Multivariate Time Series Anomaly Detection using DiffGAN Model
Fuente:
arXiv
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| Auteurs principaux: | , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866917883201716224 |
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| author | Wu, Guangqiang Zhang, Fu |
| author_facet | Wu, Guangqiang Zhang, Fu |
| contents | In recent years, some researchers have applied diffusion models to multivariate time series anomaly detection. The partial diffusion strategy, which depends on the diffusion steps, is commonly used for anomaly detection in these models. However, different diffusion steps have an impact on the reconstruction of the original data, thereby impacting the effectiveness of anomaly detection. To address this issue, we propose a novel method named DiffGAN, which adds a generative adversarial network component to the denoiser of diffusion model. This addition allows for the simultaneous generation of noisy data and prediction of diffusion steps. Compared to multiple state-of-the-art reconstruction models, experimental results demonstrate that DiffGAN achieves superior performance in anomaly detection. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2501_01591 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Multivariate Time Series Anomaly Detection using DiffGAN Model Wu, Guangqiang Zhang, Fu Machine Learning Statistics Theory G.3 In recent years, some researchers have applied diffusion models to multivariate time series anomaly detection. The partial diffusion strategy, which depends on the diffusion steps, is commonly used for anomaly detection in these models. However, different diffusion steps have an impact on the reconstruction of the original data, thereby impacting the effectiveness of anomaly detection. To address this issue, we propose a novel method named DiffGAN, which adds a generative adversarial network component to the denoiser of diffusion model. This addition allows for the simultaneous generation of noisy data and prediction of diffusion steps. Compared to multiple state-of-the-art reconstruction models, experimental results demonstrate that DiffGAN achieves superior performance in anomaly detection. |
| title | Multivariate Time Series Anomaly Detection using DiffGAN Model |
| topic | Machine Learning Statistics Theory G.3 |
| url | https://arxiv.org/abs/2501.01591 |