Abstract:
Time series anomaly detection (TSAD) is an essential problem faced in several fields, e.g., fault detection, fraud detection, and intrusion detection, etc. Although TSAD ...Show MoreMetadata
Abstract:
Time series anomaly detection (TSAD) is an essential problem faced in several fields, e.g., fault detection, fraud detection, and intrusion detection, etc. Although TSAD is a crucial problem in anomaly detection, few solutions in anomaly detection are suitable for it at present. Recently, some researchers use GAN-based methods such as TAnoGAN and TadGAN to solve TSAD problem. However, problems such as model collapse, low generalization capability and poor accuracy still exist. In this article, we proposed a Dilated Convolutional Transformer-based GAN (DCT-GAN) to enhance accuracy and improve generalization capability of the model. Specifically, DCT-GAN utilize several generators and a single discriminator to alleviate the mode collapse problem. Each generator consists of a dilated convolutional neural network and a Transformer block to obtain fine-grained and coarse-grained information of the time series, which is a useful component to improve generalization capability. We also use weight-based mechanism to balance these generators. Experiments verify the effectiveness of our method and each part of DCT-GAN.
Published in: IEEE Transactions on Knowledge and Data Engineering ( Volume: 35, Issue: 4, 01 April 2023)
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- IEEE Keywords
- Index Terms
- Time Series ,
- Generative Adversarial Networks ,
- Anomaly Detection ,
- Dilated Convolution ,
- Convolutional Generative Adversarial Network ,
- Time Series Anomaly ,
- Time Series Anomaly Detection ,
- Neural Network ,
- Generalization Capability ,
- Crucial Problem ,
- Intrusion Detection ,
- Fine-grained Information ,
- Fraud Detection ,
- GAN-based Methods ,
- Model Performance ,
- Multiple Genes ,
- Deep Neural Network ,
- Time Series Data ,
- Normal Samples ,
- Recurrent Neural Network ,
- Anomaly Score ,
- Detection Window ,
- CPU Utilization ,
- Latent Space ,
- Fake Data ,
- Convolution Kernel ,
- False Negative Samples ,
- Gradient Penalty ,
- Receptive Field ,
- Ablation Experiments
- Author Keywords
Keywords assist with retrieval of results and provide a means to discovering other relevant content. Learn more.
- IEEE Keywords
- Index Terms
- Time Series ,
- Generative Adversarial Networks ,
- Anomaly Detection ,
- Dilated Convolution ,
- Convolutional Generative Adversarial Network ,
- Time Series Anomaly ,
- Time Series Anomaly Detection ,
- Neural Network ,
- Generalization Capability ,
- Crucial Problem ,
- Intrusion Detection ,
- Fine-grained Information ,
- Fraud Detection ,
- GAN-based Methods ,
- Model Performance ,
- Multiple Genes ,
- Deep Neural Network ,
- Time Series Data ,
- Normal Samples ,
- Recurrent Neural Network ,
- Anomaly Score ,
- Detection Window ,
- CPU Utilization ,
- Latent Space ,
- Fake Data ,
- Convolution Kernel ,
- False Negative Samples ,
- Gradient Penalty ,
- Receptive Field ,
- Ablation Experiments
- Author Keywords