MPFormer: Multipatch Transformer for Multivariate Time-Series Anomaly Detection With Contrastive Learning

Recent unsupervised framework-based anomaly detection methods in Internet of Things (IoT) have been proved successful by learning temporal pattern representations from normal time-series data. However, such methods have long suffered from weakened normal-abnormal boundary incurred by discrimination-insufficient temporal pattern representations, due to: 1) normal time series often suffer from some unprocessed abnormal noise, interfering with model’s discriminative representations between normal and abnormal points and 2) previous methods rarely focus on contextual semantic features of time series that are also crucial for constructing discriminative representations. In this article, we propose multipatch contrastive learning framework for multivariate time-series anomaly detection (MPFormer), a transformer-based multipatch contrastive learning framework. This novel framework leverages contrastive learning to guide discriminative feature learning. It incorporates a data augmentation strategy to generate positive samples and maximizes the similarity between positive sample pairs, thereby capturing inherent representations of normal temporal patterns and enhancing robust discrimination ability to abnormal noise. To further facilitate discriminative representation modeling, we divide input time series into multiple patches and design a transformer-based dual-attention module to explore contextual semantic features in both interpatch and intrapatch views. Benefiting from discriminative representations being modeled, MPFormer effectively strengthens the normal-abnormal boundary, thus improving detection accuracy. Experimental results on six widely adopted data sets demonstrate that our proposed MPFormer outperforms existing baseline methods.