自主监督的时间序列表示学习通过交叉重建变压器学习.
概括
时间序列的自我监督表示学习是通过交叉重建变压器 (CRT) 增强的. 这种新的方法使用跨域重建和实例歧视,在真实世界数据集上提高了2-9%的性能.
科学领域:
- 机器学习 机器学习
- 时间序列分析时间序列分析
- 信号处理 信号处理
背景情况:
- 自主监督的表示学习对于时间序列数据至关重要,因为标记样本有限.
- 现有的对比式学习方法面临的挑战是采样政策和对构造.
- 对于改善时间序列表示,对时间谱相关性的有效建模尚未得到充分研究.
研究的目的:
- 为时间序列表示提出一种新的自我监督学习框架.
- 解决现有方法在采样和时间-光谱相关性建模方面的局限性.
- 增强学习时间序列表示的容量和区分能力.
主要方法:
- 引入了使用跨域掉落重建任务的交叉重建变压器 (CRT).
- 采用快速里埃转换 (FFT) 来获得频域,并在时间和频域中随机丢弃补丁.
- 采用了转换器架构来进行掉落的时间光谱建模,并结合实例歧视约束 (IDC) 和课程学习 (CL).
主要成果:
- 在多个现实数据集中,CRT实现了最先进的性能,在多个现实数据集中比现有方法高出2%-9%.
- 掉落的时间光谱建模有效地捕捉了跨领域的相关性.
- 实例歧视约束 (IDC) 提高了决策边界,课程学习 (CL) 提高了预训练的稳定性.
结论:
- 拟议的交叉重建变压器 (CRT) 为自我监督的时间序列表示学习提供了一种优越的方法.
- CRT有效地建模了时间-光谱相关性,克服了以前方法的局限性.
- 该方法在现实应用中显示出显著的性能提升和稳定性.
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