The growing demand for personalized and private on-device applications highlights the importance of source-free unsupervised domain adaptation (SFDA) methods, especially for time-series data, where individual differences produce large domain shifts. As sensor-embedded mobile devices become ubiquitous, optimizing SFDA methods for parameter utilization and data-sample efficiency in time-series contexts becomes crucial. Personalization in time series is necessary to accommodate the unique patterns and behaviors of individual users, enhancing the relevance and accuracy of the predictions. In this…Apple Machine Learning Research