标题： 基于稀疏时间融合变压器和高斯过程混合模型的地缘政治事件预测：中东和美国冲突动态案例研究 显示英文标题

标题： Forecasting Geopolitical Events with a Sparse Temporal Fusion Transformer and Gaussian Process Hybrid: A Case Study in Middle Eastern and U.S. Conflict Dynamics

摘要： 从全球事件、语言和语气数据库（GDELT）等数据源预测地缘政治冲突是国家安全的重要挑战。 此类数据的固有稀疏性、突发性和过度离散性导致标准深度学习模型，包括时间融合变换器（TFT），产生不可靠的长期预测。 我们引入了STFT-VNNGP，这是一种混合架构，在2023年威胁检测算法（ATD）竞赛中克服了这些限制而获胜。 为弥补这一差距，我们的模型采用两阶段过程：首先，TFT捕获复杂的时间动态以生成多分位数预测。 这些分位数随后作为有根据的输入提供给变分最近邻高斯过程（VNNGP），该过程执行合理的时空平滑和不确定性量化。 在对中东和美国冲突动态的案例研究中，STFT-VNNGP始终优于独立的TFT，表现出更好的能力来预测突发事件时期的时机和幅度，尤其是在远距离时间范围内。 这项工作提供了一个强大的框架，从具有挑战性的事件数据中生成更可靠和可操作的情报，所有代码和工作流程均已公开，以确保可重复性。 显示英文摘要

摘要： Forecasting geopolitical conflict from data sources like the Global Database of Events, Language, and Tone (GDELT) is a critical challenge for national security. The inherent sparsity, burstiness, and overdispersion of such data cause standard deep learning models, including the Temporal Fusion Transformer (TFT), to produce unreliable long-horizon predictions. We introduce STFT-VNNGP, a hybrid architecture that won the 2023 Algorithms for Threat Detection (ATD) competition by overcoming these limitations. Designed to bridge this gap, our model employs a two-stage process: first, a TFT captures complex temporal dynamics to generate multi-quantile forecasts. These quantiles then serve as informed inputs for a Variational Nearest Neighbor Gaussian Process (VNNGP), which performs principled spatiotemporal smoothing and uncertainty quantification. In a case study forecasting conflict dynamics in the Middle East and the U.S., STFT-VNNGP consistently outperforms a standalone TFT, showing a superior ability to predict the timing and magnitude of bursty event periods, particularly at long-range horizons. This work offers a robust framework for generating more reliable and actionable intelligence from challenging event data, with all code and workflows made publicly available to ensure reproducibility.