Self-Supervised Time-Series Anomaly Detection Using Learnable Data Augmentation

TL;DR

This paper introduces LATAD, a self-supervised, learnable data augmentation method for time-series anomaly detection that improves detection accuracy and provides root cause diagnosis, addressing data scarcity issues in industrial applications.

Contribution

LATAD is a novel self-supervised approach combining contrastive learning with learnable data augmentation for enhanced time-series anomaly detection.

Findings

LATAD achieves comparable or better performance than state-of-the-art methods.

LATAD provides a gradient-based technique for root cause diagnosis.

LATAD effectively handles data scarcity in industrial anomaly detection.

Abstract

Continuous efforts are being made to advance anomaly detection in various manufacturing processes to increase the productivity and safety of industrial sites. Deep learning replaced rule-based methods and recently emerged as a promising method for anomaly detection in diverse industries. However, in the real world, the scarcity of abnormal data and difficulties in obtaining labeled data create limitations in the training of detection models. In this study, we addressed these shortcomings by proposing a learnable data augmentation-based time-series anomaly detection (LATAD) technique that is trained in a self-supervised manner. LATAD extracts discriminative features from time-series data through contrastive learning. At the same time, learnable data augmentation produces challenging negative samples to enhance learning efficiency. We measured anomaly scores of the proposed technique based on latent feature similarities. As per the results, LATAD exhibited comparable or improved performance to the state-of-the-art anomaly detection assessments on several benchmark datasets and provided a gradient-based diagnosis technique to help identify root causes.