Crystal Cube: Forecasting Disruptive Events
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 n...
| Main Authors: | , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Taylor & Francis Group
2022-12-01
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| Series: | Applied Artificial Intelligence |
| Online Access: | http://dx.doi.org/10.1080/08839514.2021.2001179 |
| _version_ | 1797641068212125696 |
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| author | 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 |
| author_facet | 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 |
| author_sort | Anna L. Buczak |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-11T13:40:11Z |
| format | Article |
| id | doaj.art-4360a353004f448eaa73015ed8788c44 |
| institution | Directory Open Access Journal |
| issn | 0883-9514 1087-6545 |
| language | English |
| last_indexed | 2024-03-11T13:40:11Z |
| publishDate | 2022-12-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Applied Artificial Intelligence |
| spelling | doaj.art-4360a353004f448eaa73015ed8788c442023-11-02T13:36:37ZengTaylor & Francis GroupApplied Artificial Intelligence0883-95141087-65452022-12-0136110.1080/08839514.2021.20011792001179Crystal Cube: Forecasting Disruptive EventsAnna L. Buczak0Benjamin D. Baugher1Christine S. Martin2Meg W. Keiley-Listermann3James Howard4Nathan H. Parrish5Anton Q. Stalick6Daniel S. Berman7Mark H. Dredze8Johns Hopkins University Applied Physics LaboratoryJohns Hopkins University Applied Physics LaboratoryJohns Hopkins University Applied Physics LaboratoryJohns Hopkins University Applied Physics LaboratoryJohns Hopkins University Applied Physics LaboratoryJohns Hopkins University Applied Physics LaboratoryJohns Hopkins University Applied Physics LaboratoryJohns Hopkins University Applied Physics LaboratoryJohns Hopkins UniversityDisruptive 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.http://dx.doi.org/10.1080/08839514.2021.2001179 |
| spellingShingle | 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 Crystal Cube: Forecasting Disruptive Events Applied Artificial Intelligence |
| title | Crystal Cube: Forecasting Disruptive Events |
| title_full | Crystal Cube: Forecasting Disruptive Events |
| title_fullStr | Crystal Cube: Forecasting Disruptive Events |
| title_full_unstemmed | Crystal Cube: Forecasting Disruptive Events |
| title_short | Crystal Cube: Forecasting Disruptive Events |
| title_sort | crystal cube forecasting disruptive events |
| url | http://dx.doi.org/10.1080/08839514.2021.2001179 |
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