A neural network model to forecast Japanese demand for travel to Hong Kong

Law, Rob; Au, Norman

doi:10.1016/s0261-5177(98)00094-6

Cited by 260 publications

(151 citation statements)

References 10 publications

Supporting

Mentioning

145

Contrasting

Unclassified

Order By: Relevance

“…For comparison purpose, the data of Taiwan is also demonstrated in each table. Due to the perishable nature of the tourism industry, the need to carry accurate forecasts has become more and more crucial (Chandra & Meneze, 2001;Law, 2000;Law & Au, 1999). Meanwhile, no matter in which country or market, since tourism demand is the essential foundation on which all tourism-related business decisions conclusively rest (Song & Witt, 2006), the forecasting of tourism demand always keep on attracting lots of attentions and interests during last few decades among researchers, practitioners, and policy makers.…”

Section: Literature Reviewmentioning

confidence: 99%

A Spatiotemporal Study of Unfavorable Balance of Inbound-Outbound Travelers in Taiwan

2017

CAFES-17, ICFCT-2017, SDMCAE-17 April 18-19, 2017 Kyoto (Japan)

View full text Add to dashboard Cite

Section: Literature Reviewmentioning

confidence: 99%

A Spatiotemporal Study of Unfavorable Balance of Inbound-Outbound Travelers in Taiwan

2017

CAFES-17, ICFCT-2017, SDMCAE-17 April 18-19, 2017 Kyoto (Japan)

View full text Add to dashboard Cite

“…Based on the literature, feeding a particular NN with raw data is common because NNs are able to recognise high-level features such as serial correlations; and the NN is known as an appropriate classification and forecasting tool in business applications [37]. So there will be fifteen input nodes and one output node.…”

Section: Output Signalsmentioning

confidence: 99%

Developing a Modular Portfolio Selection Model for Short-Term and Longterm Market Trends and Mass Psychology

Jasemi

Kimiagari

2011

The South African Journal of Industrial Engineering

View full text Add to dashboard Cite

In an effort to model stock markets, many researchers have developed portfolio selection models to maximise investor satisfaction. However, this field still needs more accurate and comprehensive models. Development of these models is difficult because of unpredictable economic, social, and political variables that affect stock market behaviour. In this paper, a new model with three modules for portfolio optimisation is presented. The first module derives the efficient frontier through a new approach; the second presents an intelligent mechanism for emitting trading signals; while the third module integrates the outputs of the first two modules. Some important features of the model in comparison with others are: 1) consideration of investors' emotions -the psychology of the market -that arises from the three above-mentioned factors; 2) significant loosening of simplifying assumptions about markets and stocks; and 3) greater sensitivity to new data. OPSOMMINGIn 'n poging om aandelemarkte te modelleer het verskeie navorsers portefeulje-seleksiemodelle ontwikkel om beleggers se tevredenheid te maksimiseer. Desnieteenstaande word meer akkurate en omvattende modelle benodig. Die ontwikkeling van hierdie modelle word bemoeilik deur die onvoorspelbare ekonomiese, sosiale en politiese veranderlikes wat aandelemarkte se gedrag raak. In hierdie artikel word 'n nuwe model voorgehou wat bestaan uit drie modules vir portefeulje-optimisering. Die eerste module bepaal die doelmatigheidsgrens op 'n nuwe metode; die tweede hou 'n intelligente meganisme voor om transaksieseine te lewer terwyl die derde module die uitsette van die eerste twee modules integreer. Sommige van die belangrike eienskappe van die model wat dit van ander onderskei is: 1) konsiderasie van die beleggers se emosies -die sielkunde van die mark -wat ontstaan vanweë die genoemde faktore; 2) betekenisvolle verslapping van die vereenvoudigende aannames oor market en aandele; en 3) verhoogde sensitiwiteit tot nuwe data.

show abstract

“…Three main types of forecasting models (Li, Song & Witt, 2005; are Time series model (Cao, Ewing & Thompson, 2012;Cho, 200;Goshall & Charlesworth, 2011), Causal econometric model (Li, Song & Witt, 2006;Naude & Saayman, 2005;Page, Song & Wu, 2012;Roget & Gonzalez, 2006) and new emerging Artificial Intelligence based model, such as neural network, fuzzy time-series theory, grey theory, genetic algorithms, and expert systems (Cao, Ewing & Thompson, 2012;Carbonneau, Laframboise & Vahidov, 2008;Bodyanskiy & Popov 2006;Chen & Wang, 2007;Cho, 2003;Hadavandi, Ghanbari , Shahanaghi & Abbasian-Naghneh, 2011;Law & Au, 1999;Pai & Hong, 2005;Wong, Xia & Chu, 2010;Wu & Akbarov, 2011). From these studies, researchers often seek to identify the best individual model to generate a forecast.…”

Section: Introductionmentioning

confidence: 99%

A combination selection algorithm on forecasting

Cang

2014

European Journal of Operational Research

View full text Add to dashboard Cite

It is widely accepted in forecasting that a combination model can improve forecasting accuracy. One important challenge is how to select the optimal subset of individual models from all available models without having to try all possible combinations of these models. This paper proposes an optimal subset selection algorithm from all individual models using information theory. The experimental results in tourism demand forecasting demonstrate that the combination of the individual models from the selected optimal subset significantly outperforms the combination of all available individual models. The proposed optimal subset selection algorithm provides a theoretical approach rather than experimental assessments which dominate literature.

show abstract

A neural network model to forecast Japanese demand for travel to Hong Kong

Cited by 260 publications

References 10 publications

A Spatiotemporal Study of Unfavorable Balance of Inbound-Outbound Travelers in Taiwan

A Spatiotemporal Study of Unfavorable Balance of Inbound-Outbound Travelers in Taiwan

Developing a Modular Portfolio Selection Model for Short-Term and Longterm Market Trends and Mass Psychology

A combination selection algorithm on forecasting

Contact Info

Product

Resources

About