Abstract
This paper studies an effective unsupervised deep learning model for multivariate time series anomaly detection. Since multivariate time series usually have problems of insufficient labeling and highly-complex temporal correlation, effectively detecting anomalies in multivariate time series data is particularly challenging. To solve this problem, we propose a model named Wasserstein-GAN with gradient Penalty and effective Scoring (WPS). In this model, Wasserstein Distance with Gradient Penalty helps to capture the data regularities between generator output and real data, thus improving the training stability. Meanwhile, an effective scoring function that consists of reconstruction error, discrimination error, and prediction error is designed to evaluate the accuracy of the abnormal prediction and recall. The experimental results show that compared with the suboptimal baseline model, our proposed WPS obtains 17.68% and 10.41% improvement in prediction precision and F1 score, respectively.
This research was supported by the Science and Technology Program of Sichuan Province under Grant No. 2020JDRC0067, No. 2023JDRC0087, and No. 2020YFG0326, and the Talent Program of Xihua University under Grant No. Z202047 and No. Z222001.
S. Qi and J. Chen—Contributed to the work equally and should be regarded as co-first authors.
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Qi, S., Chen, J., Chen, P., Wen, P., Shan, W., Xiong, L. (2023). An Effective WGAN-Based Anomaly Detection Model for IoT Multivariate Time Series. In: Kashima, H., Ide, T., Peng, WC. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2023. Lecture Notes in Computer Science(), vol 13935. Springer, Cham. https://doi.org/10.1007/978-3-031-33374-3_7
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