This research introduces a novel per-timestep input-space adaptation framework designed for multivariate time-series models with fixed weights, addressing the need for secure deployments where regulatory or technical constraints prevent model fine-tuning. By back-propagating task loss through a frozen backbone using target-domain labels, the method enables effective adaptation in both source-free and source-assisted environments without requiring access to original training data. Evaluated against a convolutional backbone across clinical and sensor-based benchmarks, the approach yields substantial performance gains, significantly outperforming existing end-to-end and test-time adaptation baselines. Notably, the adapter matches or exceeds the performance of models trained natively on target data and exhibits unique architectural portability, allowing a single module to be deployed across different frozen predictors in a zero-shot capacity.
