GBT:用于非静止时间序列预测的两级变压器框架
Li Shen1, Yuning Wei1, Yangzhu Wang1
1Beihang University, RM.807, 8th Dormitory, Dayuncun Residential Quarter, No. 29, Zhichun Road, Beijing 100191, PR China.
概括
本研究介绍了GBT,这是用于时间序列预测的新变压器框架. 通过使用双阶段方法,GBT提高了准确性.
科学领域:
- * 人工智能 * 人工智能
- * 机器学习 * 机器学习
- * 数据科学数据科学
背景情况:
- * 时间序列预测 变压器 (TSFT) 模型经常过度匹配,特别是非静止数据,由于未知解码器输入的初始化不佳.
- *现有的方法在预测任务中难以处理输入和预测序列之间的不同统计性质.
研究的目的:
- * 解决TSFT中的过度装配和统计属性不匹配问题,以改进时间序列预测.
- * 引入一个新的框架,GBT (良好的开始变压器),以提高预测性能.
主要方法:
- * 拟议的GBT,一个两阶段的变压器框架,将预测解成自动回归和自我回归阶段.
- * 引入了"良好的开始"概念,其中自动回归输出初始化了自动回归阶段.
- *开发了一个错误分数修改模块,以提高自回归阶段预测能力.
主要成果:
- *在七个基准数据集上,GBT显著优于最先进的TSFT模型 (FEDformer,Pyraformer,ETSformer) 和其他预测模型 (SCINet,N-HiTS).
- * GBT 通过正规的注意力和卷积实现了卓越的性能,表现出较低的时间和空间复杂性.
- * GBT框架被证明是可通用的,并且能够增强其他预测模型.
结论:
- * GBT通过其双阶段方法提供更好的输入初始化,有效地减轻了TSFT的过度安装.
- * 拟议的框架为时间序列预测提供了更有效,更准确的解决方案,优于现有的方法.
- * GBT 的模块化设计允许与其他模型集成,以提高其预测能力.
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