Modeling of Violent Crime Rates With Economic Indicators Using Hybridization of Grey Relational Analysis and Support Vector Regression

Abstract

Regression and econometric models are commonly applied in modeling of violent crime rates. However, these models are mainly linear and only capable in modeling linear relationships. Moreover, the econometric models are quite complex to develop. Although time series model is a promising alternative tool, limited historical data of crime rates makes the standard time series models less suitable for modeling the violent crime rates. Thus, in this study, a hybrid model that can handle limited historical data is proposed for modeling the violent crime rates. The proposed hybrid model combines grey relational analysis and support vector regression. Since inaccurate parameters setting leads to inaccuracy of support vector regression model, particle swarm optimization is used to increase the accuracy of the model. The proposed hybrid model is used to model the violent crime rates of United State based on economic indicators. The proposed model also has additional features such as able to choose the data series for economic indicators and significant economic indicators for the violent crime rates. The experimental results showed that the proposed model produces more accurate forecast as compared to multiple linear regression in forecasting the violent crime rates.

Modelling Stock Market Exchange by Autoregressive Integrated Moving Average, Multiple Linear Regression and Neural Network

“…Soft computing techniques has been said to improve the forecast [18]. Neural networks have been proposed as an alternative model in time series forecasting because they are versatile in collecting nonlinear time series data [19].…”