Time series saliency maps: Explaining models across multiple domains

摘要

Traditional saliency map methods, popularized in computer vision, highlight individual input points that contribute most to a model's output. However, in the context of time series, they offer limited insights because semantically meaningful features are often found in other domains. Thus, we introduce in this paper Cross-domain Integrated Gradients, a generalization of Integrated Gradients that enables feature attributions in any domain formulated as an invertible, differentiable transformation of the time domain. Our derivation extends Integrated Gradients into complex-valued domains, enabling frequency-based attributions, while preserving path independence and completeness. We validate our method via controlled mechanistic experiments, quantitative faithfulness and perturbation-stability tests, and real-world case studies. Across wearable heart-rate extraction, EEG-based seizure detection, and zero-shot forecasting, our proposed Cross-domain Integrated Gradients approach identifies whether predictions rely on heart-rate frequencies or interference, epileptic sources or artifacts, and trend or seasonal components, revealing model behaviour that time-domain saliency does not capture. We release an open-source library with TensorFlow, native PyTorch, and Captum support for plug-and-play cross-domain explainability of time-series models.