Anomaly Detection and Explainability for Time-Series Data

As AI systems are increasingly used to monitor complex, time-dependent processes, understanding anomalies and their underlying causes becomes critical. This hands-on course introduces participants to anomaly detection and explainable AI (xAI) techniques for both transactional and time-series data, combining practical modeling with interpretable insights.

This hands-on training introduces explainable AI (xAI) for anomaly detection in both tabular and time series data. Participants work with real-world datasets, including credit card fraud and NYC taxi demand, using standard machine learning models and SHAP-based explanations. The session covers the full workflow from data exploration to model interpretation. Special attention is given to challenges such as class imbalance and temporal dependencies. The course concludes with a critical discussion of limitations and best practices for applying xAI in practice for timeseries anomaly detection.

Learning Outcomes: After attending this course, you should be able to

• Understand typical use cases of anomaly detection and explainability for transactional and time-series data
• Perform exploratory data analysis and apply suitable anomaly detection techniques
• Build and evaluate models for anomaly detection in tabular and time-series settings
• Apply and interpret explainability methods (e.g., feature importance, SHAP)
• Recognize challenges such as class imbalance and temporal dependencies and account for them in practice
• Critically assess the limitations and risks of explainability methods in real-world applications

For a detailed timetable and additional information, please see Agenda & Content in the left menu.

Target audience, eligibility & prices

Everyone is welcome who wants to get a deeper understanding of how and why AI systems make decisions.

This course is open and free of charge for all participants from academia, industry, and public administration from EU and/or EuroHPC JU member countries.

Entry level & prerequisites

Basic – no prior XAI knowledge is required.

Participants are expected to be familiar with basic machine learning concepts and python programming, prior hands-on xAI or anomaly detection experience is not required.

Course format

This course will be delivered as a LIVE ONLINE COURSE (using Zoom).

Hands-on labs

All participants will get a temporary user account on one of the ASC systems to do the hands-on labs.

You will use your own laptop or workstation to connect conveniently from your browser to the ASC Jupyterhub and do the hands-on exercises on a suitable CPU or GPU partition of the ASC.

Accepted participants will be contacted a few days before the course and asked to do a short pre-assignment that has to be completed before the course starts.

Lecturer

Anahid Wachsenegger (AIT Austrian Institute of Technology GmbH)                      
Anahid Wachsenegger is a data scientist at the AIT Austrian Institute of Technology, specializing in artificial intelligence, explainable machine learning, data-driven modeling, and time-series analysis across domains such as forestry and mobility data science. She holds a Master’s degree in Computational Intelligence from TU Wien and also served as an associate lecturer in Media and Digital Technologies at the University of Applied Sciences St. Pölten. She is passionate about developing trustworthy, transparent AI systems, applying data science to real-world challenges, and supporting students and practitioners in understanding and responsibly using modern AI technologies.

Language

English

Date, time, and location

24.04.2026, 13:00 – 16:30 (CEST), LIVE ONLINE COURSE (Zoom)

Registration

Registration is required, the registration form can be found on top of this page in the menu on the left.

Please register with your official institutional email address to prove your affiliation.

You will get an automatic confirmation by email (subject starting with "[Indico] Registration"), please check your Spam/Junk folders.

Following your successful registration, you will receive further information a few days before the course.

Please do not hesitate to contact us at training@ai-at.eu if you have any questions.

Waitinglist

After the number of registrations has reached its maximum or the registration form has been closed, you may want to send us an email (training@ai-at.eu) stating that you are interested to be put on the waiting list (vacancies may occur due to cancelations, etc.).

To be able to do the hands-on labs on the ASC systems please provide your full international mobile-phone number for the two-factor authentication required to login to the ASC systems.

Modification, withdrawal & no-show policy

Your registration is binding. Please only register for the course if you are really going to attend.

You can update your registration data or withdraw your registration anytime before the registration form has been closed via the link "Manage my registration" which you can find at the bottom of your automatic email confirmation (subject starting with "[Indico] Registration").

Alternatively, or after the registration form has been closed, please inform us about your cancelation or any change in your registration data (especially your mobile-phone number) via email (training@ai-at.eu).

No-show policy: If you do not cancel and do not show up at the course you will be blacklisted and excluded from future training events.

Organizers

This course is jointly organized by AI Factory Austria AI:AT and Austrian Scientific Computing ASC (aka ASC Research Center, TU Wien).

Acknowledgements

AI Factory Austria AI:AT has received funding from the European High-Performance Computing Joint Undertaking (JU) under grant agreement No 101253078. The JU receives support from the Horizon Europe Programm of the European Union and Austria (BMIMI / FFG).