1. A method for forecasting an availability of resources, comprising: training, by a processor, a non-parametric forecasting model based both on historical supply data and calendar information, wherein the historical supply data specifies one or more past time periods in a series of time periods and a number of resources that were available during each of the one or more past time periods, wherein the calendar information identifies one or more events and, for each event, one or more corresponding time periods in the series of time periods during which the event occurs, thereby producing a trained forecasting model that accounts for event-driven resource availability, wherein the training of the non-parametric forecasting model includes training of a Gaussian process model, wherein the training of the Gaussian process model includes training a Gaussian model with an event-driven kernel as a covariance, and wherein the event-driven kernel includes a distance function that defines a de-correlation level between a calendar feature vector associated with the historical supply data and a calendar feature vector associated with an event type. receiving, at the processor, a specification of one or more future time periods in the series of time periods; and generating, by the processor, a forecasted number of resources available for each of the one or more future time periods based on the trained non-parametric forecasting model.