Multivariate time-series anomaly detection

Qifa Wang; Qiwei Shen

doi:10.1117/12.2679609

16 June 2023 Multivariate time-series anomaly detection

Qifa Wang, Qiwei Shen

Author Affiliations +

Proceedings Volume 12702, International Conference on Intelligent Systems, Communications, and Computer Networks (ISCCN 2023); 127021T (2023) https://doi.org/10.1117/12.2679609
Event: International Conference on Intelligent Systems, Communications, and Computer Networks (ISCCN 2023), 2023, Changsha, China

Abstract

Anomalies are rare items that differ significantly from the majority of the data and raise suspicion. Time series anomaly detection is of great significance in industrial applications, data mining and other fields. In this paper, we propose an autoencoder-based anomaly detection model. In the encoder part, the dependency relationship between different time series is mined through the graph attention network, the time domain data features are extracted through the GRU, and the frequency domain features are extracted through the convolutional neural network. The temporal data is reconstructed using a decoder consisting of a recurrent neural network in the encoder part. The residual between the reconstructed data and the original data is used to further judge anomalies. Data cleaning is performed in the preprocessing part to improve model performance. The effectiveness of our method is demonstrated on three publicly available datasets, and our method is found to outperform four other common anomaly detection methods.

Citation Download Citation

Qifa Wang and Qiwei Shen "Multivariate time-series anomaly detection", Proc. SPIE 12702, International Conference on Intelligent Systems, Communications, and Computer Networks (ISCCN 2023), 127021T (16 June 2023); https://doi.org/10.1117/12.2679609

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