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A Rapid Method for Modeling a Variable Cycle Engine
Published on: August 13, 2019
PRformer:用于多变量时间序列预测的金字塔循环变压器
Yongbo Yu1, Weizhong Yu1, Feiping Nie1
1School of Artificial Intelligence, OPtics and ElectroNics (iOPEN) and the Key Laboratory of Intelligent Interaction and Applications (Ministry of Industry and Information Technology), Northwestern Polytechnical University, Xi'an, 710072, Shaanxi, PR China.
概括
变压器模型在时间序列顺序上扎. 我们的新金字塔RNN嵌入式 (PRE) 增强了变压器,可以更好地预测时间序列,改善长时间回顾窗口的性能.
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:
- 变压器模型虽然强大,但由于它们的自我注意力机制,缺乏固有的时间顺序意识.
- 依赖定位嵌入限制了变压器在时间序列预测中的有效性,特别是在扩展的回顾窗口.
- 现有的方法难以捕捉复杂的时间依赖关系,这对于准确的预测至关重要.
研究的目的:
- 开发一种改进的方法,在变压器模型中编码时间顺序,用于时间序列预测.
- 为了增强时间序列的表示,特别是对于更长的回顾窗口.
- 将一种新的嵌入技术与变压器架构集成,以获得卓越的预测性能.
主要方法:
- 引入了金字塔RNN嵌入式 (PRE),结合了金字塔1D卷积层和循环神经网络 (RNN).
- PRE构建了维护时间顺序的多尺度卷积特征,并学习了顺序顺序敏感的表示.
- 集成PRE与标准变压器编码器,以创建用于多变量时间序列预测的PRformer模型.
主要成果:
- 与标准的变压器方法相比,PRformer模型显示了显著的性能提升.
- 在各种真实世界时间序列数据集上取得了最先进的结果.
- 有效地利用了更长的回顾窗口,展示了拟议的时间表示的稳定性.
结论:
- 拟议的金字塔RNN嵌入 (PRE) 有效地解决了标准变压器模型的时间顺序限制.
- 集成PRE显著提高了变压器在时间序列预测任务中的性能,特别是长时间回顾数据.
- 强大的时间表示对于最大限度地发挥变压器架构在预测建模中的潜力至关重要.
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