  We study the fluctuations of systems modeled by Markov jump processes with
periodic generators. We focus on observables defined through time-periodic
functions of the system's states or transitions. Using large deviation theory,
canonical biasing and generalized Doob transform, we characterize the
asymptotic fluctuations of such observables after a large number of periods by
obtaining the Markov process that produces them. We show that this process,
called driven process, is the minimum under constraint of the large deviation
function for occupation and jumps.
