【目的】研究日盜竊犯罪數量的預測問題。【方法】基于LSTM網絡,利用中國北方某大型城市2005年1月1日至2007年2月24日以及2009年1月1日至2011年1月7日的每日實際盜竊犯罪數據,設置三個算例分別進行時間序列預測研究及驗證,并與ARIMA、支持向量回歸、隨機森林以及XGBoost方法的預測結果進行對比。【結果】LSTM網絡模型能夠較好地預測日盜竊犯罪數量的變化趨勢,三個算例中的百分比均方根誤差分別為18.4%、11.7%、41.9%,性能均優于ARIMA、支持向量回歸、隨機森林以及XGBoost模型。【局限】對日盜竊犯罪數量波動較大時段的預測需要開展進一步研究。【結論】本文的研究結果預期可以為社區安全防范措施的調整、巡邏警力測算與部署等具體業務工作提供決策支持。

[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.