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

TL;DR

This paper introduces a hybrid model combining a Temporal Fusion Transformer and Gaussian Process to improve long-term forecasting of sparse, bursty geopolitical events, demonstrated through conflict prediction in the Middle East and U.S.

Contribution

The paper presents STFT-VNNGP, a novel hybrid architecture that enhances long-horizon forecasting of complex, sparse event data by integrating deep learning with probabilistic smoothing.

Findings

Outperforms standalone TFT in conflict prediction accuracy

Effectively captures timing and magnitude of bursty events

Provides reliable uncertainty quantification for long-range forecasts

Abstract

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.