异常检测-1-综述-Deep Learning for Anomaly Detection: A Survey

https://www.researchgate.net/publication/330357393_Deep_Learning_for_Anomaly_Detection_A_Survey?enrichId=rgreq-40000b66a80039399492f90066ec07a0-XXX&enrichSource=Y292ZXJQYWdlOzMzMDM1NzM5MztBUzo3MTU2NTQ5MTA5Njc4MDhAMTU0NzYzNjgzNTAyMw%3D%3D&el=1_x_3&_esc=publicationCoverPdf

时序数据的深度异常检测模型，主要有CNN, RNN, LSTM

时序数据的异常检测：
Table 15: Examples of DAD techniques used in industrial operations.

• CNN: Convolution Neural Networks
• LSTM : Long Short Term Memory Networks
• GRU: Gated Recurrent Unit
• DNN : Deep Neural Networks
• AE: Autoencoders
• DAE: Denoising Autoencoders
• SVM: Support Vector Machines
• SDAE: Stacked Denoising Autoencoders
• RNN : Recurrent Neural Networks.
• DNN-SVM：Hybrid Models

Table 16: Examples of DAD techniques used in time series data.

• CNN: Convolution Neural Networks,
• GAN: Generative Adversarial networks,
• LSTM : Long Short Term Memory Networks
• GRU: Gated Recurrent Unit,
• DNN : Deep Neural Networks,
• AE: Autoencoders,
• DAE: Denoising Autoencoders,
• VAE: Variational Autoencoder
• SDAE: Stacked Denoising Autoencoders

https://blog.csdn.net/aiyouyou_/article/details/120177814