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Modeling Terror Attacks with Self-Exciting Point Processes and Forecasting the Number of Terror Events
Rampant terrorism poses a serious threat to the national security of many countries worldwide, particularly due to separatism and extreme nationalism. This paper focuses on the development and application of a temporal self-exciting point process model to the terror data of three countries: the US,...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10378437/ https://www.ncbi.nlm.nih.gov/pubmed/37509958 http://dx.doi.org/10.3390/e25071011 |
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author | Wang, Siyi Wang, Xu Li, Chenlong |
author_facet | Wang, Siyi Wang, Xu Li, Chenlong |
author_sort | Wang, Siyi |
collection | PubMed |
description | Rampant terrorism poses a serious threat to the national security of many countries worldwide, particularly due to separatism and extreme nationalism. This paper focuses on the development and application of a temporal self-exciting point process model to the terror data of three countries: the US, Turkey, and the Philippines. To account for occurrences with the same time-stamp, this paper introduces the order mark and reward term in parameter selection. The reward term considers the triggering effect between events in the same time-stamp but different order. Additionally, this paper provides comparisons between the self-exciting models generated by day-based and month-based arrival times. Another highlight of this paper is the development of a model to predict the number of terror events using a combination of simulation and machine learning, specifically the random forest method, to achieve better predictions. This research offers an insightful approach to discover terror event patterns and forecast future occurrences of terror events, which may have practical application towards national security strategies. |
format | Online Article Text |
id | pubmed-10378437 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-103784372023-07-29 Modeling Terror Attacks with Self-Exciting Point Processes and Forecasting the Number of Terror Events Wang, Siyi Wang, Xu Li, Chenlong Entropy (Basel) Article Rampant terrorism poses a serious threat to the national security of many countries worldwide, particularly due to separatism and extreme nationalism. This paper focuses on the development and application of a temporal self-exciting point process model to the terror data of three countries: the US, Turkey, and the Philippines. To account for occurrences with the same time-stamp, this paper introduces the order mark and reward term in parameter selection. The reward term considers the triggering effect between events in the same time-stamp but different order. Additionally, this paper provides comparisons between the self-exciting models generated by day-based and month-based arrival times. Another highlight of this paper is the development of a model to predict the number of terror events using a combination of simulation and machine learning, specifically the random forest method, to achieve better predictions. This research offers an insightful approach to discover terror event patterns and forecast future occurrences of terror events, which may have practical application towards national security strategies. MDPI 2023-06-30 /pmc/articles/PMC10378437/ /pubmed/37509958 http://dx.doi.org/10.3390/e25071011 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Wang, Siyi Wang, Xu Li, Chenlong Modeling Terror Attacks with Self-Exciting Point Processes and Forecasting the Number of Terror Events |
title | Modeling Terror Attacks with Self-Exciting Point Processes and Forecasting the Number of Terror Events |
title_full | Modeling Terror Attacks with Self-Exciting Point Processes and Forecasting the Number of Terror Events |
title_fullStr | Modeling Terror Attacks with Self-Exciting Point Processes and Forecasting the Number of Terror Events |
title_full_unstemmed | Modeling Terror Attacks with Self-Exciting Point Processes and Forecasting the Number of Terror Events |
title_short | Modeling Terror Attacks with Self-Exciting Point Processes and Forecasting the Number of Terror Events |
title_sort | modeling terror attacks with self-exciting point processes and forecasting the number of terror events |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10378437/ https://www.ncbi.nlm.nih.gov/pubmed/37509958 http://dx.doi.org/10.3390/e25071011 |
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