Next-Generation Conflict Forecasting: Unleashing Predictive Patterns through Spatiotemporal Learning

TL;DR

This paper introduces a novel neural network architecture that accurately forecasts various types of violent conflict at high spatial and temporal resolutions, leveraging spatiotemporal learning without manual feature engineering.

Contribution

The study presents a new Monte Carlo Dropout LSTM U-Net model that jointly performs classification and regression for conflict prediction, achieving state-of-the-art results without manual feature engineering.

Findings

Achieves state-of-the-art performance across conflict prediction tasks.

Produces probabilistic estimates and quantifies forecast uncertainty.

Requires no manual feature engineering, relying solely on historical data.

Abstract

Forecasting violent conflict at high spatial and temporal resolution remains a central challenge for both researchers and policymakers. This study presents a novel neural network architecture for forecasting three distinct types of violence -- state-based, non-state, and one-sided -- at the subnational (priogrid-month) level, up to 36 months in advance. The model jointly performs classification and regression tasks, producing both probabilistic estimates and expected magnitudes of future events. It achieves state-of-the-art performance across all tasks and generates approximate predictive posterior distributions to quantify forecast uncertainty. The architecture is built on a Monte Carlo Dropout Long Short-Term Memory (LSTM) U-Net, integrating convolutional layers to capture spatial dependencies with recurrent structures to model temporal dynamics. Unlike many existing approaches, it requires no manual feature engineering and relies solely on historical conflict data. This design enables the model to autonomously learn complex spatiotemporal patterns underlying violent conflict. Beyond achieving state-of-the-art predictive performance, the model is also highly extensible: it can readily integrate additional data sources and jointly forecast auxiliary variables. These capabilities make it a promising tool for early warning systems, humanitarian response planning, and evidence-based peacebuilding initiatives.