Dynamic resource management techniques rely on power consumption and performance models to optimize the operating frequency and utilization of processing elements, such as CPU and GPU. Despite the importance of these decisions, many existing approaches rely on fixed power and performance models that are learned online. However, o.ine models cannot guarantee accuracy when workloads differ signifucantly from the training available at design time. This paper presents an online learning framework (STAFF) that constructs adaptive run-time models for stationary and non-stationary workloads. STAFF is the first framework that (1) guarantees stability while quickly adapting to workload changes, (2) performs online feature selection with linear complexity, and (3) adapts to new model coe.cients by employing adaptively varying forgetting factor, all at the same time. Experiments on an Intelr CoreTM i5 6th generation platform demonstrate up to 6. improvement in the performance prediction accuracy compared to existing techniques.