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cs.LG2024
Interactive Counterfactual Generation for Univariate Time Series
Udo Schlegel, Julius Rauscher, Daniel A. Keim
We propose an interactive methodology for generating counterfactual explanations for univariate time series data in classification tasks by leveraging 2D projections and decision b…
cs.LG2024★ 1 cited
Finding the DeepDream for Time Series: Activation Maximization for Univariate Time Series
Udo Schlegel, Daniel A. Keim, Tobias Sutter
Understanding how models process and interpret time series data remains a significant challenge in deep learning to enable applicability in safety-critical areas such as healthcare…
cs.LG2023★ 1 cited
Introducing the Attribution Stability Indicator: a Measure for Time Series XAI Attributions
Udo Schlegel, Daniel A. Keim
Given the increasing amount and general complexity of time series data in domains such as finance, weather forecasting, and healthcare, there is a growing need for state-of-the-art…