Unified Time Series Explanations via Amortized Optimization and Instance-level Multi-Expert Knowledge Distillation

Deep neural networks (DNNs) achieve high accuracy on time series classification (TSC) but remain opaque, hindering deployment in sensitive domains. Existing post-hoc TSC explanation methods rely on a single attribution perspective and incur high per-instance computational cost, limiting real-time use. We propose XMA, a framework that unifies multiple post-hoc explainers at the instance level and amortizes the cost of explanation into a learned inference pass. Instance-level Multi-Expert Knowledge Distillation (IMEKD) selects, per instance, the attribution map with the highest combined faithfulness and robustness score from a pool of XAI experts. Objective-Regularized Amortized Optimization Explanation (ORAOE) trains an explainer DNN to reproduce this instance-best supervision while directly optimizing differentiable faithfulness and robustness objectives, letting the student refine beyond any individual teacher. Faithfulness-Preserving Segmentation (FPS) converts point-wise maps into contiguous segments without changing their faithfulness score. Across four synthetic datasets, MIT-ECG with cardiologist-verified ground truth, and 11 multivariate UEA benchmarks on three DNN architectures, XMA attains the best AUPRC on all synthetic and MIT-ECG benchmarks among compared methods, and the highest faithfulness on 7 of 11 and robustness on 8 of 11 UEA datasets, while amortizing explanation into a lightweight learned explainer rather than relying on per-instance optimization.