The criminal legal system is composed of many complex decision points, such as which charges are pursued, how bond is determined, plea deal negotiations, and sentencing. Given the profound impact of such decisions, there is a growing need to increase the transparency within this system. While prior machine learning approaches have focused on modeling single decisions (e.g., recidivism), this paper proposes a transformer-based time series model to forecast the outcome of the next proceeding in a case (e.g., charges accepted, bond set, plea deal made, etc.). By modeling a case as an event sequence, we enable a more nuanced analysis to support better oversight and understanding of decision making. We conduct an empirical analysis of thousands of cases using a newly constructed dataset derived from publicly available court records of a mid-sized U.S. city. We fit models that combine textual data from clerk notes with structured information about the defendant, prior charges, and courtroom. Our transformer model achieves .57 micro-F1 on a 20 class multi-label task, a significant improvement over several baselines and comparable to a GRU trained on the same input. We demonstrate how the model can be used to provide insights into decision-making and help court observers identify emerging issues within the jurisdiction studied.