| Vol-3761 urn:nbn:de:0074-3761-3 Copyright © 2024 for the individual papers by the papers' authors. Copyright © 2024 for the volume as a collection by its editors. This volume and its papers are published under the Creative Commons License Attribution 4.0 International (CC BY 4.0).

# TempXAI 2024
Explainable AI for Time Series and Data Streams 2024

### Proceedings of the Workshop on Explainable AI for Time Series and Data Streams (TempXAI 2024)
co-located with The European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML-PKDD 2024)

### Vilnius, Lithuania, September 9th, 2024.

Edited by

### Zahraa Abdallah *
Fabian Fumagalli **
Barbara Hammer **
Eyke Hüllermeier ***
Matthias Jakobs ****
Emmanuel Müller *****
Maximilian Muschalik ***
Panagiotis Papapetrou ******
Amal Saadallah ****
George Tzagkarakis *******

* University of Bristol, UK
** Bielefeld University, Germany
*** LMU Munich, Germany
**** Lamarr Institute for Machine Learning and Artificial Intelligence, Germany
***** Research Center Trustworthy Data Science and Security, Germany
****** Stockholm University, Sweden
******* FORTH-ICS, Greece

## Table of Contents

- Preface
  Summary: There were 5 submissions for peer-review, out of which we accepted 4 papers were accepted for this volume. In addition, we publish two extended abstracts of the tutorial that we give as a part of the workshop program.

### Extended Abstracts

- Explaining Change in Models and Data with Global Feature Importance and Effects 1-6
  Maximilian Muschalik, Fabian Fumagalli, Barbara Hammer, Eyke Hüllermeier
- Online Explainable Forecasting using Regions of Competence 7-11
  Amal Saadallah, Matthias Jakobs

### Research Papers

- Finding the DeepDream for Time Series: Activation Maximization for Univariate Time Series 12-27
  Udo Schlegel, Daniel A. Keim, Tobias Sutter
- Optimal Robust Simplifications for Explaining Time Series Classifications 28-43
  Jan Arne Telle, Cèsar Ferri, Brigt Håvardstun
- Interpreting Outliers in Time Series Data through Decoding Autoencoder 44-57
  Patrick Knab, Sascha Marton, Christian Bartelt, Robert Fuder
- Explainable Federated Learning by Incremental Decision Trees 58-69
  Parisa Jamshidi, Sławomir Nowaczyk, Mahmoud Rahat, Zahra Taghiyarrenani

2024-09-17: submitted by Amal Saadallah, metadata incl. bibliographic data published under Creative Commons CC0
2024-09-17: published on CEUR Workshop Proceedings (CEUR-WS.org, ISSN 1613-0073) |valid HTML5|