{"id":10259,"date":"2024-11-19T09:15:34","date_gmt":"2024-11-19T00:15:34","guid":{"rendered":"https:\/\/www.ibs.re.kr\/bimag\/?post_type=tribe_events&p=10259"},"modified":"2024-11-19T09:15:34","modified_gmt":"2024-11-19T00:15:34","slug":"svd-ae-an-asymmetric-autoencoder-with-svd-regularization-for-multivariate-time-series-anomaly-detection-myna-lim","status":"publish","type":"tribe_events","link":"https:\/\/www.ibs.re.kr\/bimag\/event\/svd-ae-an-asymmetric-autoencoder-with-svd-regularization-for-multivariate-time-series-anomaly-detection-myna-lim\/","title":{"rendered":"SVD-AE: An asymmetric autoencoder with SVD regularization for multivariate time series anomaly detection – Myna Lim"},"content":{"rendered":"

In this talk, we discuss the paper “SVD-AE: An asymmetric autoencoder with SVD regularization for multivariate time series anomaly detection” by Yueyue Yao, et.al., Neural Networks, 2024.\u00a0<\/span><\/p>\n

Abstract\u00a0<\/strong><\/p>\n

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Anomaly detection\u00a0in multivariate time series is of critical importance in many real-world applications, such as system maintenance and Internet monitoring. In this article, we propose a novel unsupervised framework called SVD-AE to conduct anomaly detection in multivariate time series. The core idea is to fuse the strengths of both\u00a0SVD\u00a0and\u00a0autoencoder\u00a0to fully capture complex normal patterns in multivariate time series. An asymmetric autoencoder architecture is proposed, where two encoders are used to capture features in time and variable dimensions and a shared decoder is used to generate reconstructions based on latent representations from both dimensions. A new\u00a0regularization\u00a0based on singular value decomposition theory is designed to force each encoder to learn features in the corresponding axis with mathematical supports delivered. A specific loss component is further proposed to align\u00a0Fourier coefficients\u00a0of inputs and reconstructions. It can preserve details of original inputs, leading to enhanced feature learning capability of the model. Extensive experiments on three real world datasets demonstrate the proposed algorithm can achieve better performance on multivariate time series anomaly detection tasks under highly unbalanced scenarios compared with baseline algorithms.<\/div>\n<\/div>\n<\/div>\n<\/div>\n
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