IMFACT: Counterfactual Explanations for Time Series via Intrinsic Mode Function Substitution
Udo Schlegel, Julian Rakuschek, Thomas Seidl, Andreas Holzinger, Tobias Schreck, Javier Del Ser
cs.LG, cs.AI
Submitted: 2026-08-05
Comments: 16 pages, 2 figure, 2 tables, accepted at XKDD Workshop at ECML-PKDD
Code: https://github.com/visual-xai-for-time-series/counterfactual-explanations-for-time-series
License: http://creativecommons.org/licenses/by/4.0/
The gist: Oscillatory signals, such as vibration, carry class-discriminative information in specific frequency bands; perturbing them in raw feature space for counterfactual analysis easily destroys their
Terminology
Abstract
Oscillatory signals, such as vibration, carry class-discriminative information in specific frequency bands; perturbing them in raw feature space for counterfactual analysis easily destroys their temporal structure and produces physically implausible results. In this work, we introduce IMFACT (IMF-based counterfACTuals), a model-agnostic framework for generating plausible counterfactual explanations for time series classifiers that operates in the decomposition space of Empirical Mode Decomposition. An input signal is split into Intrinsic Mode Functions (IMFs), and selected IMFs are progressively substituted with those of a Nearest Unlike Neighbour (NUN) until the classifier flips to the target class. We evaluate six IMF-selection strategies and a multi-NUN cycling extension on two UCR benchmarks (FaultDetectionA, FruitFlies). The variance-based strategy with three NUNs outperforms two prominent baseline techniques on reliability and plausibility metrics, while cycling across three NUNs yields the best proximity across both datasets.
Sources
- Counterfactual Explanations for Time Series Should be Human-Centered and Temporally Coherent in Interventions
- Towards plausibility in time series counterfactual explanations
- Visual Explanations with Attributions and Counterfactuals on Time Series Classification
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