Publication about anomaly detection at ICDM 2019
Dr. Krause presented our scientific results on Anomaly Detection in Univariate Time Series: An Empirical Comparison of Machine Learning Algorithms at the 19th Industrial Conference on Data Mining (www.data-mining-forum.de/) in New York. Co-autors are Mrs. Däubener (divis), Dr. Sebastian Schmitt (Honda Research Institute Europe GmbH) and Dr. Hao Wang (Universität [...]
New project with Honda Research Institute
A joint project with Honda Research Institute Europe, Offenbach, Germany, focuses on research and development of new methods for anomaly detection in uni- and multi-variate time series. The aim of the project is use automatic machine learning approaches for online learning of anomalies in time series. Such methods will be [...]
Anomaly Detection
Within the context of Industry 4.0 the term “anomaly” occurs very often and refers to many different incidents, depending on the branch and the problem. It might refer to sensor measurements, hacker attacks, credit card fraud or defect and abraded machines. The general definition says that anomalies are rare and [...]