The rising demand for customized and personal on-device purposes highlights the significance of source-free unsupervised area adaptation (SFDA) strategies, particularly for time-series information, the place particular person variations produce giant area shifts. As sensor-embedded cellular gadgets change into ubiquitous, optimizing SFDA strategies for parameter utilization and data-sample effectivity in time-series contexts turns into essential. Personalization in time collection is important to accommodate the distinctive patterns and behaviors of particular person customers, enhancing the relevance and accuracy of the predictions. On this work, we introduce a novel paradigm for source-model preparation and target-side adaptation aimed toward bettering each parameter and pattern effectivity in the course of the target-side adaptation course of. Our method re-parameterizes source-model weights with Tucker-style decomposed elements in the course of the source-model preparation section. Then, on the time of target-side adaptation, solely a subset of those decomposed elements is fine-tuned. This technique not solely enhances parameter effectivity, but additionally implicitly regularizes the difference course of by constraining the mannequin’s capability, which is crucial for personalization in numerous and dynamic time-series environments. Furthermore, the proposed technique achieves general mannequin compression and improves inference effectivity, making it extremely appropriate for resource-constrained gadgets. Intensive experiments on numerous time-series SFDA benchmark datasets exhibit the effectiveness and effectivity of our method, underscoring its potential for advancing customized on-device time-series purposes.