Conventional unsupervised domain adaptation (UDA) typically relies on updating the predictor's weights to optimize performance on the target domain using a labeled source dataset and unlabeled target data. However, a growing demand for secure deployments, such as closed inference APIs, firmware, or regulatory certification, means models are increasingly released with locked weights. We study domain adaptation in this locked-predictor regime for multivariate time-series models, where the deploying site holds its own labels but source training data may be inaccessible. We introduce PILOT (Per-timestep Input-space adapter for LOcked predicTors), a per-timestep input-space adapter trained against the frozen predictor by back-propagating the task loss using target labels. The framework supports two settings: a source-free variant for strict privacy constraints and a source-assisted variant when data sharing is permitted. We evaluate PILOT across five clinical time-series tasks (encompassing two domain shifts) and five AdaTime sensor benchmarks using a frozen ID-CNN backbone. PILOT achieves state-of-the-art performance across all comparable frozen-backbone, end-to-end, and test-time adaptation baselines. It yields a +15.3 improvement in AUCPR for AKI, increases the mean Macro-F1 score by +10.1 over the leading test-time adaptation method on AdaTime and matches or exceeds the performance of natively trained target-domain models. Furthermore, training PILOT on a single architecture enables zero-shot transfer to other frozen predictors, allowing the same PILOT module to be reused across different architectures.
