Vol-3760⫷ Vol-3761 ⫸Vol-3762
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 |