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Bridging the gap between attribution and counterfactual explanations, this framework achieves unprecedented stability and reliability in time series model interpretability.
Faithful explanations in time series forecasting can be achieved without sacrificing accuracy, as IB-Forecast demonstrates superior performance over traditional interpretable models and black-box approaches.
