KDD 2020, 26th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, August 23-27, 2020, San Diego, USA (Virtual Conference)
The automatic supervision of IT systems is a current challenge at Orange. Given the size and complexity reached by its IT operations, the number of sensors needed to obtain measurements over time, used to infer normal and abnormal behaviors, has increased
dramatically making traditional expert-based supervision methods slow or prone to errors. In this paper, we propose a fast and stable method called UnSupervised Anomaly Detection for multivariate time series (USAD) based on adversely trained autoencoders. Its autoencoder architecture makes it capable of learning in an unsupervised way. The use of adversarial training and its architecture allows it to isolate anomalies while providing fast training. We study the properties of our methods through experiments on five
public datasets, thus demonstrating its robustness, training speed and high anomaly detection performance. Through a feasibility study using Orange’s proprietary data we have been able to validate Orange’s requirements on scalability, stability, robustness,
training speed and high performance.
© ACM, 2020. This is the author's version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution. The definitive version was published in KDD 2020, 26th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, August 23-27, 2020, San Diego, USA (Virtual Conference) https://doi.org/10.1145/3394486.3403392