Khelifati, Abdelouahab Khayati, Mourad Cudré-Mauroux, Philippe Hänni, Adrian Liu, Qian Hauswirth, Manfred 2021 Anomaly detection is a fundamental problem that consists of identifying irregular patterns that do not conform to the expected behavior of a system or the generated data. Many anomaly detection techniques have been proposed for time series data. However, selecting the most suitable detection method remains challenging as the proposed techniques widely vary in performance. The appropriate choice of a detection method impacts many properties of mission-critical applications such as in monitoring a patient’s health, where anomalies are inevitable but need to be detected securely. In this demo, we present a new evaluator that allows to peruse the performance of several anomaly detection techniques and supports practitioners in understanding the behavior and (dis-)advantages of each technique for a given dataset. In a simple and well-structured way, practitioners can specify the desired anomaly detection setup, and our system would tune the parameters of each technique and analyze their properties in an easily understandable report. The tool also allows recommending the most appropriate technique for each anomaly type and evaluation metric. application/pdf https://folia.unifr.ch/global/documents/309431 https://folia.unifr.ch/documents/309431/files/2021_Khelifati_VADETIS.pdf eng info:eu-repo/semantics/altIdentifier/doi/10.1109/ICDE51399.2021.00298 info:eu-repo/semantics/openAccess License undefined IEEE 37th International Conference on Data Engineering (ICDE). - IEEE. - 2021, p. 2661--2664 Time Series Databases info:eu-repo/classification/udc/004 VADETIS : an Explainable Evaluator for Anomaly Detection Techniques http://purl.org/coar/resource_type/c_5794