With the growth in the complexity of real-time embedded systems, there is an increasing need for tools and techniques to understand and compare the observed runtime behavior of a system with the expected one. Since many realtime applications require periodic interactions with the environment, one of the fundamental problems in guaranteeing/monitoring their temporal correctness is to be able to infer the periodicity of certain events in the system. The practicability of a period inference tool, however, depends on both its accuracy and robustness (resilience) against noise in the output trace of the system, e.g., when the system trace is impacted by events that have a non-deterministic nature such as the presence of aperiodic tasks, release jitters and runtime execution-time variations of the tasks. This work (i) presents a period inference framework that uses regression-based machine-learning (RBML) methods, (ii) thoroughly investigates the accuracy and robustness of different families of RBML methods in the presence of uncertainties in the system parameters, and (iii) proposes further accuracy improvements by deriving candidate pruning rules based on the inherent properties of the underlying scheduling policies. We show, on both synthetically generated traces and traces from actual systems, that our solutions can reduce the error of period estimation by two to three orders of magnitudes w.r.t. state of the art. Also, our methods showed to be robust against most sources of disturbance.