Science and Engineering, University of Nevada, Reno, NV, 89557, USA. YifanZhang@MissouriState.edu.
Scientific reports
|July 10, 2024
概括
本研究介绍了Dozer Attention,这是一种用于多变量时间序列 (MTS) 预测的新机制,通过选择性地关注历史数据来提高效率. 在不牺牲准确性的情况下,Dozerformer在MTS预测任务中实现了卓越的性能.
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:
- 变压器通过捕捉长期的依赖性,在多变量时间序列 (MTS) 预测方面表现出色.
- 变压器中的正规注意力机制遭受二次时间复杂性,并处理整个历史序列,限制效率和可扩展性.
- 现有的方法在长序MTS预测中难以平衡准确性和计算成本.
研究的目的:
- 为多变量时间序列 (MTS) 预测开发一种高效准确的注意力机制.
- 为了解决基于变压器的MTS模型中二次时间复杂性和完整的历史序列处理的局限性.
- 引入Dozerformer框架,增强MTS预测能力.
主要方法:
- 提出了Dozer注意力机制,其中有三个稀疏的组件:局部,步骤和变量.
- 当地注意力集中在相邻的时间步骤上,步骤注意力关注预定义的间隔,变量注意力选择性地使用动态扩展的历史数据子集.
- 将Dozer Attention集成到MTS预测的Dozerformer框架中,并对其在九个基准数据集上的最新方法进行性能评估.
主要成果:
- 与现有的最先进的方法相比,Dozerformer框架在多个基准数据集上表现出更高的性能.
- 实验结果证实,排除历史序列的部分并不会降低预测的准确性.
- 通过拟议的稀疏注意力机制,计算效率得到了显著的改善.
结论:
- 该Dozer注意力机制有效地捕捉到基本的MTS数据属性,如本地,季节性和全球依赖性.
- Dozerformer为多变量时间序列预测提供了高效和准确的解决方案.
- 这些发现表明,选择性历史数据处理是提高MTS预测中变压器效率的可行策略.
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