Abstract
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.
Highlights
Quantitative forecasting methods are classified into causal and time series models
The results have shown that the use of particle swarm optimization (PSO) on autoregressive integrated moving average (ARIMA) model is capable of improving the performance of the hybrid model PSOSVR ARIMA
This paper proposes a time series model for crime rates forecasting
Summary
Quantitative forecasting methods are classified into causal and time series models. The causal models are based on the relationship between the variable to be forecasted and independent variables. A linear relationship is typically used in the causal models. Regression, econometric models, and inputoutput models are examples of some of the causal models. The time series models are models that use historical data to estimate the future which can be categorized into linear and nonlinear models. Most of the linear models are statistical models such as exponential smoothing, moving average, and autoregressive integrated moving average (ARIMA). The nonlinear models consist of statistical models such as bilinear models, the threshold autoregressive (TAR) models, and autoregressive conditional heteroscedastic (ARCH) as well as nonstatistical models such as artificial neural networks (ANN) and support vector regression (SVR)
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