Abstract
Time series anomaly detection is a critical task with applications in various domains. Due to annotation challenges, self-supervised methods have become the mainstream approach
for time series anomaly detection in recent years. However,
current contrastive methods categorize data perturbations into
binary classes, normal or anomaly, which lack clarity on the specific impact of different perturbation methods. Inspired by the hypothesis that "the higher the probability of misclassifying perturbation types, the higher the probability of anomalies", we propose PCRTA, our
approach firstly devises a perturbation classifier to learn the
pseudo-labels of data perturbations. Furthermore, for addressing "class collapse issue" in contrastive learning, we propose a perturbation guiding positive and negative samples
selection strategy by introducing learnable perturbation classification networks. Extensive experiments on six realworld datasets demonstrate the significant superiority of our
model over thirteen state-of-the-art competitors, and obtains average
5.14%, 8.24% improvement in F1 score and AUC-PR, respectively.
Keywords
- Machine Learning: ML: Representation learning
- Machine Learning: ML: Self-supervised Learning
- Machine Learning: ML: Time series and data streams
- Machine Learning: ML: Unsupervised learning
