# Overview:
The "TempXAI: Explainable AI for Time Series and Data Streams Workshop" is hosted as part of this year's ECML PKDD 2024 conference in Vilnius. The TempXAI workshop focuses on exploring the crucial intersection of Explainable AI (XAI) and the challenges posed by time series and data streams. Our primary objectives include understanding dynamic interpretability, delving into techniques that offer transparent insights into time-evolving data, and providing a better understanding of machine learning models in dynamic environments. We aim to advance incremental explainability by investigating methods that ensure interpretability remains effective as models adapt to changing data over time or methods that are able to explain these changes. Moreover, we seek to promote real-time decision-making by exploring applications of XAI in real-time decision-making scenarios, addressing the need for interpretable models in time-sensitive contexts. The workshop also aims to share practical insights by encouraging the sharing of novel XAI tools that are specific to time series and data streams, in addition to case studies and practical implementations in employing interpretable machine learning for time series and data streams.
More Info at: https://sites.google.com/view/tempxai-workshop/home

# Topics:
The TempXAI workshop welcomes papers that cover, but are not limited to, one or several of the following topics:
- Explainable AI methods for time series modeling
- Explainable AI methods for data streams and models in flux
- Interpretable machine learning algorithms for time series and data streams
- Explainable deep learning for time series and data stream modeling
- Explainable concept drift detection in time series and data streams
- Explainable anomaly detection in time series or data streams
- Explainable pattern discovery and recognition in time series
- Explainability methods for multivariate time series
- Explainable time series features engineering
- Explainable aggregation of time series
- Integration of domain knowledge in time series modeling
- Explainability for continual learning and domain adaptation
- Visual explanations for (long) temporal data
- Causality; Stochastic process modeling
- Explainability metrics and evaluation, including benchmark time series and streaming datasets
- Case studies and applications of explainable artificial intelligence for time series or data streams
- Regulatory compliance and ethics

# Submission:
Please submit your work through CMT, where further information will be provided at the workshops website. At least one author of each accepted paper must be registered to the conference and attend the workshop. The submission can be part of the following two tracks.

## Research Track
We welcome submissions of novel scientific research and position papers presenting novel ideas, perspectives, or challenges in explainable AI for Time Series and Data Streams as regular papers (max. 8-16 pages) or extended abstracts (up to 2-4 pages). Each paper will be double-blind peer-reviewed and, upon selection, be presented and discussed at the workshop. We intend to publish proceedings within the open-access, indexed CEUR Workshop Proceedings series. Please format your papers according to the one column CEUR-WS template.

## Spotlight Track
We also welcome submissions of published research as extended abstracts (max. 2 pages CEUR-WS template). The goal is to advertise emerging and impactful contributions in the scientific community centered around TempXAI. The spotlight papers will be excluded from the workshop proceedings.

# Link:
https://sites.google.com/view/tempxai-workshop/home