A hybrid approach based on arima and artificial neural networks for crime series forecasting

Abstract

Crime forecasting is an interesting application area of research with ARIMA and ANN models offer a good technique for predicting time series. Time series data often contain both linear and nonlinear patterns. Therefore, neither ARIMA nor neural networks can be adequate in modeling and predicting time series data. In this study, a hybrid ARIMA and neural network model is proposed to predict crime series data. The hybrid approach for the crime series prediction is tested using 216-month observations of four crime category that are Non-Domestic Violence Related Assault, Break and Enter Non Dwelling, Steal from Retail Store and Steal from Person. Specifically, the results from the hybrid model provide a good modeling framework capable of capturing the nonlinear nature of the complex time series and thus producing more accurate predictions. The accuracy results from the hybrid models for the four case studies are 92.08%, 91.78%, 93.62 and 94.13%, respectively, which are satisfactory in common model applications. Predicted crime data from the hybrid model are compared with those from the ARIMA and neural network using the performance measures. As the result, the hybrid model provides a better accuracy over the ARIMA and neural network models for crime series forecasting.