Copyright 2015 John Wiley & Sons, Ltd. This paper considers univariate and multivariate models to forecast monthly conflict events in the Sudan over the out-of-sample period 2009-2012. The models used to generate these forecasts were based on a specification from a machine learning algorithm fit to 2000-2008 monthly data. The model that includes previous month's wheat price performs better than a similar model which does not include past wheat prices (the univariate model). Both models did not perform well in forecasting conflict in a neighborhood of the 2012 'Heglig crisis'.
