Crystal Cube: Forecasting Disruptive Events
| Autor: | Anna L. Buczak, Benjamin D. Baugher, Christine S. Martin, Meg W. Keiley-Listermann, James Howard, Nathan H. Parrish, Anton Q. Stalick, Daniel S. Berman, Mark H. Dredze |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2022 |
| Předmět: | |
| Zdroj: | Applied Artificial Intelligence, Vol 36, Iss 1 (2022) |
| Druh dokumentu: | article |
| ISSN: | 0883-9514 1087-6545 08839514 |
| DOI: | 10.1080/08839514.2021.2001179 |
| Popis: | 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. |
| Databáze: | Directory of Open Access Journals |
| Externí odkaz: |
|