Weakly Augmented Variational Autoencoder in Time Series Anomaly Detection
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arXiv
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| Hauptverfasser: | , , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866929201198661632 |
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| author | Wu, Zhangkai Cao, Longbing Zhang, Qi Zhou, Junxian Chen, Hui |
| author_facet | Wu, Zhangkai Cao, Longbing Zhang, Qi Zhou, Junxian Chen, Hui |
| contents | Due to their unsupervised training and uncertainty estimation, deep Variational Autoencoders (VAEs) have become powerful tools for reconstruction-based Time Series Anomaly Detection (TSAD). Existing VAE-based TSAD methods, either statistical or deep, tune meta-priors to estimate the likelihood probability for effectively capturing spatiotemporal dependencies in the data. However, these methods confront the challenge of inherent data scarcity, which is often the case in anomaly detection tasks. Such scarcity easily leads to latent holes, discontinuous regions in latent space, resulting in non-robust reconstructions on these discontinuous spaces. We propose a novel generative framework that combines VAEs with self-supervised learning (SSL) to address this issue. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2401_03341 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Weakly Augmented Variational Autoencoder in Time Series Anomaly Detection Wu, Zhangkai Cao, Longbing Zhang, Qi Zhou, Junxian Chen, Hui Machine Learning Due to their unsupervised training and uncertainty estimation, deep Variational Autoencoders (VAEs) have become powerful tools for reconstruction-based Time Series Anomaly Detection (TSAD). Existing VAE-based TSAD methods, either statistical or deep, tune meta-priors to estimate the likelihood probability for effectively capturing spatiotemporal dependencies in the data. However, these methods confront the challenge of inherent data scarcity, which is often the case in anomaly detection tasks. Such scarcity easily leads to latent holes, discontinuous regions in latent space, resulting in non-robust reconstructions on these discontinuous spaces. We propose a novel generative framework that combines VAEs with self-supervised learning (SSL) to address this issue. |
| title | Weakly Augmented Variational Autoencoder in Time Series Anomaly Detection |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2401.03341 |