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Workshop Anomaly and Change Point Detection

| Financial Sector | Health & Medicine | Industry 4.0 | Environment & Energy | Knowledge & Business Intelligence | Data Management & Analysis | Machine Learning & Deep Learning | Smart Data & Knowledge Services

Are you interested in the topics of "Anomaly and Change Point Detection" or are you already working in the field and looking for an opportunity to exchange and discuss?

Then this workshop is just right for you!

Teaser Bild zum Thema „Machine Learning“. Quelle: Adobe Stock© everythingpossible –


The research department Smart Data & Knowledge Services of DFKI is organizing a workshop on "Anomaly and Change Point Detection" together with the Department of Financial Mathematics of the Fraunhofer Institute for Industrial Mathematics (ITWM). Here you have the opportunity to present questions and first research results from this field in talks or posters.


September 8, 2022  -  September 9, 2022


More information about the program will follow as soon as the application and registration is completed.

Drinks and snacks will be offered during the coffee breaks. There will also be a dinner afterwards. The costs for the dinner are to be borne by the participants themselves.


The workshop will take place in Kaiserslautern. It is planned to organize a part of the event at Fraunhofer ITWM and a part at the premises of DFKI. More detailed information will follow as soon as the detailed program is available.



Registration and Call For Presentations

Use our form to register for the workshop "Anomaly and Change Point Detection". 

We are looking forward to numerous participation and diverse submissions of presentations and scientific posters.

Deadline for submitting program topics (talks, poster) is 01.08.2022!

Planned Topics in the Workshop Program

The topics of "Anomaly and Change Point Detection" are becoming more and more important. In the current development, more and more data is available, not least with regard to Germany's digitization initiative. This enables the use of Machine Learning (ML) methods and their further development in more and more areas.

At the same time, errors and changes are integrated in the data, which we would like to detect and use. Here, many research questions are still open. This workshop aims to provide a platform for exchange and discussion.

The research areas of "Anomaly and Change Point Detection" cover many different fields and are used in applications to answer a wide variety of questions. As an example, the following topics are mentioned here (other topics are conceivable):

  • Anomaly Detection
  • Change Point Detection
  • Outlier Detection
  • Unsupervised Machine Learning (ML)
  • Data Analysis
  • Mixed Type Datasets (e.g., tabular data with categorical and numerical columns)
  • Dataset Comparison
  • Dataset Similarity
  • Transferability of Results to Internal Datasets
  • Dataset Generation
  • Data Synthesis
  • Explainability

Examples of Fields of Application in “Anomaly and Change Point Detection“

The topics are also becoming more and more important in various areas in practice. This is because more and more data is available due to current developments, not least with regard to Germany's digitalisation initiative. This enables the use of machine learning (ML) methods and their further development. The detection of anomalies or outliers always means discovering patterns in data that are not expected and cannot be classified in a defined normal state.

Well-known examples from the application and practice include:

  • In production: In the manufacturing industry, anomaly detection is important for improving quality control, for example. A distinction is often made between one-off errors (anomaly) or general misadjustments of the machine (change point/structure break). Then, for example, the sensors can be adjusted.
  • With climate change in general, outliers in the data occure frequently and the real change detectable in the general trend. However, an example from the past also shows exceptions. In the case of the ozone measurement in the 1980s: The ozone hole had already been discovered before, but was deleted from the data as an anomaly. The data was therefore misinterpreted.
  • Parking data during the Corona pandemic: When predicting parking availability, major events are often the outliers, as here, for example, on a Sunday, there are suddenly hardly any spaces available, whereas at Corona the general parking situation was minimised, as fewer people were on the road at all.
  • In the large area of financial data, anomaly detection is versatile. It can be used to identify fraud or manipulation in the network, but also to detect minor changes in the usage behaviour of customers and thus better predict their behaviour.


Prof. Dr. Jörn Hees

Team Leader Multimedia Analysis and Data Mining (MADM) , DFKI

Dr. Stefanie Schwaar

Financial Mathematics , Fraunhofer Institute for Industrial Mathematics ITWM

Sabrina Klar

Departmental Office/Teamassistent , Fraunhofer Institute for Industrial Mathematics ITWM