  Asymptotic fluctuation theorems are statements of a Gallavotti-Cohen symmetry
in the rate function of either the time-averaged entropy production or heat
dissipation of a process. Such theorems have been proved for various general
classes of continuous-time deterministic and stochastic processes, but always
under the assumption that the forces driving the system are time independent,
and often relying on the existence of a limiting ergodic distribution. In this
paper we extend the asymptotic fluctuation theorem for the first time to
inhomogeneous continuous-time processes without a stationary distribution,
considering specifically a finite state Markov chain driven by periodic
transition rates. We find that for both entropy production and heat
dissipation, the usual Gallavotti-Cohen symmetry of the rate function is
generalized to an analogous relation between the rate functions of the original
process and its corresponding backward process, in which the trajectory and the
driving protocol have been time-reversed. The effect is that spontaneous
positive fluctuations in the long time average of each quantity in the forward
process are exponentially more likely than spontaneous negative fluctuations in
the backward process, and vice-versa, revealing that the distributions of
fluctuations in universes in which time moves forward and backward are related.
As an additional result, the asymptotic time-averaged entropy production is
obtained as the integral of a periodic entropy production rate that generalizes
the constant rate pertaining to homogeneous dynamics.
