Implementation of genetic algorithm in model identification of box-jenkins methodology

During the past several decades, a considerable amount of studies have been carried out on time series and in particular the Box-Jenkins (BJ) method. As with all techniques of statistical analysis, the conclusions of time series analysis are critically dependent on the assumptions underlying the ana...

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Main Author:	Md. Maarof, Mohd. Zulariffin
Format:	Thesis
Language:	English
Published:	2013
Subjects:	QA Mathematics
Online Access:	http://eprints.utm.my/id/eprint/47921/25/MohdZulariffinMdMaarofMFS2013.pdf
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spelling	my-utm-ep.479212017-07-06T01:49:47Z Implementation of genetic algorithm in model identification of box-jenkins methodology 2013-07 Md. Maarof, Mohd. Zulariffin QA Mathematics During the past several decades, a considerable amount of studies have been carried out on time series and in particular the Box-Jenkins (BJ) method. As with all techniques of statistical analysis, the conclusions of time series analysis are critically dependent on the assumptions underlying the analysis and BJ is a commonly used forecasting method that can yield highly accurate forecasts for certain types of data. Genetic Algorithm (GA) is a heuristic method of optimization. This study presents the study on developing an extrapolative BJ model with the use of GA method to produce forecasting models using time series data. BJ method has a cycle of four phases, the data transformation phase for model identification, parameter estimation, model diagnostic checking or validation, and finally producing the forecast. Although many researchers and practitioners have concentrated in the parameter estimation part of BJ model, the most crucial stage in building the model is in the data transformation and model identification where any false identification will lead to assuming a wrong model and will increase in the cost of reidentification. Hence, using GA a subset of artificial intelligence methods was introduced into the process of BJ to solve the problem in the model identification and parameter estimation phase. The data used in this study are the monthly data of international tourists arrival into Malaysia from 1990 to 2011. This is a case study in the implementation of GA-BJ model. The result from this study may be divided into two main parts, namely the result for the in-sample data (fitted model) and outsample data (forecast model). The analysis shows that the out-sample values using GA-BJ model gives better forecast accuracy than the out-sample values for BJ model. This shows that the combination of BJ and GA methods gives a more accurate model than using a single method for forecasting. This study concludes that GA method can be an alternative way in identifying the right order of component in BJ model. 2013-07 Thesis http://eprints.utm.my/id/eprint/47921/ http://eprints.utm.my/id/eprint/47921/25/MohdZulariffinMdMaarofMFS2013.pdf application/pdf en public masters Universiti Teknologi Malaysia, Faculty of Science Faculty of Science
institution	Universiti Teknologi Malaysia
collection	UTM Institutional Repository
language	English
topic	QA Mathematics
spellingShingle	QA Mathematics Md. Maarof, Mohd. Zulariffin Implementation of genetic algorithm in model identification of box-jenkins methodology
description	During the past several decades, a considerable amount of studies have been carried out on time series and in particular the Box-Jenkins (BJ) method. As with all techniques of statistical analysis, the conclusions of time series analysis are critically dependent on the assumptions underlying the analysis and BJ is a commonly used forecasting method that can yield highly accurate forecasts for certain types of data. Genetic Algorithm (GA) is a heuristic method of optimization. This study presents the study on developing an extrapolative BJ model with the use of GA method to produce forecasting models using time series data. BJ method has a cycle of four phases, the data transformation phase for model identification, parameter estimation, model diagnostic checking or validation, and finally producing the forecast. Although many researchers and practitioners have concentrated in the parameter estimation part of BJ model, the most crucial stage in building the model is in the data transformation and model identification where any false identification will lead to assuming a wrong model and will increase in the cost of reidentification. Hence, using GA a subset of artificial intelligence methods was introduced into the process of BJ to solve the problem in the model identification and parameter estimation phase. The data used in this study are the monthly data of international tourists arrival into Malaysia from 1990 to 2011. This is a case study in the implementation of GA-BJ model. The result from this study may be divided into two main parts, namely the result for the in-sample data (fitted model) and outsample data (forecast model). The analysis shows that the out-sample values using GA-BJ model gives better forecast accuracy than the out-sample values for BJ model. This shows that the combination of BJ and GA methods gives a more accurate model than using a single method for forecasting. This study concludes that GA method can be an alternative way in identifying the right order of component in BJ model.
format	Thesis
qualification_level	Master's degree
author	Md. Maarof, Mohd. Zulariffin
author_facet	Md. Maarof, Mohd. Zulariffin
author_sort	Md. Maarof, Mohd. Zulariffin
title	Implementation of genetic algorithm in model identification of box-jenkins methodology
title_short	Implementation of genetic algorithm in model identification of box-jenkins methodology
title_full	Implementation of genetic algorithm in model identification of box-jenkins methodology
title_fullStr	Implementation of genetic algorithm in model identification of box-jenkins methodology
title_full_unstemmed	Implementation of genetic algorithm in model identification of box-jenkins methodology
title_sort	implementation of genetic algorithm in model identification of box-jenkins methodology
granting_institution	Universiti Teknologi Malaysia, Faculty of Science
granting_department	Faculty of Science
publishDate	2013
url	http://eprints.utm.my/id/eprint/47921/25/MohdZulariffinMdMaarofMFS2013.pdf
_version_	1747817264037494784

Implementation of genetic algorithm in model identification of box-jenkins methodology

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