计算机科学
自回归模型
变压器
鉴别器
异常检测
算法
模式识别(心理学)
卷积神经网络
人工智能
电压
数学
工程类
电信
探测器
电气工程
计量经济学
作者
Yifan Li,Xiaoyan Peng,Jia Zhang,Zhiyong Li,Ming Wen
出处
期刊:IEEE Transactions on Knowledge and Data Engineering
[Institute of Electrical and Electronics Engineers]
日期:2023-04-01
卷期号:35 (4): 3632-3644
被引量:34
标识
DOI:10.1109/tkde.2021.3130234
摘要
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.
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