犯罪量动态优化组合预测方法

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。

Dynamic Optimization Combination Forecasting Method for Crime Quantity

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 posterio...