TadGAN: Time Series Anomaly Detection Using Generative Adversarial Networks

@article{Geiger2020TadGANTS,
  title={TadGAN: Time Series Anomaly Detection Using Generative Adversarial Networks},
  author={Alexander Geiger and D. Liu and Sarah Alnegheimish and Alfredo Cuesta-Infante and K. Veeramachaneni},
  journal={2020 IEEE International Conference on Big Data (Big Data)},
  year={2020},
  pages={33-43}
}
Time series anomalies can offer information relevant to critical situations facing various fields, from finance and aerospace to the IT, security, and medical domains. However, detecting anomalies in time series data is particularly challenging due to the vague definition o f a nomalies and said data’s frequent lack of labels and highly complex temporal correlations. Current state-of-the-art unsupervised machine learning methods for anomaly detection suffer from scalability and portability… Expand
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