scholarly journals Economic Indicators Selection for Property Crime Rates using Grey Relational Analysis and Support Vector Regression

2022 ◽  
Vol 16 ◽  
pp. 1-8
Author(s):  
Razana Alwee ◽  
Siti Mariyam Hj Shamsuddin ◽  
Roselina Sallehuddin
Keyword(s):  
Support Vector Regression ◽  
Grey Relational Analysis ◽  
Crime Rate ◽  
Consumer Price Index ◽  
Property Crime ◽  
Economic Indicators ◽  
Support Vector ◽  
Features Selection ◽  
Relational Analysis ◽  
Grey Relational

Features selection is very important in the multivariate models because the accuracy of forecasting results produced by the model are highly dependent on these selected features. The purpose of this study is to propose grey relational analysis and support vector regression for features selection. The features are economic indicators that are used to forecast property crime rate. Grey relational analysis selects the best data series to represent each economic indicator and rank the economic indicators according to its importance to the property crime rate. Next, the support vector regression is used to select the significant economic indicators where particle swarm optimization estimates the parameters of support vector regression. In this study, we use unemployment rate, consumer price index, gross domestic product and consumer sentiment index as the economic indicators, as well as property crime rate for the United States. From our experiments, we found that the gross domestic product, unemployment rate and consumer price index are the most influential economic indicators. The proposed method is also found to produce better forecasting accuracy as compared to multiple linear regressions.

MODELING OF VIOLENT CRIME RATES WITH ECONOMIC INDICATORS USING HYBRIDIZATION OF GREY RELATIONAL ANALYSIS AND SUPPORT VECTOR REGRESSION

2015 ◽  
Vol 76 (1) ◽  
Author(s):  
Razana Alwee ◽  
Siti Mariyam Shamsuddin ◽  
Roselina Sallehuddin
Keyword(s):  
Support Vector Regression ◽  
Hybrid Model ◽  
Violent Crime ◽  
Historical Data ◽  
Crime Rates ◽  
Economic Indicators ◽  
Support Vector ◽  
Relational Analysis ◽  
Proposed Model ◽  
Grey Relational

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.

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

2013 ◽  
Vol 2013 ◽  
pp. 1-11 ◽  
Author(s):  
Razana Alwee ◽  
Siti Mariyam Hj Shamsuddin ◽  
Roselina Sallehuddin
Keyword(s):  
Particle Swarm Optimization ◽  
Support Vector Regression ◽  
Hybrid Model ◽  
Moving Average ◽  
Property Crime ◽  
Crime Rates ◽  
Economic Indicators ◽  
Support Vector ◽  
Swarm Optimization ◽  
Autoregressive Integrated Moving Average

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.

Sign in / Sign up

Export Citation Format

Share Document