An adversarial time-frequency reconstruction network for unsupervised anomaly detection

Jin Fan, Zehao Wang, Huifeng Wu, Danfeng Sun, Jia Wu, Xin Lu

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)


Detecting anomalies in massive volumes of multivariate time series data, particularly in the IoT domain, is critical for maintaining stable systems. Existing anomaly detection models based on reconstruction techniques face challenges in distinguishing normal and abnormal samples from unlabeled data, leading to performance degradation. Moreover, accurately reconstructing abnormal values and pinpointing anomalies remains a limitation. To address these issues, we introduce the Adversarial Time–Frequency Reconstruction Network for Unsupervised Anomaly Detection (ATF-UAD). ATF-UAD consists of a time reconstructor, a frequency reconstructor and a dual-view adversarial learning mechanism. The time reconstructor utilizes a parity sampling mechanism to weaken the dependency between neighboring points. Then attention mechanisms and graph convolutional networks (GCNs) are used to update the feature information for each point, which combines points with close feature relationships and dilutes the influence of abnormal points on normal points. The frequency reconstructor transforms the input sequence into the frequency domain using a Fourier transform and extracts the relationship between frequencies to reconstruct anomalous frequency bands. The dual-view adversarial learning mechanism aims to maximize the normal values in the reconstructed sequences and highlight anomalies and aid in their localization within the data. Through dual-view adversarial learning, ATF-UAD minimizes reconstructed value errors and maximizes the identification of residual outliers. We conducted extensive experiments on nine datasets from different domains, and ATF-UAD showed an average improvement of 6.94% in terms of F1 score compared to the state-of-the-art method.
Original languageEnglish
Pages (from-to)44-56
Number of pages12
JournalNeural Networks
Publication statusPublished - 16 Sept 2023


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