The paper addresses the problem of time offset synchronization in the
presence of temperature variations, which lead to a non-Gaussian
environment. In this context, regular Kalman filtering reveals to be
suboptimal. A functional optimization approach is developed in order to
approximate optimal estimation of the clock offset between master and
slave. A numerical approximation is provided to this aim, based on
regular neural network training. Other heuristics are provided as well,
based on spline regression. An extensive performance evaluation
highlights the benefits of the proposed techniques, which can be easily
generalized to several clock synchronization protocols and operating
environments.