Does Source-Free Time-Series Adaptation Really Require Updating the Encoder?

Abstract

Source-free domain adaptation (SFDA) adapts a pretrained model to an unlabeled target domain without accessing source data, enabling the reuse of time-series predictors across sensing systems. Existing methods commonly update the encoder using unlabeled target objectives. Yet changes in users, equipment, and operating conditions also alter the spectral characteristics of input signals, raising a fundamental question: does adaptation require changing the encoder, or can we instead adapt the signal it receives? We propose Source Free Frequency-Prototype Alignment (SF-FPA), which learns a spectral input transformation while freezing the source encoder and classifier parameters. First, a frequency adaptation layer applies amplitude and phase corrections to target signals. Separate branches adjust spectral magnitudes and phase relationships, with circular phase encoding avoiding discontinuities at the angular boundary. Second, confidence-filtered prototype alignment guides target representations toward source class prototypes. Combined with information maximization, this objective propagates gradients through the source predictor to learn spectral corrections that support class discrimination. Extensive experiments on three real-world time series benchmarks demonstrate state-of-the-art average Macro-F1 performance. Component ablations and controlled comparisons with encoder adaptation and a parameter-matched time-domain adapter further support spectral input adaptation as an effective approach to reusing source-trained predictors. The implementation is available at https://github.com/ShichangMeng/SF_FPA.