Buczak, Anna L. and Baugher, Benjamin D. and Martin, Christine S. and Keiley-Listermann, Meg W. and Howard, James and Parrish, Nathan H. and Stalick, Anton Q. and Berman, Daniel S. and Dredze, Mark H. (2022) Crystal Cube: Forecasting Disruptive Events. Applied Artificial Intelligence, 36 (1). ISSN 0883-9514
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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.
Item Type: | Article |
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Subjects: | Library Keep > Computer Science |
Depositing User: | Unnamed user with email support@librarykeep.com |
Date Deposited: | 14 Jun 2023 11:44 |
Last Modified: | 13 Jan 2024 04:45 |
URI: | http://archive.jibiology.com/id/eprint/1115 |