Angel or Devil: Discriminating Hard Samples and Anomaly Contaminations for Unsupervised Time Series Anomaly Detection

TL;DR

This paper introduces PLDA, a dual data augmentation method combining loss and parameter behaviors, to improve unsupervised time series anomaly detection by better distinguishing hard normal samples from contaminations.

Contribution

The paper proposes a novel dual augmentation approach, PLDA, that leverages parameter behavior alongside loss behavior to enhance anomaly detection performance.

Findings

PLDA improves detection accuracy by up to 8% across multiple datasets.

PLDA outperforms three existing data augmentation methods.

PLDA can be integrated with various anomaly detectors for better results.

Abstract

Training in unsupervised time series anomaly detection is constantly plagued by the discrimination between harmful `anomaly contaminations' and beneficial `hard normal samples'. These two samples exhibit analogous loss behavior that conventional loss-based methodologies struggle to differentiate. To tackle this problem, we propose a novel approach that supplements traditional loss behavior with `parameter behavior', enabling a more granular characterization of anomalous patterns. Parameter behavior is formalized by measuring the parametric response to minute perturbations in input samples. Leveraging the complementary nature of parameter and loss behaviors, we further propose a dual Parameter-Loss Data Augmentation method (termed PLDA), implemented within the reinforcement learning paradigm. During the training phase of anomaly detection, PLDA dynamically augments the training data through an iterative process that simultaneously mitigates anomaly contaminations while amplifying informative hard normal samples. PLDA demonstrates remarkable versatility, which can serve as an additional component that seamlessly integrated with existing anomaly detectors to enhance their detection performance. Extensive experiments on ten datasets show that PLDA significantly improves the performance of four distinct detectors by up to 8\%, outperforming three state-of-the-art data augmentation methods.