Call for paper for the "TempXAI: Explainable AI for Time Series and Data Streams Workshop"!

The TempXAI workshop focuses on exploring the crucial intersection of Explainable AI (XAI) and the challenges posed by time series and data streams. The 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. 

Important dates:

• Paper Submission deadline: June 22, 2024

• Accept/Reject Notification: July 22, 2024

• Camera-ready deadline: August 01, 2024

• Workshop: TBA

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

For more details on the workshop and the submission, visit this website. Any general enquiries, please drop a mail @ tempxaiworkshop@gmail.com