  This paper is devoted to studying the Onsager-Machlup functional for
stochastic differential equations with time-varying noise of the
{\alpha}-H\"older, 0<{\alpha}<1/4,
  dXt =f(t,Xt)dt+g(t)dWt.
  Our study focuses on scenarios where the diffusion coefficient g(t) exhibits
temporal variability, starkly contrasting the conventional assumption of a
constant diffusion coefficient in the existing literature. This variance brings
some complexity to the analysis. Through this investigation, we derive the
Onsager-Machlup functional, which acts as the Lagrangian for mapping the most
probable transition path between metastable states in stochastic processes
affected by time-varying noise. This is done by introducing new measurable
norms and applying an appropriate version of the Girsanov transformation. To
illustrate our theoretical advancements, we provide numerical simulations,
including cases of a one-dimensional SDE and a fast-slow SDE system, which
demonstrate the application to multiscale stochastic volatility models, thereby
highlighting the significant impact of time-varying diffusion coefficients.
