Alwee Razana, Shamsuddin Siti Mariyam Hj, Sallehuddin Roselina
Soft Computing Research Group, Faculty of Computing, Universiti Teknologi Malaysia, 81310 Skudai, Johor, Malaysia.
ScientificWorldJournal. 2013 May 23;2013:951475. doi: 10.1155/2013/951475. Print 2013.
Crimes forecasting is an important area in the field of criminology. Linear models, such as regression and econometric models, are commonly applied in crime forecasting. However, in real crimes data, it is common that the data consists of both linear and nonlinear components. A single model may not be sufficient to identify all the characteristics of the data. The purpose of this study is to introduce a hybrid model that combines support vector regression (SVR) and autoregressive integrated moving average (ARIMA) to be applied in crime rates forecasting. SVR is very robust with small training data and high-dimensional problem. Meanwhile, ARIMA has the ability to model several types of time series. However, the accuracy of the SVR model depends on values of its parameters, while ARIMA is not robust to be applied to small data sets. Therefore, to overcome this problem, particle swarm optimization is used to estimate the parameters of the SVR and ARIMA models. The proposed hybrid model is used to forecast the property crime rates of the United State based on economic indicators. The experimental results show that the proposed hybrid model is able to produce more accurate forecasting results as compared to the individual models.
犯罪预测是犯罪学领域的一个重要研究方向。线性模型，比如回归模型和计量经济学模型，在犯罪预测中被广泛应用。然而，在实际犯罪数据中，数据通常既包含线性成分也包含非线性成分。单一模型可能不足以识别数据的所有特征。本研究的目的是引入一种将支持向量回归（SVR）和自回归积分移动平均（ARIMA）相结合的混合模型，用于犯罪率预测。支持向量回归对于小训练数据和高维问题具有很强的鲁棒性。同时，自回归积分移动平均能够对多种类型的时间序列进行建模。然而，支持向量回归模型的准确性取决于其参数值，而自回归积分移动平均应用于小数据集时不够稳健。因此，为克服这一问题，采用粒子群优化算法来估计支持向量回归和自回归积分移动平均模型的参数。所提出的混合模型基于经济指标用于预测美国的财产犯罪率。实验结果表明，与单个模型相比，所提出的混合模型能够产生更准确的预测结果。
