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. 04777v1 Announce Type: cross 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.
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.
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.
arXiv:2605. 20088v2 Announce Type: replace-cross Abstract: Discovering shapelets -- i.
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.
The paper introduces DualRes, a compact oscillatory state‑space model designed for vibration‑based fault diagnosis when labeled data are scarce and computational resources are limited. DualRes integrates two spectral views of vibration and employs selective oscillatory memory to learn how long to retain temporal patterns, resulting in a lightweight encoder with only 39,528 parameters. Evaluations on six bearing datasets and a gearbox benchmark show that DualRes outperforms nine competing methods across most label budgets, achieving significant gains in macro‑F1, faster inference, and reduced storage requirements.
arXiv:2606. 03631v1 Announce Type: cross Abstract: Multivariate time series classification (MTSC) is pivotal in high-stakes domains, such as clinical diagnosis and industrial fault detection, where safe deployment necessitates transparent decision-making.
arXiv:2602. 08638v2 Announce Type: replace-cross Abstract: As a fundamental data mining task, unsupervised time series anomaly detection (TSAD) aims to build a model for identifying abnormal timestamps without assuming the availability of annotations.
The paper introduces XACT, a framework that learns sparse attribution masks over coefficients from any invertible time‑frequency transform, such as STFT, continuous wavelet transform, and discrete wavelet transform. XACT extends the virtual inspection layer approach to wavelet transforms, enabling Layer‑wise Relevance Propagation (LRP) to generate explanations in these representations. Experiments on synthetic and two real‑world datasets show that XACT produces precise, sparse, and structured explanations, often outperforming baseline methods in highlighting relevant features.
WinoTS introduces a wavelet‑based self‑distillation framework for time‑series models that uses time‑frequency augmentations to create multi‑scale structural views, avoiding distortion of signal dynamics. The method outperforms state‑of‑the‑art baselines in long‑term forecasting, cross‑domain zero‑shot transfer, and unsupervised anomaly detection, and linear probing on frozen representations often beats fully supervised training from scratch. Ablation studies show WinoTS is architecture‑agnostic and demonstrates that time‑frequency transformations offer a principled alternative to vision‑style spatial augmentations.
Counterfactual (CF) explanations for time-series classifiers are usually evaluated one example at a time: what minimal edit flips this single window's prediction? We argue that the more informative qu...