时间序列分析的自主监督学习:分类学,进展和前景
概括
自主监督学习 (SSL) 通过减少对标记数据的依赖,显著提高时间序列分析. 本调查为时间序列提供了基于生成,基于对比和基于对抗的SSL方法的分类.
科学领域:
- 机器学习 机器学习
- 数据科学数据科学数据科学
- 人工智能的人工智能
背景情况:
- 自主监督学习 (SSL) 在时间序列任务中表现出强的表现.
- 通过SSL,减少了对广泛标记数据集的需求,通过预训练和微调,通过最小的标签实现了高性能.
- 现有的调查主要关注计算机视觉和自然语言处理,在时间序列SSL文献中留下了一个空白.
研究的目的:
- 对时间序列数据的最先进的自我监督学习方法进行全面审查.
- 为了解决缺乏针对时间序列SSL的专门调查的问题.
- 建立现有时间序列SSL方法的结构化概述和分类.
主要方法:
- 对现有的SSL和时间序列文献进行系统审查.
- 开发一种新的分类法,将时间序列SSL方法分为基于生成,基于对比和基于对抗的方法.
- 详细分析十个子类别,包括它们的核心原则,框架和权衡.
主要成果:
- 将时间序列SSL方法分类为生成,对比和对抗范式.
- 用于时间序列预测,分类,异常检测和集群的常用数据集的识别和摘要.
- 在时间序列领域讨论各种SSL技术的优缺点.
结论:
- 该调查为理解和推进时间序列分析中的自我监督学习提供了基础资源.
- 它强调了SSL在从未标记的时间序列数据中解锁洞察力的潜力.
- 在时间序列中概述了SSL的未来研究方向,为进一步的创新铺平了道路.
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