Real-world time series from environmental sensors and industrial systems are frequently corrupted by impulsive noise, where extreme events occur with far greater probability than what Gaussian models predict. In such settings, the Euclidean distance underlying standard matrix profile computation becomes unreliable, as squared differences amplify the effect of heavy-tailed outliers on similarity search.
- Speaker
- Date
- Monday 28 Sep 2026, 12:00 - 13:00
- Type
- Seminar
- Room
- ET-14
- Building
- E Building
In this talk, we introduce a generalisation of the matrix profile framework that extends the Euclidean distance with an Lp (pseudo-)norm, enabling robust motif discovery and discord detection under heavy-tailed noise.
Experimental evaluation on real data demonstrates that the Lp-based matrix profile consistently outperforms the Euclidean baseline in preserving the ranking of true anomalies, with the advantage increasing as noise becomes more impulsive.
The approach extends an existing open-source Python toolkit for irregular multivariate time series, offering a robust alternative for anomaly detection in non-Gaussian environments.
About the speaker
Georgia M. Tsanta is a postgraduate student in the Computer Science and Engineering programme at the University of Crete and a research fellow at the Signal Processing Laboratory of the Institute of Computer Science at FORTH.
She earned her BSc in Computer Science from the University of Crete in 2025. Her broader scientific interests encompass Machine Learning, Data Science, Time Series Analysis, and Artificial Intelligence applications.
Coordinators
See also
- More information
Lunch will be provided (vegetarian option included).
For more information please contact the Secretariat Econometrics at eb-secr@ese.eur.nl
