期刊
INFORMATION SCIENCES
卷 610, 期 -, 页码 266-280出版社
ELSEVIER SCIENCE INC
DOI: 10.1016/j.ins.2022.07.179
关键词
Anomaly detection; Multivariate time series; Autoencoder; Contrastive learning; Data augmentation
资金
- National Natural Science Foundation of China [61601046, 61171098]
- 111 Project of China [B08004]
- EEA
- BUPT Excellent Ph.D.. Students Foundation [CX2022149]
- Norway Grants 2014-2021 [EEA-RO-NO-2018-04]
This paper proposes CAE-AD, a novel contrastive autoencoder for anomaly detection in multivariate time series (MTS) data. By introducing multi-grained contrasting methods, this method can capture the dependencies and dynamics of MTS, extract the normal data pattern, and improve the performance of anomaly detection.
With the proliferation of the Internet of Things, a large amount of multivariate time series (MTS) data is being produced daily by industrial systems, corresponding in many cases to life-critical tasks. The recent anomaly detection researches focus on using deep learning methods to construct a normal profile for MTS. However, without proper constraints, these methods cannot capture the dependencies and dynamics of MTS and thus fail to model the normal pattern, resulting in unsatisfactory performance. This paper proposes CAE-AD, a novel contrastive autoencoder for anomaly detection in MTS, by introducing multi -grained contrasting methods to extract normal data pattern. First, to capture the temporal dependency of series, a projection layer is employed and a novel contextual contrasting method is applied to learn the robust temporal representation. Second, the projected series is transformed into two different views by using time-domain and frequency-domain data augmentation. Last, an instance contrasting method is proposed to learn local invariant characteristics. The experimental results show that CAE-AD achieves an F1-score ranging from 0.9119 to 0.9376 on the three public datasets, outperforming the baseline methods.(c) 2022 Published by Elsevier Inc.
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