Crystal Cube: Forecasting Disruptive Events. Issue 1 (31st December 2022)

• Record Type: Journal Article
• Authors: Buczak, Anna L.; Baugher, Benjamin D.; Martin, Christine S.; Keiley-Listermann, Meg W.; Howard, James; Parrish, Nathan H.; Stalick, Anton Q.; Berman, Daniel S.; Dredze, Mark H.
• Is Part Of: Applied artificial intelligence. Volume 36:Issue 1(2022)
• Publication Date: 2022-12-31
• Subjects: Artificial intelligence -- Periodicals; 006.3
• DOI: 10.1080/08839514.2021.2001179
• Languages: English
• ISSNs: 0883-9514

Abstract

ABSTRACT: Disruptive events within a country can have global repercussions, creating a need for the anticipation and planning of these events. Crystal Cube (CC) is a novel approach to forecasting disruptive political events at least one month into the future. The system uses a recurrent neural network and a novel measure of event similarity between past and current events. We also introduce the innovative Thermometer of Irregular Leadership Change (ILC). We present an evaluation of CC in predicting ILC for 167 countries and show promising results in forecasting events one to twelve months in advance. We compare CC results with results using a random forest as well as previous work.