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.
arXiv:2608. 08207v1 Announce Type: cross Abstract: Multivariate Time Series Classification (MTSC) demands models that can effectively capture complex temporal patterns across multiple scales while remaining computationally efficient.
By Pingping Liu, Muyao Wang, Zijian Zhang, Tongshun Zhang, Hao Miao, Guorui Xie, Qingliang Li, Qiuzhan Zhou
arXiv:2607. 21573v1 Announce Type: cross Abstract: Faithful explanations of time-series classifiers should identify subsequences that are not only sufficient to preserve a black-box model's prediction, but also necessary for maintaining it.
By Hongnan Ma, Yiwei Shi, Mengyue Yang, Weiru Liu
This paper proposes ConceptCF, a method for counterfactual generation that operates on human-interpretable concepts. In high-stakes domains such as healthcare and predictive maintenance, artificial intelligence models can increase efficiency and safety.
arXiv:2607. 18748v1 Announce Type: cross Abstract: This paper proposes ConceptCF, a method for counterfactual generation that operates on human-interpretable concepts.
By Annemarie Jutte, Faizan Ahmed, Jeroen Linssen, Maurice van Keulen
arXiv:2605. 20088v2 Announce Type: replace-cross Abstract: Discovering shapelets -- i.
By Seongjun Lee, Seokhyun Lee, Changhee Lee