Visual Explainable AI for Time Series establishing a framework for explainable artificial intelligence for time series deep learning models using attributions and counterfactuals.
Deep learning models are often not applied to time series tasks as such models are not explainable or understandable. Visual explainable AI for time series strives to bridge the gap between state-of-the-art deep learning architectures for various tasks and explanations for such model decisions. The project will be divided into sub-projects that have been published and worked on as published workshop, conference, and journal papers.
Here are the direct links to the online demos for the corresponding conference papers.
https://icfts.time-series-xai.dbvis.de/
https://davots.time-series-xai.dbvis.de/