机器人时间系列恢复和分类使用测试时间噪音模拟器网络
Eun Som Jeon1, Suhas Lohit2, Rushil Anirudh3
1Geometric Media Lab, Arizona State University, Tempe, AZ, USA.
概括
这项研究引入了一种新的框架,用于在测试时清理损坏的时间序列数据,即使使用未知的噪声模型. 该方法使用未标记的数据来估计噪声,使预先训练有素的分类器能够进行可靠的预测.
科学领域:
- 数据科学数据科学数据科学
- 机器学习 机器学习
- 信号处理 信号处理
背景情况:
- 时间序列数据经常受到损坏,例如缺少样本和传感器噪声.
- 当在部署期间不知道噪音模型时,纠正这些问题是具有挑战性的.
- 这阻止了在清洁源数据上使用预先训练有素的分类器.
研究的目的:
- 开发一个时间序列数据的一般框架,可以使用未标记的数据在测试时估计噪声模型.
- 为了使受过预训练的分类器能够在损坏的时间序列数据上使用.
- 展示一个强大的时间序列分析和预测方法.
主要方法:
- 采用了一个合解码器模型和一个学习噪声模型模拟器.
- 该框架在测试时使用未标记的样本完全估计了噪声模型.
- 该方法清除损坏的数据以匹配源训练数据统计数据.
主要成果:
- 拟议的框架有效地"清理"损坏的时间序列数据.
- 清除的数据可以直接使用预训练的分类器进行准确的预测.
- 跨不同领域和传感器类型的实证研究证实了该方法的有效性和普遍性.
结论:
- 开发的框架为处理受损的时间序列数据提供了一个通用的解决方案,而无需先前了解噪声模型.
- 这种方法显著提高了机器学习模型在现实应用中的稳定性.
- 该方法可在各种传感器数据类型中进行可靠的时间序列分析和预测.
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