Forecasting revenue passenger enplanements using wavelet-support vector machine

Forecasting is an important element in an airline industry due to its capability in projecting airport activities that will reflect the relationship that drives aviation activities. A wavelet-support vector machine (WSVM) conjunction model for revenue passenger enplanements forecast is proposed in t...

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Main Author:	Zainuddin, Mohamad Aiman
Format:	Thesis
Language:	English
Published:	2015
Subjects:	QA Mathematics
Online Access:	http://eprints.utm.my/id/eprint/54631/1/MohamadAimanZainuddinMFS2015.pdf
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id	my-utm-ep.54631
record_format	uketd_dc
spelling	my-utm-ep.546312020-10-21T01:09:54Z Forecasting revenue passenger enplanements using wavelet-support vector machine 2015-05 Zainuddin, Mohamad Aiman QA Mathematics Forecasting is an important element in an airline industry due to its capability in projecting airport activities that will reflect the relationship that drives aviation activities. A wavelet-support vector machine (WSVM) conjunction model for revenue passenger enplanements forecast is proposed in this study. The conjunction model is the combination of two models which are Discrete Wavelet Transform (DWT) and Support Vector Machine (SVM). The method is then compared with single SVM and Seasonal Decomposition-Support Vector Machine (SDSVM) conjunctions. Seasonal Decomposition (SD) readings are obtained through X-12- ARIMA. The monthly domestic and international revenue passenger enplanements data dated from January 1996 to December 2012 are used. The performances of the three models are then compared utilizing mean absolute error (MAE), mean square error (MSE) and mean absolute percentage error (MAPE). The results indicate that WSVM conjunction model has higher accuracy and performs better than both basic single SVM and SDSVM conjunctions. 2015-05 Thesis http://eprints.utm.my/id/eprint/54631/ http://eprints.utm.my/id/eprint/54631/1/MohamadAimanZainuddinMFS2015.pdf application/pdf en public http://dms.library.utm.my:8080/vital/access/manager/Repository/vital:86611 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 Zainuddin, Mohamad Aiman Forecasting revenue passenger enplanements using wavelet-support vector machine
description	Forecasting is an important element in an airline industry due to its capability in projecting airport activities that will reflect the relationship that drives aviation activities. A wavelet-support vector machine (WSVM) conjunction model for revenue passenger enplanements forecast is proposed in this study. The conjunction model is the combination of two models which are Discrete Wavelet Transform (DWT) and Support Vector Machine (SVM). The method is then compared with single SVM and Seasonal Decomposition-Support Vector Machine (SDSVM) conjunctions. Seasonal Decomposition (SD) readings are obtained through X-12- ARIMA. The monthly domestic and international revenue passenger enplanements data dated from January 1996 to December 2012 are used. The performances of the three models are then compared utilizing mean absolute error (MAE), mean square error (MSE) and mean absolute percentage error (MAPE). The results indicate that WSVM conjunction model has higher accuracy and performs better than both basic single SVM and SDSVM conjunctions.
format	Thesis
qualification_level	Master's degree
author	Zainuddin, Mohamad Aiman
author_facet	Zainuddin, Mohamad Aiman
author_sort	Zainuddin, Mohamad Aiman
title	Forecasting revenue passenger enplanements using wavelet-support vector machine
title_short	Forecasting revenue passenger enplanements using wavelet-support vector machine
title_full	Forecasting revenue passenger enplanements using wavelet-support vector machine
title_fullStr	Forecasting revenue passenger enplanements using wavelet-support vector machine
title_full_unstemmed	Forecasting revenue passenger enplanements using wavelet-support vector machine
title_sort	forecasting revenue passenger enplanements using wavelet-support vector machine
granting_institution	Universiti Teknologi Malaysia, Faculty of Science
granting_department	Faculty of Science
publishDate	2015
url	http://eprints.utm.my/id/eprint/54631/1/MohamadAimanZainuddinMFS2015.pdf
_version_	1747817692408053760

Forecasting revenue passenger enplanements using wavelet-support vector machine

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