Recently, inspired by predictive process monitoring, the modeling and prediction of the entire process information system has been proposed as process model forecasting. By forecasting individual elements of a directly-follows graph, the future state of the system can be predicted. However, the current state-of-the-art principally employs univariate forecasting of direct-follows relationships (DFs). This univariate approach overlooks the process structure and possible relations between different elements within the process. This paper introduces a comprehensive deployment of multivariate time series models, more specifically a range of different machine- and deep learning approaches, to forecast DFs. These are benchmarked on different event logs collected from real-life event processes. Our extensive experiments reveal that the performance of these forecasting models varies significantly across different processes, highlighting the importance of model selection.