Process analytics is an umbrella of data-driven techniques which includes\nmaking predictions for individual process instances or overall process models.\nAt the instance level, various novel techniques have been recently devised,\ntackling next activity, remaining time, and outcome prediction. At the model\nlevel, there is a notable void. It is the ambition of this paper to fill this\ngap. To this end, we develop a technique to forecast the entire process model\nfrom historical event data. A forecasted model is a will-be process model\nrepresenting a probable future state of the overall process. Such a forecast\nhelps to investigate the consequences of drift and emerging bottlenecks. Our\ntechnique builds on a representation of event data as multiple time series,\neach capturing the evolution of a behavioural aspect of the process model, such\nthat corresponding forecasting techniques can be applied. Our implementation\ndemonstrates the accuracy of our technique on real-world event log data.\n