Time series data anomaly detection based on LSTM-GAN
DOI:
https://doi.org/10.54097/fcis.v1i2.1701Keywords:
Time series data, Anomaly detection, GANAbstract
With the improvement of modern technology, a large number of time series data have been produced. The anomaly detection of time series data can provide relevant information for key situations faced by various fields. This paper proposed an unsupervised temporal anomaly detection method based on generation countermeasure network. In this model, Wasserstein distance is used instead of the original measurement method, and LSTM is used as the basic network of GAN. The model uses the reconstruction loss of the generator and the loss of the discriminator to define the anomaly function to judge the anomaly. This paper uses real world time series data sets involving various fields to evaluate the model. Experiments show that the model is effective in anomaly detection of time series data.
Downloads
References
C. -K. Lee, Y. -J. Cheon and W. -Y. Hwang, "Studies on the GAN-Based Anomaly Detection Methods for the Time Series Data," in IEEE Access, vol. 9, pp. 73201-73215, 2021, doi: 10.1109/ACCESS.2021.3078553.
Su Y , Zhao Y , Niu C , et al. Robust Anomaly Detection for Multivariate Time Series through Stochastic Recurrent Neural Network[C]// the 25th ACM SIGKDD International Conference. ACM, 2019.
Tang J , Chen Z , Fu A , et al. Enhancing Effectiveness of Outlier Detections for Low Density Patterns[J]. Springer-Verlag, 2002.
MA Biao, JIA Jun-cheng, DONG Guo-zhu et al. WGAN: Industrial Control Sensor Data Anomaly Detection Method Based on Wavelet Transform and Attention Mechanism. [J/OL]. Journal of Chinese Computer Systems,2021:1-12.
Malhotra P , Vig L , Shroff G , et al. Long Short Term Memory Networks for Anomaly Detection in Time Series[C]// 23rd European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, ESANN 2015. 2015.
R. Hsieh, J. Chou and C. Ho, "Unsupervised Online Anomaly Detection on Multivariate Sensing Time Series Data for Smart Manufacturing," 2019 IEEE 12th Conference on Service-Oriented Computing and Applications (SOCA), 2019, pp. 90-97, doi: 10.1109/SOCA.2019.00021.
Goodfellow I J, Pouget-Abadie J, Mirza, et al. Generative Adversarial Networks[J]. Advances in Neural Information Processing Systems, 2014, 3:2672-2680.
Arjovsky M, Chintala S, Bottou L. Wasserstein GAN[J]. arXiv preprint arXiv:1701.07875, 2017.M.
