Making future projections about quantities of interest is a key component of decision-making, which has broad applications. For instance, in healthcare, one may be interested in monitoring the severity level of a disease given a treatment plan, while carefully accounting for potential sources of uncertainty. Alternatively, one may be interested in predicting the occupancy level of a data center or of a customer support office throughout the week. This project aims to develop methods, based on deep neural networks, to make such predictions from data. Specifically, we plan to rely on a highly flexible architecture, called Transformers, to produce models that are broadly applicable to various kinds of data and problem settings. If successful, our work will enable the improvement of forecasting in ServiceNow products and will provide new tools with the potential of positively impacting a variety of fields such as healthcare, finance, production planning, and more.