The development of the Cognitive Radio(CR) technology has benefited from the availability of realistic and accurate spectrum occupancy models. The spectrum occupancy models proposed in the literatures so far are able to capture and reproduce the statistical characteristics of occupied time series. For example, the busy/idle-period lengths of terrestrial wireless network can be fitted by Generalized Pareto(GP) distribution, exponential distribution, etc. However, the traditional parameter estimation distribution cannot give a good fit in satellite link spectrum occupancy. In this context, a method of Kernel Density Estimation(KDE) is proposed to fit the probability density distribution. On this basis, the Auto Regressive Integrated Moving Average Model(ARIMA) and fuzzy neural network are adopted to predict and compare the time series of the spectrum occupancy models. The conclusion shows that the proposed method can describe and reproduce the statistical characteristics of the occupied time series of the S-band used in the satellite downlink more accurately; while the prediction of the fuzzy neural network is more accurate than that of the ARIMA model.