Partially-Supervised Graph Derivation Network With Meta-Learning for Time-Series Anomaly Detection

Abstract

Time-series anomaly detection is essential in various fields, such as industrial monitoring, cybersecurity, and finance. Traditional supervised methods often face challenges due to the limited availability of labeled anomaly instances for training. Moreover, these methods struggle to deal with intricate systems that incorporate information about topological structures. In this article, we propose a novel approach called the partially-supervised graph derivation network with meta learning (PS-GDNML) for time-series anomaly detection. PS-GDNML combines the power of graph-based representations, partially-supervised learning, and meta-learning to enhance the effectiveness and robustness of anomaly detection. The method represents time-series data as a graph, where each data point is a node, and temporal relationships are captured through edges. By leveraging a graph attention neural network (GAT), the model effectively captures complex dependencies and relationships within the data. To address the scarcity of labeled anomaly instances, PS-GDNML adopts a partially-supervised learning framework. It utilizes both labeled and unlabeled data, enabling the model to learn from the available information and generalize to detect anomalies in unseen data. Additionally, to explore the underlying commonalities of data across different time periods and enhance the model’s adaptability, we adopted a new meta-learning method called task relation meta-learner (TRMLearner). The purpose of this project is to utilize task relationships to guide the meta-learning optimization process. We evaluated the performance of PS-GDNML on benchmark datasets and compared it with state-of-the-art anomaly detection methods. The experimental results demonstrate that, even with a restricted set of labeled instances, our method excels at accurately detecting anomalies. Furthermore, the meta-learning component enhances the model’s capacity to generalize to novel and evolving anomaly patterns.