<p>Quasi-periodicity refers to a pattern in a function where it appears periodic at a certain frequency but exhibits evolving amplitudes over time. This is often the case in practical settings such as the modeling of case counts of infectious disease or the population dynamics of species over time. In this paper, we consider a class of Gaussian processes, called seasonal Gaussian Processes (sGP), for model-based inference of such quasi-periodic behavior. We illustrate that the exact sGP can be efficiently fitted using its state space representation for equally spaced time points. However, for large datasets with irregular spacing, the exact approach becomes computationally inefficient and unstable. To address this, we develop a continuous finite dimensional approximation for sGP using the seasonal B-spline (sB-spline) basis constructed by damping B-splines with sinusoidal functions. We prove the covariance convergence rate of the proposed approximation to the true sGP as the number of basis functions increases, and show its superior approximation quality through numerical studies. We also provide a unified and interpretable way to define priors for the sGP, based on the notion of predictive standard deviation. Finally, we implement the proposed inference method on several real data examples to illustrate its practical usage.</p>