Abstract

Many political and societal factors can affect the course of geopolitical events, making them extremely challenging to forecast. In this work, we seek to develop a computational approach for forecasting geopolitical events. We show that one can leverage past geopolitical interactions to predict future related geopolitical interactions that have not necessarily occurred before. We propose to leverage ICEWS, a geopolitical interaction event dataset, to develop a predictive modeling framework for geopolitical events using both supervised and unsupervised machine learning approaches.

Metadata

publication

ICML Workshop on Learning and Reasoning with Graph-Structured Data, 2019

year

2019

publication date

2019

authors

Mehrnoosh Mirtaheri, Sami Abu-El-Haija, Tozammel Hossain

link

https://graphreason.github.io/papers/34.pdf

resource_link

https://graphreason.github.io/papers/34.pdf

journal

ICML Workshop on Learning and Reasoning with Graph-Structured Data