Improved mutual information method in combination model selection for forecasting tourist arrival

During the past several decades, a considerable amount of studies has been carried out on finding the highest accurate forecast model. Recently, it has been demonstrated that combining forecasts of individual models can improve forecast performance. Nevertheless, in practice, selecting individual fo...

Full description

Saved in:

Bibliographic Details
Main Author:	Md. Maarof, Mohd. Zulariffin
Format:	Thesis
Language:	English
Published:	2019
Subjects:	QA Mathematics
Online Access:	http://eprints.utm.my/id/eprint/81581/1/MohdZulariffinMdPFS2019.pdf
Tags:	Add Tag No Tags, Be the first to tag this record!

id	my-utm-ep.81581
record_format	uketd_dc
spelling	my-utm-ep.815812019-09-10T01:41:12Z Improved mutual information method in combination model selection for forecasting tourist arrival 2019 Md. Maarof, Mohd. Zulariffin QA Mathematics During the past several decades, a considerable amount of studies has been carried out on finding the highest accurate forecast model. Recently, it has been demonstrated that combining forecasts of individual models can improve forecast performance. Nevertheless, in practice, selecting individual forecast for model combination based on forecast accuracy evaluation might not have extracted all the significant information for the actual output forecast values. Hence, it is advocated to select the optimal individual model from theoretical and experimental aspects that may be able to offer more information to provide a better prediction of combination forecast model. Thus, the mutual information algorithm scaling proposed (MI-S-P) approach is proposed in this study to select the optimal individual model as an input for combination forecast model. Seven individual models and three linear combination methods are applied in this study to evaluate the effectiveness of the MI-S-P approach. The data used in this study is a short term 12 months ahead forecast which includes the monthly data on the top five international tourists arrival entering into Malaysia from the year 2000 to 2013. The results from this study is divided into two main parts, namely in-sample data (fitted model) and out-sample data (forecast model). The analyses show that the in-sample and out-sample values using MI-S-P model has successfully improve forecast accuracy on average by 2% compared to using all of individual forecast combination models. This study concludes that MI-S-P approach can be an alternative way in identifying the right optimal individual model for modelling combination forecast model. 2019 Thesis http://eprints.utm.my/id/eprint/81581/ http://eprints.utm.my/id/eprint/81581/1/MohdZulariffinMdPFS2019.pdf application/pdf en public phd doctoral Universiti Teknologi Malaysia Mathematics
institution	Universiti Teknologi Malaysia
collection	UTM Institutional Repository
language	English
topic	QA Mathematics
spellingShingle	QA Mathematics Md. Maarof, Mohd. Zulariffin Improved mutual information method in combination model selection for forecasting tourist arrival
description	During the past several decades, a considerable amount of studies has been carried out on finding the highest accurate forecast model. Recently, it has been demonstrated that combining forecasts of individual models can improve forecast performance. Nevertheless, in practice, selecting individual forecast for model combination based on forecast accuracy evaluation might not have extracted all the significant information for the actual output forecast values. Hence, it is advocated to select the optimal individual model from theoretical and experimental aspects that may be able to offer more information to provide a better prediction of combination forecast model. Thus, the mutual information algorithm scaling proposed (MI-S-P) approach is proposed in this study to select the optimal individual model as an input for combination forecast model. Seven individual models and three linear combination methods are applied in this study to evaluate the effectiveness of the MI-S-P approach. The data used in this study is a short term 12 months ahead forecast which includes the monthly data on the top five international tourists arrival entering into Malaysia from the year 2000 to 2013. The results from this study is divided into two main parts, namely in-sample data (fitted model) and out-sample data (forecast model). The analyses show that the in-sample and out-sample values using MI-S-P model has successfully improve forecast accuracy on average by 2% compared to using all of individual forecast combination models. This study concludes that MI-S-P approach can be an alternative way in identifying the right optimal individual model for modelling combination forecast model.
format	Thesis
qualification_name	Doctor of Philosophy (PhD.)
qualification_level	Doctorate
author	Md. Maarof, Mohd. Zulariffin
author_facet	Md. Maarof, Mohd. Zulariffin
author_sort	Md. Maarof, Mohd. Zulariffin
title	Improved mutual information method in combination model selection for forecasting tourist arrival
title_short	Improved mutual information method in combination model selection for forecasting tourist arrival
title_full	Improved mutual information method in combination model selection for forecasting tourist arrival
title_fullStr	Improved mutual information method in combination model selection for forecasting tourist arrival
title_full_unstemmed	Improved mutual information method in combination model selection for forecasting tourist arrival
title_sort	improved mutual information method in combination model selection for forecasting tourist arrival
granting_institution	Universiti Teknologi Malaysia
granting_department	Mathematics
publishDate	2019
url	http://eprints.utm.my/id/eprint/81581/1/MohdZulariffinMdPFS2019.pdf
_version_	1747818363461042176

Improved mutual information method in combination model selection for forecasting tourist arrival

Similar Items