Prof. Dr. Felix Naumann

DADS (Distributed Detection of Sequential Anomalies in Univariate Time Series)

We built a scalabe time-series anomaly detector.

Source Code

The source code can be found here.


We used the following datasets:

SEDAbdul-Aziz, Ali & Woike, Mark & Oza, Nikunj & Matthews, Bryan & Lekki, John. (2011). Rotor health monitoring combining spin tests and data-driven anomaly detection methods. Structural Health Monitoring. 11. 3-12. 10.1177/1475921710395811.

Goldberger, Ary & Amaral, Luís & Glass, L. & Havlin, Shlomo & Hausdorg, J. & Ivanov, Plamen & Mark, R. & Mietus, J. & Moody, G. & Peng, Chung-Kang & Stanley, H. & Physiobank, Physiotoolkit. (2000). Components of a new research resource for complex physiologic signals. PhysioNet. 101.

Moody, G.B. & Mark, R.G.. (2001). The impact of the MIT-BIH arrhythmia database. IEEE engineering in medicine and biology magazine : the quarterly magazine of the Engineering in Medicine & Biology Society. 20. 45-50. 10.1109/51.932724.

MVKeogh, E. & Lin, J. & Fu, Ada. (2005). HOT SAX: Efficiently finding the most unusual time series subsequence. Proceedings - IEEE International Conference on Data Mining, ICDM. 8 pp.-. 10.1109/ICDM.2005.79.

Senin, Pavel & Lin, Jessica & Wang, Xing & Oates, Tim & Gandhi, Sunil & Boedihardjo, Arnold & Chen, Crystal & Frankenstein, Susan. (2015). Time series anomaly discovery with grammar-based compression.

Wijk, Jarke & Selow, E.. (1999). Cluster and calendar based visualization of time series data. 4-9, 140. 10.1109/INFVIS.1999.801851.

SynthBoniol, Paul & Palpanas, Themis. (2020). Series2Graph: Graph-based Subsequence Anomaly Detection for Time Series. Proceedings of the International Conference on Very Large Databases.