Hybrid Support Vector Regression and Autoregressive Integrated Moving Average Models Improved by Particle Swarm Optimization for Property Crime Rates Forecasting with Economic Indicators

被引:27
作者
Alwee, Razana [1 ]
Shamsuddin, Siti Mariyam Hj [1 ]
Sallehuddin, Roselina [1 ]
机构
[1] Univ Teknol Malaysia, Fac Comp, Soft Comp Res Grp, Skudai 81310, Johor, Malaysia
关键词
ARTIFICIAL NEURAL-NETWORKS; TIME-SERIES; UNEMPLOYMENT; ARIMA; PREDICTION; SVR; ALGORITHM; MACHINES; ENGLAND;
D O I
10.1155/2013/951475
中图分类号
O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];
学科分类号
07 ; 0710 ; 09 ;
摘要
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.
引用
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页数:11
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