Forecasting conflict in africa with automated machine learning systems

Abstract

ABSTRACT The ViEWS problem is to forecast changes in the level of state-based violence for each of the next six months at the PRIO-GRID and country level. For this competition and toward the goal of improving sub-national and country level forecasts, we experiment with combinations of automated machine learning (autoML) systems and limited datasets that emphasize the endogenous nature of conflict. Two core findings emerge: autoML improves predictive performance and the Dynamics model performs best. The data used for the Dynamics model is limited to measures of state-based violence built from the event-level violence data plus those describing the spatial and temporal structure of the data. The intent is to capture spatial and temporal conflict dynamics while not overfitting to exogenous factors, which is especially problematic with flexible autoML algorithms and the types of highly disaggregate data used here. At the PGM level, this model won the ViEWS competition for both “predictive accuracy” and “originality.” Beyond the ViEWS competition, we expect conflict forecasting models that couple advanced autoML systems with variables that reflect a diverse set of conflict dynamics to have high predictive performance, especially at sub-national and sub-annual aggregations.