基于战时空袭警报数据，建模预测特定区域未来警报发生
Predictive Analytics of Air Alerts in the Russian-Ukrainian War
- 利用相邻区域警报状态与时间特征构建预测模型
- 发现警报存在空间相关性及随时间变化的规律
- 适合关注冲突动态与应急预警系统的研究者参考
本文针对2022年2月24日爆发的俄乌战争中的空袭警报，开展探索性数据分析与预测分析。结果表明，不同区域的警报存在显著关联性，并呈现地理空间分布模式，使得基于特定时间窗口内某区域的警报预测成为可能。研究发现，某一区域的警报状态高度依赖其邻近区域的状态。季节性特征如小时、星期几和月份对目标变量预测至关重要。部分区域对从数据集初始日期起的天数特征高度敏感，表明空袭警报模式随时间演变。这些结果为建立时空联合预测模型提供了依据。
原文摘要 · Abstract (English)
The paper considers exploratory data analysis and approaches in predictive analytics for air alerts during the Russian-Ukrainian war which broke out on Feb 24, 2022. The results illustrate that alerts in regions correlate with one another and have geospatial patterns which make it feasible to build a predictive model which predicts alerts that are expected to take place in a certain region within a specified time period. The obtained results show that the alert status in a particular region is highly dependable on the features of its adjacent regions. Seasonality features like hours, days of a week and months are also crucial in predicting the target variable. Some regions highly rely on the time feature which equals to a number of days from the initial date of the dataset. From this, we can deduce that the air alert pattern changes throughout the time.
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