We establish particular wavelet-based decompositions of Gaussian stationary processes in continuous time. These decompositions have a multiscale structure, independent Gaussian random variables in high-frequency terms, and the random coefficients of low-frequency terms approximating the Gaussian stationary process itself. They can also be viewed as extensions of the earlier wavelet-based decompositions of Zhang and Walter (IEEE Trans. Signal Process. 42(7):1737–1745, [1994]) for stationary processes, and Meyer et al. (J. Fourier Anal. Appl. 5(5):465–494, [1999]) for fractional Brownian motion. Several examples of Gaussian random processes are considered such as the processes with rational spectral densities. An application to simulation is presented where an associated Fast Wavelet Transform-like algorithm plays a key role.

CEMAT - Center for Computational and Stochastic Mathematics