It may be critical for drivers to have information about the occupancy rates of the parking spaces around their destination in order to reduce the traffic density, a non- negligible part of which caused by the trips to find an available parking space. In this study, we predict parking occupancy rates (and thus, space availability) using three different techniques: (i) auto-regressive integrated moving average model, (ii) seasonal auto- regressive integrated moving average model and (iii) neural networks. In the implementation phase, we use the data set of the on-street parking spaces of the well- known “SFpark” project carried out in San Francisco. We take into account not only the past occupancy rates of parking spaces, but also exogenous variables that affect the corresponding occupancy rates as day type and time period of the day. We make predictions with different model structures of each of the considered methods for each parking space with different parking occupancy patterns in the data set and then compare the results to find the best model design for each parking space. We also, evaluate the results in terms of the superiority of the methods over each other and note that the performance of neural networks is better than those of the other approaches in terms of the mean squared errors.