262 citations · 272 across the 13 of their papers we have counts for
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cs.LG2021
Time Series Model Attribution Visualizations as Explanations
Udo Schlegel, Daniel A. Keim
Attributions are a common local explanation technique for deep learning models on single samples as they are easily extractable and demonstrate the relevance of input values. In ma…
cs.LG2021★ 1 cited
TS-MULE: Local Interpretable Model-Agnostic Explanations for Time Series Forecast Models
Udo Schlegel, Duy Vo Lam, Daniel A. Keim +1
Time series forecasting is a demanding task ranging from weather to failure forecasting with black-box models achieving state-of-the-art performances. However, understanding and de…