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用自我激发点过程建模恐怖袭击,并预测恐怖事件的数量
Siyi Wang1, Xu Wang1, Chenlong Li2
1Department of Mathematics, Wilfrid Laurier University, Waterloo, ON N2L 3C5, Canada.
Entropy (Basel, Switzerland)
|July 29, 2023
概括
本研究介绍了一种时间自我激发点过程模型,用于分析美国,土耳其和菲律宾的恐怖主义事件. 该模型通过结合事件顺序和机器学习来提高国家安全的预测准确性.
科学领域:
- 计算社会科学 计算社会科学
- 统计 统计 统计 统计
- 国家安全研究 国家安全研究
背景情况:
- 恐怖主义是一个重大的全球国家安全挑战,由分裂主义和极端民族主义等因素驱动.
- 了解恐怖主义的时间模式对于有效的安全战略至关重要.
研究的目的:
- 开发和应用一个时间自我激发点过程模型来分析恐怖主义数据.
- 通过使用先进的统计和机器学习技术,改进对未来恐怖事件的预测.
主要方法:
- 利用时间自我激发点过程模型,应用于来自美国,土耳其和菲律宾的恐怖主义数据.
- 引入了一个订单标记和奖励术语来解释同时发生的事件.
- 基于日期和基于月份的抵达时间模型进行比较.
- 开发了一种混合模拟和随机森林机器学习模型用于事件预测.
主要成果:
- 开发的模型有效地捕捉了恐怖主义事件中的时间模式.
- 包含订单和奖励条款可以改善对同时发生事件的参数选择.
- 混合预测模型在预测恐怖事件数量方面表现出更高的准确性.
结论:
- 时间自我激发点过程模型为理解恐怖主义事件动态提供了有洞察力的方法.
- 预测模型有实际应用,用于加强国家安全战略和反恐工作.
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