Publikation
Combining Transformer with a Discriminator for Anomaly Detection in Multivariate Time Series
Chihiro Maru; Boris Brandherm; Ichiro Kobayashi
In: 2022 Joint 12th International Conference on Soft Computing and Intelligent Systems and 23rd International Symposium on Advanced Intelligent Systems (SCIS&ISIS). Joint International Conference on Soft Computing and Intelligent Systems and International Symposium on Advanced Intelligent Systems (SCIS&ISIS-2022), November 29 - December 2, Ise, Japan, Pages 1-7, ISBN 978-1-6654-9924-8, IEEE, 11/2022.
Zusammenfassung
Anomaly detection in multivariate time series has been attracting attention in order to realize continuous stable operation of systems. As systems diversify and monitoring targets become more complex, the number and types of measurements obtained from sensors in the system have dramatically increased. It is necessary to instantly process a large amount of complex multivariate time series in order to determine anomalies with high detection accuracy. In this paper, we proposed TDAD, Transformer with a Discriminator for Anomaly Detection in multivariate time series. Introducing an adversarial training and attention mechanisms has improved extractions of detailed loss and time series features during model training. We compare the performance of TDAD with five other deep learning methods on five publicly available datasets and demonstrate that it can determine anomalies with high accuracy.