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

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 tim...

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Main Author:	Mohd. Zaki, Mohd. Suhaimi
Format:	Thesis
Language:	English
Published:	2014
Subjects:	QA75 Electronic computers Computer science
Online Access:	http://eprints.utm.my/id/eprint/50712/25/MohdSuhaimiMohdZakiMFC2014.pdf
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id	my-utm-ep.50712
record_format	uketd_dc
spelling	my-utm-ep.507122020-07-09T01:11:16Z A hybrid approach based on arima and artificial neural networks for crime series forecasting 2014-09 Mohd. Zaki, Mohd. Suhaimi QA75 Electronic computers. Computer science 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. 2014-09 Thesis http://eprints.utm.my/id/eprint/50712/ http://eprints.utm.my/id/eprint/50712/25/MohdSuhaimiMohdZakiMFC2014.pdf application/pdf en public http://dms.library.utm.my:8080/vital/access/manager/Repository/vital:89320 masters Universiti Teknologi Malaysia, Faculty of Computing Faculty of Computing
institution	Universiti Teknologi Malaysia
collection	UTM Institutional Repository
language	English
topic	QA75 Electronic computers Computer science
spellingShingle	QA75 Electronic computers Computer science Mohd. Zaki, Mohd. Suhaimi A hybrid approach based on arima and artificial neural networks for crime series forecasting
description	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.
format	Thesis
qualification_level	Master's degree
author	Mohd. Zaki, Mohd. Suhaimi
author_facet	Mohd. Zaki, Mohd. Suhaimi
author_sort	Mohd. Zaki, Mohd. Suhaimi
title	A hybrid approach based on arima and artificial neural networks for crime series forecasting
title_short	A hybrid approach based on arima and artificial neural networks for crime series forecasting
title_full	A hybrid approach based on arima and artificial neural networks for crime series forecasting
title_fullStr	A hybrid approach based on arima and artificial neural networks for crime series forecasting
title_full_unstemmed	A hybrid approach based on arima and artificial neural networks for crime series forecasting
title_sort	hybrid approach based on arima and artificial neural networks for crime series forecasting
granting_institution	Universiti Teknologi Malaysia, Faculty of Computing
granting_department	Faculty of Computing
publishDate	2014
url	http://eprints.utm.my/id/eprint/50712/25/MohdSuhaimiMohdZakiMFC2014.pdf
_version_	1747817518055030784

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

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