Predicting Time Series of Theft Crimes Based on LSTM Network
Yan Jinghua1,2,3(),Hou Miaomiao3
1National Science Library, Chinese Academy of Sciences, Beijing 100190, China 2Department of Library, Information and Archives Management, School of Economics and Management, University of Chinese Academy of Sciences, Beijing 100190, China 3School of Information and Network Security, People’s Public Security University of China, Beijing 100038, China
[Objective] This paper tries to predict the daily number of theft activities. [Methods] We used LSTM network to analyze theft data from a large city in north China. First, we retrieved our data from January 1, 2005 to February 24, 2007 and from January 1, 2009 to January 7, 2011, respectively. Then, we set three different cases to examine the time series prediction of the daily number. Finally, we compared our results with those of ARIMA, Support Vector Regression, Random Forest and XGBoost with the same data set. [Results] The percentage root mean square error (PRMSE) of our model were 18.4%, 11.7% and 41.9%, respectively, which were better than those of ARIMA, Support Vector Regression, Random Forest or XGBoost model. [Limitations] More research is needed to predict the period when the number of theft crimes fluctuates dramatically. [Conclusions] The proposed model could improve the decision makings for community safety, police patrol and other specific missions.
颜靖华,侯苗苗. 基于LSTM网络的盗窃犯罪时间序列预测研究*[J]. 数据分析与知识发现, 2020, 4(11): 84-91.
Yan Jinghua,Hou Miaomiao. Predicting Time Series of Theft Crimes Based on LSTM Network. Data Analysis and Knowledge Discovery, 2020, 4(11): 84-91.
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