面向鲁棒多元时间序列异常检测的因果感知对比学习
Causality-Aware Contrastive Learning for Robust Multivariate Time-Series Anomaly Detection
AI总结：
本文提出因果感知对比学习方法CAROTS，通过因果保持与扰动样本构建正负对训练编码器，并引入相似性过滤的单类对比损失，在多个数据集上验证了因果关系融入可提升多元时间序列异常检测的鲁棒性。
摘要：
利用多元时间序列中复杂的变量间因果关系，为更鲁棒、更可靠的多元时间序列异常检测（MTSAD）提供了一条有前景的途径，但这仍是一个尚未充分探索的研究领域。本文提出了面向鲁棒多元时间序列的因果感知对比学习（CAROTS），这是一种将因果概念融入对比学习的新型MTSAD流程。CAROTS采用两个数据增强器来获得因果保持样本和因果扰动样本，分别作为广泛的正常变化和合成异常。以因果保持样本和因果扰动样本分别作为正样本和负样本，CAROTS执行对比学习来训练一个编码器，其潜在空间基于因果关系将正常样本和异常样本分开。此外，CAROTS引入了一种相似性过滤的单类对比损失，鼓励对比学习过程逐步纳入更多具有共同因果关系但语义多样的样本。在五个真实世界数据集和两个合成数据集上的大量实验验证了因果关系的融入赋予CAROTS更强的MTSAD能力。代码可在https://github.com/kimanki/CAROTS获取。
英文摘要：
Utilizing the complex inter-variable causal relationships within multivariate time-series provides a promising avenue toward more robust and reliable multivariate time-series anomaly detection (MTSAD) but remains an underexplored area of research. This paper proposes Causality-Aware contrastive learning for RObust multivariate Time-Series (CAROTS), a novel MTSAD pipeline that incorporates the notion of causality into contrastive learning. CAROTS employs two data augmentors to obtain causality-preserving and -disturbing samples that serve as a wide range of normal variations and synthetic anomalies, respectively. With causality-preserving and -disturbing samples as positives and negatives, CAROTS performs contrastive learning to train an encoder whose latent space separates normal and abnormal samples based on causality. Moreover, CAROTS introduces a similarity-filtered one-class contrastive loss that encourages the contrastive learning process to gradually incorporate more semantically diverse samples with common causal relationships. Extensive experiments on five real-world and two synthetic datasets validate that the integration of causal relationships endows CAROTS with improved MTSAD capabilities. The code is available at https://github.com/kimanki/CAROTS.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
