Malaysian tourism interest forecasting using Nonlinear Auto-Regressive Moving Average (NARMA) model

Tourism is an important industry for many nations including Malaysia. A tourism forecasting model is necessary in order to optimize resource allocation to maximize facilities and services to tourists. In realization of this issue, this paper proposes a comparison between two models (Nonlinear Auto-R...

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Published in:IEEE Symposium on Wireless Technology and Applications, ISWTA
Main Author: Kadir S.N.; Tahir N.M.; Yassin I.M.; Zabidi A.
Format: Conference paper
Language:English
Published: IEEE Computer Society 2014
Online Access:https://www.scopus.com/inward/record.uri?eid=2-s2.0-84920053678&doi=10.1109%2fISWTA.2014.6981186&partnerID=40&md5=7838c8070cee203afa77e7fbea212cb3
id 2-s2.0-84920053678
spelling 2-s2.0-84920053678
Kadir S.N.; Tahir N.M.; Yassin I.M.; Zabidi A.
Malaysian tourism interest forecasting using Nonlinear Auto-Regressive Moving Average (NARMA) model
2014
IEEE Symposium on Wireless Technology and Applications, ISWTA


10.1109/ISWTA.2014.6981186
https://www.scopus.com/inward/record.uri?eid=2-s2.0-84920053678&doi=10.1109%2fISWTA.2014.6981186&partnerID=40&md5=7838c8070cee203afa77e7fbea212cb3
Tourism is an important industry for many nations including Malaysia. A tourism forecasting model is necessary in order to optimize resource allocation to maximize facilities and services to tourists. In realization of this issue, this paper proposes a comparison between two models (Nonlinear Auto-Regressive (NAR) and Nonlinear Auto-Regressive Moving Average (NARMA)) to forecast Malaysian tourism influx based on the volume of internet searches of the keyword 'tourism Malaysia' in Google Trends, based on proven strong correlatedness between the volume of internet searches with tourism in a particular area. Both models were constructed using two-stage Multi-Layer Perceptron (MLP) neural networks. The first stage involves the prediction of the NAR model, while the second stage involves the construction of the Moving Average (MA) part. The resulting NARMA model is a combination of both the MLPs. Results suggest that the NARMA model is more suited to approximate the tourism data due to its relatively better Mean Squared Error (MSE) and fitting results. © 2014 IEEE.
IEEE Computer Society
23247843
English
Conference paper

author Kadir S.N.; Tahir N.M.; Yassin I.M.; Zabidi A.
spellingShingle Kadir S.N.; Tahir N.M.; Yassin I.M.; Zabidi A.
Malaysian tourism interest forecasting using Nonlinear Auto-Regressive Moving Average (NARMA) model
author_facet Kadir S.N.; Tahir N.M.; Yassin I.M.; Zabidi A.
author_sort Kadir S.N.; Tahir N.M.; Yassin I.M.; Zabidi A.
title Malaysian tourism interest forecasting using Nonlinear Auto-Regressive Moving Average (NARMA) model
title_short Malaysian tourism interest forecasting using Nonlinear Auto-Regressive Moving Average (NARMA) model
title_full Malaysian tourism interest forecasting using Nonlinear Auto-Regressive Moving Average (NARMA) model
title_fullStr Malaysian tourism interest forecasting using Nonlinear Auto-Regressive Moving Average (NARMA) model
title_full_unstemmed Malaysian tourism interest forecasting using Nonlinear Auto-Regressive Moving Average (NARMA) model
title_sort Malaysian tourism interest forecasting using Nonlinear Auto-Regressive Moving Average (NARMA) model
publishDate 2014
container_title IEEE Symposium on Wireless Technology and Applications, ISWTA
container_volume
container_issue
doi_str_mv 10.1109/ISWTA.2014.6981186
url https://www.scopus.com/inward/record.uri?eid=2-s2.0-84920053678&doi=10.1109%2fISWTA.2014.6981186&partnerID=40&md5=7838c8070cee203afa77e7fbea212cb3
description Tourism is an important industry for many nations including Malaysia. A tourism forecasting model is necessary in order to optimize resource allocation to maximize facilities and services to tourists. In realization of this issue, this paper proposes a comparison between two models (Nonlinear Auto-Regressive (NAR) and Nonlinear Auto-Regressive Moving Average (NARMA)) to forecast Malaysian tourism influx based on the volume of internet searches of the keyword 'tourism Malaysia' in Google Trends, based on proven strong correlatedness between the volume of internet searches with tourism in a particular area. Both models were constructed using two-stage Multi-Layer Perceptron (MLP) neural networks. The first stage involves the prediction of the NAR model, while the second stage involves the construction of the Moving Average (MA) part. The resulting NARMA model is a combination of both the MLPs. Results suggest that the NARMA model is more suited to approximate the tourism data due to its relatively better Mean Squared Error (MSE) and fitting results. © 2014 IEEE.
publisher IEEE Computer Society
issn 23247843
language English
format Conference paper
accesstype
record_format scopus
collection Scopus
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