Existing models for predicting conflict fatalities frequently produce conservative forecasts that gravitate towards the mean. While these approaches have a low average prediction error, they offer limited insights into temporal variations in conflict-related fatalities. Yet, accounting for variability is particularly relevant for policymakers, providing an indication on when to intervene. In this article, we introduce a novel risk-taking methodology, the ‘Shape finder’, designed to capture variability in fatality data, or rather the sudden surges and declines in the number of deaths over time. The method involves isolating historically analogous sequences of fatalities to create a reference repository. Comparing the shape of the input sequence to the historical references, the most similar historical cases are selected. Predictions are then generated using the average future outcomes of the selected matches. The Shape finder is derived from the theoretical understanding that strategic and adaptive interactions between the government and a non-state armed group produce recurring temporal patterns in fatality data, which are indicative of broader developments. In this article, we demonstrate that our approach maintains high accuracy while significantly enhancing the ability to predict shifts, surges, and declines in conflict fatalities over time. We show that combining the Shape finder with existing approaches, the Violence Early-Warning System ensemble, achieves a lower mean squared error and better accounts for variability in fatality data. The Shape finder methodology performs particularly well for high intensity cases, or rather country-months with substantial armed violence.

中文翻译：

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考虑冲突动态的可变性：基于模式的预测模型

用于预测冲突死亡人数的现有模型经常产生趋向于平均值的保守预测。虽然这些方法的平均预测误差较低，但它们对冲突相关死亡人数的时间变化提供的见解有限。然而，考虑可变性对政策制定者来说尤其重要，它为何时进行干预提供了指示。在本文中，我们介绍了一种新的冒险方法，即“形状查找器”，旨在捕捉死亡数据的变化，或者更确切地说，死亡人数随时间的突然激增和下降。该方法涉及分离历史上相似的死亡序列以创建参考存储库。将 input 序列的形状与历史参考进行比较，选择最相似的历史案例。然后使用所选比赛的平均未来结果生成预测。Shape finder 源自政府和非国家武装团体之间的战略和适应性互动在死亡数据中产生重复的时间模式，这表明了更广泛的发展。在本文中，我们证明了我们的方法保持了高精度，同时显著提高了预测冲突死亡人数随时间变化、激增和下降的能力。我们表明，将 Shape finder 与现有方法（暴力早期预警系统集成）相结合，可以实现更低的均方误差，并更好地解释死亡数据的可变性。Shape finder 方法对于高强度病例，或者更确切地说是具有大量武装暴力的国家月份，表现特别好。