计算机工程 ›› 2011, Vol. 37 ›› Issue (17): 274-275,278.doi: 10.3969/j.issn.1000-3428.2011.17.092

• 开发研究与设计技术 • 上一篇    下一篇

犯罪量动态优化组合预测方法

李 明,薛安荣,王富强,吴正寅   

  1. (江苏大学计算机科学与通信工程学院,江苏 镇江 212013)
  • 收稿日期:2011-03-04 出版日期:2011-09-05 发布日期:2011-09-05
  • 作者简介:李 明(1984-),男,硕士,主研方向:数据挖掘;薛安荣,副教授、博士、CCF会员;王富强、吴正寅,硕士
  • 基金项目:
    国家自然科学基金资助项目(60773049);江苏大学高级人才启动基金资助项目(09JDG041)

Dynamic Optimization Combination Forecasting Method for Crime Quantity

LI Ming, XUE An-rong, WANG Fu-qiang, WU Zheng-yin   

  1. (School of Computer Science and Telecommunication Engineering, Jiangsu University, Zhenjiang 212013, China)
  • Received:2011-03-04 Online:2011-09-05 Published:2011-09-05

摘要: 单一预测模型在预测犯罪量时难以协调拟合和泛化关系,从而影响预测结果的准确性。针对以上问题,提出一种数据驱动的可动态优化组合预测方法。以分析自回归求和移动平均模型、向量自回归模型及支持向量机模型的优点为基础,使用后验概率为每个模型赋予权重,结合误差最小原则动态调整权重。实验结果表明,该方法具有较高的预测精度和稳定性,能满足短时犯罪量预测的需要。

关键词: 犯罪量预测, 组合预测模型, 支持向量机模型, 向量自回归模型, 自回归求和移动平均模型

Abstract: When single forecasting model forecastes the crime quantity, it is difficult to coordinate the fitting and generalization. The result of forecasting is not accuracy. Aiming at the problem presented above, this paper proposes a data-driven dynamic optimization combination forecasting method based on each advantages of the model virtues of Autoregressive Integrated Moving Average(ARIMA), Vector Autoregressive Model(VAR) and Support Vector Machine(SVM). The method gives the weight to each model by using the posterior probability, then dynamic adjusts the weight on the principle of minimum error. Experimental results show that the method has high prediction accuracy and stability to meet the needs of short-time crime forecasting.

Key words: crime quantity forecasting, combination forecasting model, Support Vector Machine(SVM) model, Vector Autoregressive Mo- del(VAR), Autoregressive Integrated Moving Average(ARIMA) model

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