Outlier treatments using interolation on Malaysia tourist arrival forecasting: SARIMA and ANN approaches

Outliers are unusual observations that appear in a piece of data that are very different from the rest of the data. The presence of an outlier may directly affect the variance, the model parameters, and the overall estimation, especially during forecasting. To obtain an accurate forecast, any...

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Main Author:	Wahir, Norsoraya Azurin
Format:	Thesis
Language:	English English English
Published:	2020
Subjects:	HD28-70 Management Industrial Management
Online Access:	http://eprints.uthm.edu.my/1095/1/24p%20NORSORAYA%20AZURIN%20WAHIR.pdf http://eprints.uthm.edu.my/1095/2/NORSORAYA%20AZURIN%20WAHIR%20COPYRIGHT%20DECLARATION.pdf http://eprints.uthm.edu.my/1095/3/NORSORAYA%20AZURIN%20WAHIR%20WATERMARK.pdf
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spelling	my-uthm-ep.10952021-09-21T06:14:08Z Outlier treatments using interolation on Malaysia tourist arrival forecasting: SARIMA and ANN approaches 2020 Wahir, Norsoraya Azurin HD28-70 Management. Industrial Management Outliers are unusual observations that appear in a piece of data that are very different from the rest of the data. The presence of an outlier may directly affect the variance, the model parameters, and the overall estimation, especially during forecasting. To obtain an accurate forecast, any outliers that are present in the data must be addressed. This research used monthly Malaysia tourist arrivals from 1998 until 2015 and an ARIMA outlier detection method to detect outliers on original data. The detected outliers were regarded as missing values then treated using interpolation method which are Linear Interpolation and Cubic Spline Interpolation methods. In this study, SARIMA model and Artificial Neural Network model were used as forecasting tools using the data before and after outlier treatment. The comparison of forecast performance between all models were calculated using MSE, MAD, MAPE and R2 including the data before and after outlier treatment. This study found that once the outlier in the data was treated, ANN model of Cubic Spline Interpolation performs the best models compare to other models which is 95.65% using R2 validation test. On the other hand, ANN approach outperforms SARIMA approach on both data for before and after outlier treatment which are 6.05% and 2.52%. 2020 Thesis http://eprints.uthm.edu.my/1095/ http://eprints.uthm.edu.my/1095/1/24p%20NORSORAYA%20AZURIN%20WAHIR.pdf text en public http://eprints.uthm.edu.my/1095/2/NORSORAYA%20AZURIN%20WAHIR%20COPYRIGHT%20DECLARATION.pdf text en staffonly http://eprints.uthm.edu.my/1095/3/NORSORAYA%20AZURIN%20WAHIR%20WATERMARK.pdf text en validuser mphil masters Universiti Tun Hussein Onn Malaysia Fakulti Sains Gunaan dan Teknologi
institution	Universiti Tun Hussein Onn Malaysia
collection	UTHM Institutional Repository
language	English English English
topic	HD28-70 Management Industrial Management
spellingShingle	HD28-70 Management Industrial Management Wahir, Norsoraya Azurin Outlier treatments using interolation on Malaysia tourist arrival forecasting: SARIMA and ANN approaches
description	Outliers are unusual observations that appear in a piece of data that are very different from the rest of the data. The presence of an outlier may directly affect the variance, the model parameters, and the overall estimation, especially during forecasting. To obtain an accurate forecast, any outliers that are present in the data must be addressed. This research used monthly Malaysia tourist arrivals from 1998 until 2015 and an ARIMA outlier detection method to detect outliers on original data. The detected outliers were regarded as missing values then treated using interpolation method which are Linear Interpolation and Cubic Spline Interpolation methods. In this study, SARIMA model and Artificial Neural Network model were used as forecasting tools using the data before and after outlier treatment. The comparison of forecast performance between all models were calculated using MSE, MAD, MAPE and R2 including the data before and after outlier treatment. This study found that once the outlier in the data was treated, ANN model of Cubic Spline Interpolation performs the best models compare to other models which is 95.65% using R2 validation test. On the other hand, ANN approach outperforms SARIMA approach on both data for before and after outlier treatment which are 6.05% and 2.52%.
format	Thesis
qualification_name	Master of Philosophy (M.Phil.)
qualification_level	Master's degree
author	Wahir, Norsoraya Azurin
author_facet	Wahir, Norsoraya Azurin
author_sort	Wahir, Norsoraya Azurin
title	Outlier treatments using interolation on Malaysia tourist arrival forecasting: SARIMA and ANN approaches
title_short	Outlier treatments using interolation on Malaysia tourist arrival forecasting: SARIMA and ANN approaches
title_full	Outlier treatments using interolation on Malaysia tourist arrival forecasting: SARIMA and ANN approaches
title_fullStr	Outlier treatments using interolation on Malaysia tourist arrival forecasting: SARIMA and ANN approaches
title_full_unstemmed	Outlier treatments using interolation on Malaysia tourist arrival forecasting: SARIMA and ANN approaches
title_sort	outlier treatments using interolation on malaysia tourist arrival forecasting: sarima and ann approaches
granting_institution	Universiti Tun Hussein Onn Malaysia
granting_department	Fakulti Sains Gunaan dan Teknologi
publishDate	2020
url	http://eprints.uthm.edu.my/1095/1/24p%20NORSORAYA%20AZURIN%20WAHIR.pdf http://eprints.uthm.edu.my/1095/2/NORSORAYA%20AZURIN%20WAHIR%20COPYRIGHT%20DECLARATION.pdf http://eprints.uthm.edu.my/1095/3/NORSORAYA%20AZURIN%20WAHIR%20WATERMARK.pdf
_version_	1747830736830857216

Outlier treatments using interolation on Malaysia tourist arrival forecasting: SARIMA and ANN approaches

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