{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,17]],"date-time":"2025-10-17T13:51:37Z","timestamp":1760709097067,"version":"3.41.2"},"reference-count":43,"publisher":"Emerald","issue":"6","license":[{"start":{"date-parts":[[2016,7,11]],"date-time":"2016-07-11T00:00:00Z","timestamp":1468195200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.emerald.com\/insight\/site-policies"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2016,7,11]]},"abstract":"<jats:sec>\n               <jats:title content-type=\"abstract-heading\">Purpose<\/jats:title>\n               <jats:p> \u2013 Accurate forecast of tourist arrivals is crucial for Thailand since the tourism industry is a major economic factor of the country. However, a nonstationarity, normally consisted in nonlinear tourism time series can seriously ruin the forecasting computation. The purpose of this paper is to propose a hybrid forecasting method, namely discrete wavelet decomposition (DWD)-NARX, which combines DWD and the nonlinear autoregressive neural network with exogenous input (NARX) to cope with such nonstationarity, as a consequence, improve the effectiveness of the demand-side management activities. <\/jats:p>\n            <\/jats:sec>\n            <jats:sec>\n               <jats:title content-type=\"abstract-heading\">Design\/methodology\/approach<\/jats:title>\n               <jats:p> \u2013 According to DWD-NARX, wavelet decomposition is executed for efficiently extracting the hidden significant, temporal features contained in the nonstationary time series. Then, each extracted feature set at a particular resolution level along with a relative price as an exogenous input factor are fed into NARX for further forecasting. Finally, the forecasting results are reconstructed. Forecasting performance measures rely on mean absolute percentage error, mean absolute error as well as mean square error. Model overfitting avoidance is also considered. <\/jats:p>\n            <\/jats:sec>\n            <jats:sec>\n               <jats:title content-type=\"abstract-heading\">Findings<\/jats:title>\n               <jats:p> \u2013 The results indicate the superiority of the DWD-NARX over other efficient related neural forecasters in the cases of high forecasting performance rate as well as competently coping with model overfitting. <\/jats:p>\n            <\/jats:sec>\n            <jats:sec>\n               <jats:title content-type=\"abstract-heading\">Research limitations\/implications<\/jats:title>\n               <jats:p> \u2013 The scope of this study is confined to Thailand tourist arrivals forecast based on short-term projection. To resolve such limitations, future research should aim to apply the generalization capability of DWD-NARX on other domains of managerial time series forecast under long-term projection environment. However, the exogenous input factor is to be empirically revised on domain-by-domain basis. <\/jats:p>\n            <\/jats:sec>\n            <jats:sec>\n               <jats:title content-type=\"abstract-heading\">Originality\/value<\/jats:title>\n               <jats:p> \u2013 Few works have been implemented either to handle the nonstationarity, consisted in nonlinear, unpredictable time series, or to achieve great success on finding an appropriate and effective exogenous forecasting input. This study applies DWD to attain efficient feature extraction; then, utilizes the competent forecaster, NARX. This would comprehensively and specifically deal with the nonstationarity difficulties at once. In addition, this study finds the effectiveness of simply using a relative price, generated based on six top-ranked original tourist countries as an exogenous forecasting input.<\/jats:p>\n            <\/jats:sec>","DOI":"10.1108\/imds-11-2015-0463","type":"journal-article","created":{"date-parts":[[2016,6,29]],"date-time":"2016-06-29T14:48:49Z","timestamp":1467211729000},"page":"1242-1258","source":"Crossref","is-referenced-by-count":15,"title":["Thailand tourism forecasting based on a hybrid of discrete wavelet decomposition and NARX neural network"],"prefix":"10.1108","volume":"116","author":[{"given":"Ratree","family":"Kummong","sequence":"first","affiliation":[]},{"given":"Siriporn","family":"Supratid","sequence":"additional","affiliation":[]}],"member":"140","reference":[{"key":"key2020121420141099300_b1","doi-asserted-by":"crossref","unstructured":"Aiken, M.\n                (1999), \u201cUsing a neural network to forecast inflation\u201d, \n                  Industrial Management & Data Systems\n               , Vol. 99 No. 7, pp. 296-301.","DOI":"10.1108\/02635579910291984"},{"key":"key2020121420141099300_b2","doi-asserted-by":"crossref","unstructured":"Amjady, N.\n                and \n                  Keynia, F.\n                (2009), \u201cShort-term load forecasting of power systems by combination of wavelet transform and neuro-evolutionary algorithm\u201d, \n                  Energy\n               , Vol. 34 No. 1, pp. 46-57.","DOI":"10.1016\/j.energy.2008.09.020"},{"key":"key2020121420141099300_b3","doi-asserted-by":"crossref","unstructured":"Babu, C.N.\n                and \n                  Reddy, B.E.\n                (2015), \u201cPrediction of selected Indian stock using a partitioning-interpolation based ARIMA-GARCH model\u201d, \n                  Applied Computing and Informatics\n               , Vol. 11 No. 2, pp. 130-143.","DOI":"10.1016\/j.aci.2014.09.002"},{"key":"key2020121420141099300_b4","unstructured":"Babu, N.R.\n                and \n                  Pachiyappan, A.\n                (2014), \u201cDynamic neural network based very short-term wind speed forecasting\u201d, \n                  Wind Engineering\n               , Vol. 38 No. 2, pp. 120-128."},{"key":"key2020121420141099300_b5","doi-asserted-by":"crossref","unstructured":"Chang, F.-J.\n               , \n                  Chen, P.-A.\n               , \n                  Lu, Y.-R.\n               , \n                  Huang, E.\n                and \n                  Chang, K.-Y.\n                (2014), \u201cReal-time multi-step-ahead water level forecasting by recurrent neural networks for urban flood control\u201d, \n                  Journal of Hydrology\n               , Vol. 517, September, pp. 836-846.","DOI":"10.1016\/j.jhydrol.2014.06.013"},{"key":"key2020121420141099300_b6","doi-asserted-by":"crossref","unstructured":"Chen, S.\n               , \n                  Billings, S.A.\n                and \n                  Grant, P.M.\n                (1990), \u201cNon-linear system identification using neural networks\u201d, \n                  International Journal of Control\n               , Vol. 51 No. 6, pp. 1191-1214.","DOI":"10.1080\/00207179008934126"},{"key":"key2020121420141099300_b7","doi-asserted-by":"crossref","unstructured":"Claveria, O.\n                and \n                  Torra, S.\n                (2014), \u201cForecasting tourism demand to Catalonia: neural networks vs time series models\u201d, \n                  Economic Modelling\n               , Vol. 36, January, pp. 220-228.","DOI":"10.1016\/j.econmod.2013.09.024"},{"key":"key2020121420141099300_b8","doi-asserted-by":"crossref","unstructured":"\u00c7oruh, S.\n               , \n                  Geyik\u00e7i, F.\n               , \n                  K\u0131l\u0131\u00e7, E.\n                and \n                  \u00c7oruh, U.\n                (2014), \u201cThe use of NARX neural network for modeling of adsorption of zinc ions using activated almond shell as a potential biosorbent\u201d, \n                  Bioresource Technology\n               , Vol. 151, January, pp. 406-410.","DOI":"10.1016\/j.biortech.2013.10.019"},{"key":"key2020121420141099300_b9","unstructured":"\u00c7uhadar, M.\n               , \n                  Cogurcu, I.\n                and \n                  Kukrer, C.\n                (2014), \u201cModelling and forecasting cruise tourism demand to Izmir by different artificial neural\u201d, \n                  International Journal of Business and Social Research\n               , Vol. 4 No. 3, pp. 12-28."},{"key":"key2020121420141099300_b10","unstructured":"DiscoveryThailand\n                (2015), \u201cTravel guide Thailand destinations\u201d, available at: www.discoverythailand.com\/destination.asp (accessed April 16, 2015)."},{"key":"key2020121420141099300_b11","doi-asserted-by":"crossref","unstructured":"Elman, J.L.\n                (1990), \u201cFinding structure time\u201d, \n                  Cognitive Science\n               , Vol. 14 No. 2, pp. 179-221.","DOI":"10.1207\/s15516709cog1402_1"},{"key":"key2020121420141099300_b12","doi-asserted-by":"crossref","unstructured":"El-Shafie, A.\n               , \n                  Noureldin, A.\n               , \n                  Taha, M.\n               , \n                  Hussain, A.\n                and \n                  Mukhlisin, M.\n                (2012), \u201cDynamic versus static neural network model for rainfall forecasting at Klang River Basin, Malaysia\u201d, \n                  Hydrology and Earth System Sciences\n               , Vol. 16 No. 4, pp. 1151-1169.","DOI":"10.5194\/hess-16-1151-2012"},{"key":"key2020121420141099300_b13","unstructured":"Fourier, J.B.\n                (1822), \n                  Th\u00e9orie Analytique de la Chaleur\n               , Chez Firmin Didot, p\u00e8re et fils, Paris."},{"key":"key2020121420141099300_b14","unstructured":"FRED\n                (2015), \u201cMain economic indicators\u201d, available at: http:\/\/research.stlouisfed.org\/fred2\/release?rid=205 (accessed January 23, 2015)."},{"key":"key2020121420141099300_b15","unstructured":"Gabor, D.\n                (1946), \u201cTheory of communication\u201d, \n                  Journal of the Institute of Electrical Engineers\n               , Vol. 93 No. 26, pp. 429-457."},{"key":"key2020121420141099300_b16","unstructured":"HikerBays\n                (n.d.), \u201cThailand tourist board and the most important informations for travelers\u201d, available at: http:\/\/hikersbay.com\/asia\/thailand?gclid=CJmrqJaH-8QCFRcOjgodGZEA_w (accessed April 16, 2015)."},{"key":"key2020121420141099300_b17","unstructured":"Horne, B.G.\n                and \n                  Giles, C.L.\n                (1995), \u201cAn experimental comparison of recurrent neural networks\u201d, \n                   Proceedings of the Conference Neural Information Processing Systems 1994, MIT Press, Denver\n               , pp. 697-704."},{"key":"key2020121420141099300_b18","doi-asserted-by":"crossref","unstructured":"Hu, Y.\n               , \n                  Xie, R.\n                and \n                  Zhang, W.\n                (2012), \u201cTourists flow prediction by clustering-based GRNN\u201d, \n                  Proceedings of the 9th International Forum on Digital TV and Wireless Multimedia Communication in Shanghai, Springer, Chennai, November 9-10\n               , pp. 396-402.","DOI":"10.1007\/978-3-642-34595-1_54"},{"key":"key2020121420141099300_b19","unstructured":"Hyndman, R.J.\n                (2010), \u201cWhy every statistician should know about cross-validation\u201d, available at: http:\/\/robjhyndman.com\/hyndsight\/crossvalidation\/ (accessed August 23, 2015)."},{"key":"key2020121420141099300_b20","doi-asserted-by":"crossref","unstructured":"Kao, L.-J.\n               , \n                  Chiu, C.-C.\n               , \n                  Lu, C.-J.\n                and \n                  Chang, C.-H.\n                (2013), \u201cA hybrid approach by integrating wavelet-based feature extraction with MARS and SVR for stock index forecasting\u201d, \n                  Decision Support Systems\n               , Vol. 54 No. 3, pp. 1228-1244.","DOI":"10.1016\/j.dss.2012.11.012"},{"key":"key2020121420141099300_b21","doi-asserted-by":"crossref","unstructured":"Leontaritis, I.J.\n                and \n                  Billings, S.A.\n                (1985), \u201cInput-output parametric models for non-linear systems part I: deterministic non-linear systems\u201d, \n                  International Journal of Control\n               , Vol. 41 No. 2, pp. 303-328.","DOI":"10.1080\/0020718508961129"},{"key":"key2020121420141099300_b22","doi-asserted-by":"crossref","unstructured":"Lim, C.\n                and \n                  McAleer, M.\n                (2002), \u201cTime series forecasts of international travel demand for Australia\u201d, \n                  Tourism Management\n               , Vol. 23 No. 4, pp. 389-396.","DOI":"10.1016\/S0261-5177(01)00098-X"},{"key":"key2020121420141099300_b23","doi-asserted-by":"crossref","unstructured":"Mallat, S.G.\n                (1989), \u201cA theory for multiresolution signal decomposition: the wavelet representation\u201d, \n                  IEEE Transactions on Pattern Recognition and Machine Intelligence\n               , Vol. 11 No. 7, pp. 674-693.","DOI":"10.1109\/34.192463"},{"key":"key2020121420141099300_b24","unstructured":"Ministry of Tourism and Sports,Thailand\n                (2014), \u201cStatistic tourist\u201d, available at: www.tourism.go.th\/home\/listcontent (accessed March 1, 2014)."},{"key":"key2020121420141099300_b25","doi-asserted-by":"crossref","unstructured":"Mitrea, C.A.\n               , \n                  Lee, K.M.\n                and \n                  Wu, Z.\n                (2009), \u201cA comparison between neural networks and traditional forecasting methods: a case study\u201d, \n                  International Journal of Engineering Business Management\n               , Vol. 1 No. 2, pp. 19-24.","DOI":"10.5772\/6777"},{"key":"key2020121420141099300_b26","doi-asserted-by":"crossref","unstructured":"Morris, S.A.\n               , \n                  Greer, T.H.\n               , \n                  Hughes, C.\n                and \n                  Clark, W.J.\n                (2004), \u201cPrediction of CASE adoption: a neural network approach\u201d, \n                  Industrial Management & Data Systems\n               , Vol. 104 No. 2, pp. 129-135.","DOI":"10.1108\/02635570410522099"},{"key":"key2020121420141099300_b27","doi-asserted-by":"crossref","unstructured":"Narendra, K.S.\n                and \n                  Parthasarathy, K.\n                (1990), \u201cIdentification and control of dynamical systems using neural networks\u201d, \n                  IEEE Transactions\n               , Vol. 1 No. 1, pp. 4-27.","DOI":"10.1109\/72.80202"},{"key":"key2020121420141099300_b28","doi-asserted-by":"crossref","unstructured":"Norgaard, M.\n               , \n                  Ravn, O.\n               , \n                  Poulsen, N.\n                and \n                  Hansen, L.\n                (2000), \n                  Neural Networks for Modelling and Control of Dynamic Systems\n               , Springer, Berlin.","DOI":"10.1007\/978-1-4471-0453-7"},{"key":"key2020121420141099300_b29","unstructured":"OANDA\n                (2015), \u201cAverage exchange rates\u201d, available at: www.oanda.com\/currency\/average (accessed January 23, 2015)."},{"key":"key2020121420141099300_b30","doi-asserted-by":"crossref","unstructured":"Petropoulos, F.\n               , \n                  Nikolopoulos, K.\n               , \n                  Spithourakis, G.P.\n                and \n                  Assimakopoulos, V.\n                (2013), \u201cEmpirical heuristics for improving intermittent demand forecasting\u201d, \n                  Industrial Management & Data Systems\n               , Vol. 113 No. 5, pp. 683-696.","DOI":"10.1108\/02635571311324142"},{"key":"key2020121420141099300_b31","unstructured":"Quan, K.\n                (2013), \u201cAnd the world\u2019s no. 1 tourist destination is \u2026 Bangkok is now the most-visited city in world\u201d, available at: http:\/\/newsfeed.time.com\/2013\/06\/01\/bangkok-claims-the-worlds-no-1-tourist-destination-title\/ (accessed April 16, 2015)."},{"key":"key2020121420141099300_b32","doi-asserted-by":"crossref","unstructured":"Rumelhart, D.E.\n                and \n                  McClelland, J.L.\n               , \n                  Corporate PDP Research Group\n                (1986), \n                  Parallel Distributed Processing: Explorations in the Microstructure of Cognition\n               , Vol. 1, MIT Press, Cambridge MA.","DOI":"10.7551\/mitpress\/5236.001.0001"},{"key":"key2020121420141099300_b33","doi-asserted-by":"crossref","unstructured":"Sheremetov, L.\n               , \n                  Cosultchi, A.\n               , \n                  Mart\u00ednez-Mu\u00f1oz, J.\n               , \n                  Gonzalez-S\u00e1nchez, A.\n                and \n                  Jim\u00e9nez-Aquino, M.\n                (2014), \u201cData-driven forecasting of naturally fractured reservoirs based on nonlinear autoregressive neural networks with exogenous input\u201d, \n                  Journal of Petroleum Science and Engineering\n               , Vol. 123 No. SI, pp. 106-119.","DOI":"10.1016\/j.petrol.2014.07.013"},{"key":"key2020121420141099300_b34","doi-asserted-by":"crossref","unstructured":"Song, H.\n               , \n                  Wong, K.K.\n                and \n                  Chon, K.K.\n                (2003), \u201cModelling and forecasting the demand for Hong Kong tourism\u201d, \n                  International Journal of Hospitality Management\n               , Vol. 22 No. 4, pp. 435-451.","DOI":"10.1016\/S0278-4319(03)00047-1"},{"key":"key2020121420141099300_b35","doi-asserted-by":"crossref","unstructured":"Specht, D.F.\n                (1991), \u201cA general regression neural networks\u201d, \n                  IEEE Transactions\n               , Vol. 2 No. 6, pp. 568-567.","DOI":"10.1109\/72.97934"},{"key":"key2020121420141099300_b36","doi-asserted-by":"crossref","unstructured":"Teixeira, J.\n                and \n                  Fernandes, P.\n                (2014), \u201cTourism time series forecast with artificial neural networks\u201d, \n                  Review of Applied Management Studies\n               , Vol. 12 Nos 1-2, pp. 26-36.","DOI":"10.1016\/j.tekhne.2014.08.001"},{"key":"key2020121420141099300_b37","unstructured":"Trading Economics\n                (2015), \u201cConsumer price index (CPI)\u201d, available at: www.tradingeconomics.com\/ (accessed January 22, 2015)."},{"key":"key2020121420141099300_b38","doi-asserted-by":"crossref","unstructured":"Wong, K.K.\n               , \n                  Song, H.\n                and \n                  Chon, K.S.\n                (2006), \u201cBayesian models for tourism demand forecasting\u201d, \n                  Tourism Management\n               , Vol. 27 No. 5, pp. 773-780.","DOI":"10.1016\/j.tourman.2005.05.017"},{"key":"key2020121420141099300_b39","unstructured":"World Travel and Tourism Council\n                (2014), \u201cAnnual research: key facts\u201d, available at: www.wttc.org\/-\/media\/files\/reports\/economic%20impact%20research\/country%20reports\/thailand2014.pdf (accessed March 3, 2015)."},{"key":"key2020121420141099300_b40","doi-asserted-by":"crossref","unstructured":"Xu, Y.L.\n               , \n                  Chen, H.X.\n               , \n                  Guo, W.\n                and \n                  Zhu, Q.Y.\n                (2014), \u201cA comparison of NARX and BP neural network in short-term building cooling load prediction\u201d, \n                  Applied Mechanics and Materials\n               , Vols 513-517, February, pp. 1545-1548.","DOI":"10.4028\/www.scientific.net\/AMM.513-517.1545"},{"key":"key2020121420141099300_b41","doi-asserted-by":"crossref","unstructured":"Yousefi, S.\n               , \n                  Weinreich, I.\n                and \n                  Reinarz, D.\n                (2005), \u201cWavelet-based prediction of oil prices\u201d, \n                  Chaos, Solitons and Fractals\n               , Vol. 25 No. 2, pp. 265-275.","DOI":"10.1016\/j.chaos.2004.11.015"},{"key":"key2020121420141099300_b42","doi-asserted-by":"crossref","unstructured":"Zhang, B.-L.\n               , \n                  Coggins, R.\n               , \n                  Jabri, M.\n                and \n                  Dersch, D.\n                (2001), \u201cMultiresolution forecasting for futures trading using wavelet decompositions\u201d, \n                  IEEE Transactions on Neural Networks\n               , Vol. 12 No. 4, pp. 765-775.","DOI":"10.1109\/72.935090"},{"key":"key2020121420141099300_b43","doi-asserted-by":"crossref","unstructured":"Zhang, X.\n               , \n                  Liu, Y.\n               , \n                  Yang, M.\n               , \n                  Zhang, T.\n               , \n                  Young, A.A.\n                and \n                  Li, X.\n                (2013), \u201cComparative study of four time series methods in forecasting typhoid fever incidence in China\u201d, \n                  Plos One\n               , Vol. 8 No. 5, pp. 1-11.","DOI":"10.1371\/journal.pone.0063116"}],"container-title":["Industrial Management &amp; Data Systems"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/www.emeraldinsight.com\/doi\/full-xml\/10.1108\/IMDS-11-2015-0463","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.emerald.com\/insight\/content\/doi\/10.1108\/IMDS-11-2015-0463\/full\/xml","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.emerald.com\/insight\/content\/doi\/10.1108\/IMDS-11-2015-0463\/full\/html","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,7,24]],"date-time":"2025-07-24T21:54:00Z","timestamp":1753394040000},"score":1,"resource":{"primary":{"URL":"http:\/\/www.emerald.com\/imds\/article\/116\/6\/1242-1258\/177583"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2016,7,11]]},"references-count":43,"journal-issue":{"issue":"6","published-print":{"date-parts":[[2016,7,11]]}},"alternative-id":["10.1108\/IMDS-11-2015-0463"],"URL":"https:\/\/doi.org\/10.1108\/imds-11-2015-0463","relation":{},"ISSN":["0263-5577"],"issn-type":[{"type":"print","value":"0263-5577"}],"subject":[],"published":{"date-parts":[[2016,7,11]]}}}