From Post-Hoc to Ante-Hoc: Consistently Explainable Semi-Supervised Time Series Classification

Semi-Supervised Time Series Classification (SS-TSC) with Deep Neural Networks (DNNs) achieves strong accuracy by leveraging unlabeled data, yet the resulting models remain black boxes that offer no insight into which features drive each prediction. Existing post-hoc explanation methods for TSC can identify important features but provide no guarantee that the model genuinely relies on them, while ante-hoc approaches impose architectural constraints that limit flexibility in existing model usages. Neither setting explore the semi-supervised regime, where limited labels can affect explainability. We propose \textbf{Sensory Gating}, the first SS-TSC framework that simultaneously bridges post-hoc attribution and ante-hoc explanation through a four-stage pipeline: (i) a base classifier is trained with our proposed Forecasting Joint-Embedding Predictive Architecture (F-JEPA) auxiliary objective; (ii) post-hoc attribution maps are converted into per-instance binary masks via greedy selection of sufficient features; (iii) a lightweight \textbf{Sensory Gate} amortizes these masks through Binary Cross Entropy (BCE) supervision with data-driven threshold calibration; and (iv) a knowledge distillation stage to provide an explainable SS-TSC student model that operates on gated (masked) input, while consistently reproduces the original black box teacher (F-JEPA)'s predictions. We validate our framework on eight datasets across four label ratios with five seeds per configuration. F-JEPA achieves better performance compared to recent state-of-the-art (SOTA) semi-supervised TSC methods, while the sensory gate reduces the fraction of features the classifier requires to 20--45\% with competitive accuracy. On MIT-BIH, where ground-truth saliency annotations are available, the gate's selected features align with domain-relevant regions while classification performance remains competitive with the ungated baseline at 10--20\% kept ratio.