Temporal patterns in conflict prediction: An improved shape-based approach

Abstract:

Existing models for predicting conflict fatalities often yield cautious forecasts. Although these approaches tends to be accurate, they offer limited insight into temporal variations in conflict-related fatalities. In this paper, we introduce a novel methodology, which we call “Shape Finder”, to capture complex interdependencies in fatalities data. The method involves isolating historically analogous sequences of fatalities to create a reference repository. Predictions are then generated by analyzing the average future outcomes of these reference sequences, enabling the model to make more dynamic forecasts. Our approach maintains high accuracy while significantly enhancing the ability to predict shifts, surges, and declines in conflict fatalities over time. Empirical tests demonstrate that combining the shape finder methodology with existing approaches not only achieves a lower mean squared error (MSE), but also better accounts for variability in the time series data.

Authors:

Thomas Schincariol, Hannah Frank, and Thomas Chadefaux