Tourism Forecasting Research Articles

Rough Fuzzy Approach in Tourism Demand Analysis

The substantial growth of the tourism activities in Malaysia clearly marks tourism as one of the most remarkable economic and social phenomena of the past few years. This paper introduces the rough-fuzzy approach in tourism forecasting. The rough-fuzzy is the extension of rough sets. Its can also be defined when the value of decisions and conditions attribute are uncertain. Within the hybridization process, we can see the strengthens of knowledge by the membership function. The study shows that the membership value of tourist arrivals from Saudi Arabia, Australia and US is 1, while the values for tourist arrivals from Taiwan and UK are 0.6755 and 0.2053 respectively. The degree of tourist arrivals from China and Thailand is also equal to 1, and from Japan’s is 0.4167. Keywords: Tourism demand; decision table; membership value; rough-fuzzy sets.

Forecasting tourist arrivals to Balearic Islands using genetic programming

Traditionally, univariate time-series models have largely dominated forecasting for international tourism demand. In this paper, the ability of a genetic program (GP) to predict monthly tourist arrivals from UK and Germany to Balearic Islands, Spain is explored. GP has already been employed satisfactorily in different scientific areas, including economics. The technique shows different advantages regarding to other forecasting methods. Firstly, it does not assume a priori a rigid functional form of the model. Secondly, it is more robust and easy-to-use than other non-parametric methods. Finally, it provides explicitly a mathematical equation which allows a simple ad hoc interpretation of the results. Comparing the performance of the proposed technique against other method commonly used in tourism forecasting (no-change model, moving average and ARIMA), the empirical results reveal that GP can be a valuable tool in this field.

Management and Policy Implications of Coastal Tourism Forecasts

One of the fastest growing segments of the world's tourism industry is coastal tourism. As interest in coastal tourism intensifies, the increasing demand on coastal resources will require local governments and private businesses to obtain information on future tourism demand. However, management agencies often fail to incorporate forecasting results into the strategic planning process. The purpose of this article is to present comprehensive forecasting information of coastal tourism demand using various tourism-related time series variables, and provide public and private agencies with a framework for integrating coastal demand forecasts into the strategic planning process. Two different quantitative forecasting techniques were used to project future coastal tourism demand in South Carolina: Auto Regressive Integrated Moving Average (ARIMA) methods for short-term and midterm forecasts and Naive 1 method for long-term forecasts. Short-term forecast results suggest accommodation taxes in coastal counties will steadily increase over the next several years, while midterm forecasts of coastal tourism-related employment indicates a significant increase in labor force requirements. Long-term forecasts imply visitation to and demand of beaches and coastal resources will continue to intensify. In the coming years, tourism-related businesses in coastal destinations will be challenged to provide satisfactory experiences due to a small labor pool of quality workers. Failure to incorporate forecasts in the strategic planning process may result in the degradation of tourism resources, resulting in a reduction of tourist visitation. Integrating accurate forecasts into the strategic planning process are essential to improving the likelihood of sustainable development at tourism destinations.

An Econometric Model Forecasting Egypt's Aggregate International Tourism Demand and Revenues

Egypt is one of the most important tourist destinations worldwide; it receives millions of visitors every year. Consequently, tourism forecasting and planning are vital in maintaining and managing this flow of visitors. Hence, it is imperative for policymakers in both the public and private tourism sectors in Egypt to examine tourist arrival patterns, and to generate forecasts for the industry. The objective of this paper is to forecast the performance of the tourism industry in Egypt until the year 2017. The main factors affecting such performance are the deficiency in exploiting human capital efficiently, ineffective marketing campaigns, and lack of strategies to enhance Egypt's image. This study examines the aggregate tourism demand function for Egypt using the time series data for the period 1982–2006. An Auto Regressive Moving Average (ARMA) model is applied as an efficient tool to forecast the future values of dependent variables (international tourism arrivals and revenues); and its relationship with the above-mentioned independent ones, until the year 2017.

Hierarchical forecasts for Australian domestic tourism

In this paper we explore the hierarchical nature of tourism demand time series and produce short-term forecasts for Australian domestic tourism. The data and forecasts are organized in a hierarchy based on disaggregating the data according to geographical regions and purposes of travel. We consider five approaches to hierarchical forecasting: two variations of the top-down approach, the bottom-up method, a newly proposed top-down approach where top-level forecasts are disaggregated according to the forecasted proportions of lower level series, and a recently proposed optimal combination approach. Our forecast performance evaluation shows that the top-down approach based on forecast proportions and the optimal combination method perform best for the tourism hierarchies we consider. By applying these methods, we produce detailed forecasts of the Australian domestic tourism market.

Modelling and forecasting tourism from East Asia to Thailand under temporal and spatial aggregation

Tourism is one of the key service industries in Thailand, with a 5.27% share of Gross Domestic Product in 2003. Since 2000, international tourist arrivals, particularly those from East Asia, to Thailand have been on a continuous upward trend. Tourism forecasts can be made based on previous observations, so that historical analysis of tourist arrivals can provide a useful understanding of inbound trips and the behaviour of trends in foreign tourist arrivals to Thailand. As tourism is seasonal, a good forecast is required for stakeholders in the industry to manage risk. Previous research on tourism forecasts has typically been based on annual and monthly data analysis, while few past empirical tourism studies using the Box–Jenkins approach have taken account of pre-testing for seasonal unit roots based on Franses [P.H. Franses, Seasonality, nonstationarity and the forecasting of monthly time series, International Journal of Forecasting 7 (1991) 199–208] and Beaulieu and Miron [J.J. Beaulieu, J.A. Miron, Seasonal unit roots in aggregate U.S. data, Journal of Econometrics 55 (1993) 305–328] framework. An analysis of the time series of tourism demand, specifically monthly tourist arrivals from six major countries in East Asia to Thailand, from January 1971 to December 2005 is examined. This paper analyses stationary and non-stationary tourist arrivals series by formally testing for the presence of unit roots and seasonal unit roots prior to estimation, model selection and forecasting. Various Box–Jenkins autoregressive integrated moving average (ARIMA) models and seasonal ARIMA models are estimated, with the tourist arrivals series showing seasonal patterns. The fitted ARIMA and seasonal ARIMA models forecast tourist arrivals from East Asia very well for the period 2006(1)–2008(1). Total monthly and annual forecasts can be obtained through temporal and spatial aggregation.

Call for Papers: Special issue of the International Journal of Forecasting on tourism forecasting

Call for Papers: Special issue of the International Journal of Forecasting on tourism forecasting

Appraising Tourism and Hospitality Service Failure Events: A Chinese Perspective

Tourism forecasts suggest that the People's Republic of China is a growing tourism market to international destinations. It is likely that these tourists will encounter a range of service experiences and, at times, service failures or problems. Gaining an insight into how Chinese tourists interpret service failure events can help firms develop more effective service recovery strategies. This study aimed to explore the cultural values that Chinese hold for service failure and service recovery, using means—end chain methodology. The analysis of 70 in-depth interviews found five key themes representing the values that were threatened in poor service situations. The attributes and consequences were analyzed and then linked to the five values of face, equity, value, harmony, and junzi aspiration. Implications for hospitality service recovery with Chinese customers are discussed.

New Forecasting Models

Tourism forecasting plays an important role in tourism planning and management. Various forecasting techniques have been developed and applied to the tourism context, amongst which econometric forecasting has been winning an increasing popularity in tourism research. This article therefore aims to introduce the latest developments of econometric forecasting approaches and their applications to tourism demand analysis. Particular emphases are placed on the time varying parameter (TVP) forecasting technique and its application to the almost ideal demand system (AIDS). The discussions in this article fall into two main parts, in line with the two broad categories of econometric forecasting approaches: the first part refers to the single-equation forecasting techniques, focusing particularly on both long-run and short-term TVP models. The second part introduces the system-of-equations forecasting models, represented by the AIDS and its dynamic versions including the combination with the TVP technique, which will be discussed one by one following the order of methodological developments.

A fractionally integrated autoregressive moving average approach to forecasting tourism demand

The primary aim of this paper is to incorporate fractionally integrated ARMA ( p, d, q) ( ARFIMA) models into tourism forecasting, and to compare the accuracy of forecasts with those obtained by previous studies. The models are estimated using the volume of monthly international tourist arrivals in Singapore. Empirical findings demonstrate the evidence that the approach we propose generates relatively lower sample mean absolute percentage errors (MAPEs). This study also deals with the volatile data faced by a forecaster. We use the Asian financial crisis and the September 11 event as examples of economic and political shocks. With respect to the objective of shaving the coefficient MAPE, forecasts based on the selected ARFIMA models dominate convincingly.

Tourism forecasting: To combine or not to combine?

Existing non-tourism related literature shows that forecast combination can improve forecasting accuracy. This study tests this proposition in the tourism context by examining the efficiency of combining forecasts based on three different combination methods. The data used for this study relate to tourist arrivals in Hong Kong from the top ten tourism generating countries/regions. The forecasts are derived from four different forecasting models: autoregressive integrated moving average (ARIMA) model, autoregressive distributed lag model (ADLM), error correction model (ECM) and vector autoregressive (VAR) model. All forecasts are ex post and the empirical results show that the relative performance of combination versus single model forecasts varies according to the origin–destination tourist flow under consideration, which parallels previous findings regarding the relative performance of individual forecasting methods. The results also vary with the combination techniques used. Furthermore, although the combined forecasts do not always outperform the best single model forecasts, almost all the combined forecasts are not outperformed by the worst single model forecasts. This suggests that forecast combination can considerably reduce the risk of forecasting failure. This conclusion also implies that combined forecasts are likely to be preferred to single model forecasts in many practical situations.

Modeling Seasonality in Tourism Forecasting

Within the multiplicative seasonal ARIMA modeling context, there are two forecasting models, ARIMA14 and ARIMA1. ARIMA14 is used for modeling stochastic nonstationary seasonality and requires first and fourth differences to achieve stationarity. ARIMA1 considers the series only in first differences, and seasonality is modeled with a constant and three seasonal dummies. The selection of either model depends on the nature of seasonality. Conventional unit root tests determine the nature of seasonality and the order of integration and, therefore, the series’ choice of forecasting model. To determine whether the test correctly identifies the forecasting model for tourism demand, out-of-sample forecasting performance of ARIMA1 and ARIMA14 is compared with HEGY unit root model selection method. Comparing forecasting performance of both models with HEGY unit root model selection shows that the outcome of HEGY test procedure may not be useful in the selection of a univariate time-series model for quarterly tourism demand series.

Analyzing the Behavioral Trends in Tourist Arrivals from Japan to Australia

As tourism forecasts are obtained based on past observations, an historical analysis of Japan’s postwar economic success, social factors, and the national government’s institutional policies and reforms can help to provide a better understanding of the growth in Japanese outbound travel and the trending patterns in Japanese tourist arrivals to Australia. To achieve these aims, a statistical analysis of the time series behavior of tourism demand, specifically quarterly tourist arrivals from Japan to Australia from 1976 to 2000, are examined. In addition to analyzing the full sample, the authors also consider three subsamples, namely quarter 1 of 1976 to quarter 2 of 1987, quarter 3 of 1987 to quarter 2 of 1997, and quarter 3 of 1997 to quarter 2 of 2000, to evaluate the sensitivity of the estimates to changes in trends arising from the 1987 stock market crash and the Asian economic and financial crises in 1997. Autoregressive moving average time series models are estimated to analyze alternative patterns of trending behavior within this class of models.

Tourism forecasting and marketing, by Kelvin Wong and Haiyan Song (eds). Haworth Hospitality Press. No. of pages: 150. ISBN 0-7890-2086-6.

International Journal of Tourism ResearchVolume 7, Issue 3 p. 185-185 Book Review Tourism forecasting and marketing, by Kelvin Wong and Haiyan Song (eds). Haworth Hospitality Press. No. of pages: 150. ISBN 0-7890-2086-6. Bob van Limburg, Bob van LimburgSearch for more papers by this author Bob van Limburg, Bob van LimburgSearch for more papers by this author First published: 14 April 2005 https://doi.org/10.1002/jtr.523AboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinked InRedditWechat No abstract is available for this article. Volume7, Issue3May/June 2005Pages 185-185 RelatedInformation

An Application of PAR Models for Tourism Forecasting

This paper extends the existing literature on tourism forecasting by developing an application study based on periodic models. The need for this type of approach derives from the common procedure in tourism literature of classifying series into three seasons: peak, shoulder and off-peak. This classification is useful in identifying more parsimonious periodic models when compared with unrestricted periodic monthly models. The authors apply their proposed models on several tourism series collected and analysed from Portugal's southernmost province, Algarve. The economy of this region relies heavily on the tourism industry, catering largely for the European market. Besides statistically validating these models, the authors compare their forecasting ability with other models in the current literature. The results show that the models presented achieve a superior forecasting performance.

Future Tourists' Characteristics and Decisions: The Use of Genetic Algorithms as a Forecasting Method

This paper applies genetic algorithms to tourism forecasting. To date, genetic algorithms have normally been used as an optimization method. Their application to forecasting with real-world data has not been studied extensively. The paper illustrates how genetic algorithms can be used for this purpose and the new kind of forecast that is obtained. Specifically, the algorithms are applied to a real tourist population, and the characteristics and decisions of each future tourist are forecast. The results suggest that tourism forecasting based on genetic algorithms could reduce the risk of decision making in tourism planning.

Long-Term Forecasts for International Tourism

Modifications of earlier versions of models to make forecasts for world tourism allow us to trace the effects of changes in income and prices produced by and in each individual country considered in this model. At the theoretical level, in using a share approach these models could be viewed as similar to partial demand models based on the assumption of a multi-stage budgeting process. The new WTTOUR-2001 model is used to generate long-term forecasts of tourism imports and exports for 25 countries for the period up to 2020. It allows a more realistic simulation of the impact of political events such as the enlargement of the European Union.

Sftis: A Decision Support System for Tourism Demand Analysis and Forecasting

Accurate forecasts of tourism demand are prerequisite to the decision-making process in many organizations of the private or public sector. Any information concerning the future evolution of tourism flows is of great importance to hoteliers, tour operators and other industries related to tourism or transportation, in order to adjust their policy and corporate finance in an effective and efficient way. This paper aims to present a new, improved model for Tourism Demand Analysis and Forecasting (DTAF) and then to discuss the development of an innovative Decision Support System (called SFTIS) which provides the user with appropriate tools necessary for forecasting, analysis and effective use of tourism data, as a mean of decision support for optimal planning and policy making. In order to evaluate the system, we used Greek inbound tourism data for the year 2000 from ten major origin countries.

Tourism Forecasting: The General-to-Specific Approach

The general-to-specific approach to econometric modeling and forecasting has a clear strategy in model specification, estimation, and selection and is demonstrated in the context of international tourism demand. A general autoregressive distributed lag model is reduced to various specific models by imposing parameter restrictions, and model selection is based on restriction tests, consistency with economic theory, and diagnostic statistics. The models are estimated using data on inbound tourism to South Korea from its four major generating countries: Germany, Japan, the United Kingdom, and the United States. Statistical tests show that a unique model specification across origin countries is not appropriate.

Leading indicator tourism forecasts

Leading indicators have been widely used in general business forecasting situations, but only rarely in a tourism context. In this study leading indicator transfer function (TF) models are developed to generate forecasts of international tourism demand from the UK to six major destinations. The out-of-sample forecasting accuracy is compared with the accuracy of forecasts generated by univariate ARIMA and error correction models (ECMs). The inclusion of a causal input within an ARIMA time series framework (TF model) does not result in an improvement in forecasting performance. The time series models outperform the ECM for short-term forecasting, but the ECM generates more accurate longer-term forecasts.