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

Chun Hung Roberts Law, Norman Au

Research output: Journal article publicationJournal articleAcademic researchpeer-review

216 Citations (Scopus)

Abstract

Apart from simple guesswork, time-series and regression techniques have largely dominated forecasting models for international tourism demand. This paper presents a new approach that uses a supervised feed-forward neural network model to forecast Japanese tourist arrivals in Hong Kong. The input layer of the neural network contains six nodes: Service Price, Average Hotel Rate, Foreign Exchange Rate, Population, Marketing Expenses, and Gross Domestic Expenditure. The single node in the output layer of the neural network represents the Japanese demand for travel to Hong Kong. Officially published annual data in the period of 1967 to 1996 were used to build the neural network. Estimated Japanese arrivals were compared with actual published Japanese arrivals. Experimental results showed that using the neural network model to forecast Japanese arrivals outperforms multiple regression, naive, moving average, and exponent smoothing.
Original languageEnglish
Pages (from-to)89-97
Number of pages9
JournalTourism Management
Volume20
Issue number1
DOIs
Publication statusPublished - 1 Feb 1999

Keywords

  • Hong Kong
  • Japanese tourist arrivals
  • Neural networks
  • Tourism demand forecasting

ASJC Scopus subject areas

  • Development
  • Transportation
  • Tourism, Leisure and Hospitality Management
  • Strategy and Management

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