A Hybrid Model of Crime Prediction

被引：4
|
作者：
Liu, Meilin [1 ]
Lu, Tianliang [1 ]
机构：
[1] Peoples Publ Secur Univ China, Coll Informat Technol & Cyber Secur, Beijing, Peoples R China
来源：
2018 INTERNATIONAL CONFERENCE ON COMPUTER INFORMATION SCIENCE AND APPLICATION TECHNOLOGY | 2019年 / 1168卷
基金：
中国国家自然科学基金; 国家重点研发计划;
关键词：
LSTM NEURAL-NETWORKS;
D O I：
10.1088/1742-6596/1168/3/032031
中图分类号：
TP301 [理论、方法];
学科分类号：
081202 ;
摘要：
According to statistics, crimes are not random in spatial and temporal distribution. The key to predicting policing is predicting in advance when and where a crime may occur, and providing a reference for preventive measures of police officers. In this paper, a hybrid model of LSTM and STARMA is established. Crime data is complicated. It can be decomposed into trend components, seasonal components, and random components. The LSTM model is established for the trend components and the seasonal components. The STARMA model is established about random components. It solves the problem that the STARMA cannot be modeled on nonlinear or non-stationary data. Verification results show that the model is effective in crime prediction.
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收藏
页数：7
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