Research Topic: Anomaly Detection
We propose ROCKAD, a kernel-based approach for semi-supervised whole time series anomaly detection, i.e. the assignment of a single anomaly score to an entire time series.
Our key idea is to use the time series classifier ROCKET as an unsupervised feature extractor and to train anomaly detectors to deduce an anomaly score. To the best of our knowledge, this is the first approach to transfer the ideas of ROCKET to the task of anomaly detection.
(UNDER CONSTRUCTION — VISIT AGAIN IN A FEW DAYS — GITHUB AND DETAILED RESULTS WILL BE ADDED)
Theissler, Andreas, Manuel Wengert, and Felix Gerschner. „ROCKAD: Transferring ROCKET to Whole Time Series Anomaly Detection.“ Advances in Intelligent Data Analysis XXI: 21st International Symposium on Intelligent Data Analysis, IDA 2023, Louvain-la-Neuve, Belgium, April 12–14, 2023, Proceedings. Cham: Springer Nature Switzerland, 2023.
Link to paper