Benchmarking Conflict Event Forecasting

Conflict-event forecasting is a high-stakes task with direct implications for humanitarian response, early warning, and policy planning. Despite the growing availability of fine-grained conflict-event data, the task remains underexplored in the machine learning community. We introduce ConflictCast, the first machine learning benchmark for conflict event forecasting. Given an observed history of geo-referenced conflict-events, models predict an ensamble of future event trajectories, with their times, locations, and types; sampled trajectories can then be evaluated directly or aggregated to the space–time grids used by existing operational forecasting systems. ConflictCast builds on four conflict-event datasets and includes neural spatio-temporal point processes, conflict-tailored neural models, tree-ensemble binned regressors, and simple baselines. We evaluate models under different conditioning regimes and a variety of metrics. Across datasets, we identify where current architectures succeed and fail, highlight directions for future work, and make standardized evaluation results publicly available through our website and code repository (anonymized links in the paper).