Forecasting Tourist Arrivals Research Articles

Forecasting with X-12-ARIMA: International tourist arrivals to India and Thailand

Forecasting is an essential analytical tool in tourism policy and planning. This paper focuses on forecasting methods based on X-12-ARIMA seasonal adjustment and this method was developed by the Census Bureau in the United States. It has been continually improved since the 1960s, and it is used by many statistics agencies and central banks. The secondary data were used to produce forecasts of international tourist arrivals to India for 2007-2010 and also these data were used to produce forecasts of international tourist arrivals to Thailand for 2006-2010. From these period the results confirm that the best forecasting method based on the X-12-ARIMA seasonal adjustment is X-12-ARIMA(0,1,2)(0,1,1), X-12-ARIMA(0,1,1)(0,1,1) and X-12-ARIMA(2,1,0)(0,1,1) for India and the best forecasting method based on this method is X-12-ARIMA(0,1,1)(0,1,1) and X-12-ARIMA(2,1,0)(0,1,1) for Thailand. Furthermore this method predict that international tourism arrivals to India for 2007–2010 will growth at a positive rate as same as in this during period the number of international tourists arrival to India will be 5,079,651 million, 5,652,180 million, 6,224,480 million and 6,796,890 million, respectively. Also this method predict that international tourism arrivals to Thailand for 2006-2010 will growth at a positive rate as same as in this during period the number of international tourists arrival to Thailand will be 12,211,033 million, 12,699,532 million, 13,187,591 million, 13,674,669 million and 14,161,998 million, respectively. If these results can be generalized for future year, then it suggests that both the India government sector and the Thailand government sector also the private tourism industry sector of these country should prepare to receive increasing numbers of international tourist arrivals both to India and Thailand in this period.

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.

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.

Comparison of ARIMA, neural networks and hybrid models in time series: tourist arrival forecasting

For time series forecasting, different artificial neural network (ANN) and hybrid models are recommended as alternatives to commonly used autoregressive integrated moving average (ARIMA) models. Recently, combined models with both linear and nonlinear models have greater attention. In this article, ARIMA, linear ANN, multilayer perceptron (MLP), and radial basis function network (RBFN) models are considered along with various combinations of these models for forecasting tourist arrivals to Turkey. Comparison of forecasting performances shows that models with nonlinear components give a better performance.

Effect of Demand Volume on Forecasting Accuracy

The forecasting of international tourist arrivals is normally done on a per country basis. The volume of tourist flow varies widely among countries, largely depending on their population size but also on their openness to tourism. This paper uses data for Austria, China (PRC), Canada, the Cook Islands, Cyprus, Japan, the Maldives, Malta, New Zealand, Singapore, Slovenia and Thailand over a quarterly estimation period from 1995 to 1999 to forecast ahead for 2000 to 2002. In addition, the total arrivals to Japan from 24 different countries of origin are also examined with the same estimation and forecast periods. The topic explored is whether it is possible to examine the structure of the time-series data to determine why particular forecasting is more or less accurate. As a starting point, forecasts are obtained from larger data volumes relative to smaller volumes. The forecasting comparison uses the short-term time series methods of the basic structural model and the Holt–Winters model, with a comparison for base accuracy against the naïve model. The results show in the forecasting comparison that the volume of flow and volatility and seasonality do not directly influence the accuracy of the forecast.

Can Seasonal Unit Root Testing Improve the Forecasting Accuracy of Tourist Arrivals?

Tourism has emerged as one of the main industries in many Caribbean countries. Jamaica is no exception: tourist receipts are its largest source of foreign exchange earnings. Because of the importance of the tourism sector to the Jamaican economy, accurate forecasts of tourist arrivals are critical. This paper argues that forecasting accuracy can be improved by the use of seasonal unit root testing. Using six tourist arrival series, from 1968:1 to 2001:3, it was found that unit root testing, applying the HEGY procedure, did help to improve the forecasts at all horizons. The exceptions were those series that exhibited considerable volatility; in these cases the seasonal unit root test did not prove superior at the short horizon. These results, however, may not be robust with respect to data at other frequencies.

Factors that determine the price of game

Fraser (1999) forecasts that South Africa’s tourism industry will grow by more than 5.5 % between 1998 and 2010, which is better than the predicted 4.1% for the global market. It is estimated that arrivals will increase from six million international tourists in 2000 to 30.5 million in 2020 (WTO 2001). The African continent can triple the size of its tourism industry by 2020 if proper efforts are made. This will result in forecast tourist arrivals of 77.3 million to Africa.

Trends and Forecasts for Inbound Tourism to China

Summary The development of tourism in China over the last two decades is reviewed. It is argued that the success of the economic reforms in China which has resulted in fast economic growth has also been the main cause of rapid tourism growth in China. Forecasts of international tourist arrivals by source country market and destination region within China are generated over the period 2001-05. An integrative approach is used which combines both time-series and econometric methodologies, termed structural integrated time-series econometric analysis (SITEA).

Time series forecasts of international travel demand for Australia

This paper analyses stationary and non-stationary international tourism time series data 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 are estimated over the period 1975(1)–1989(4) for tourist arrivals to Australia from Hong Kong, Malaysia and Singapore. The mean absolute percentage error and root mean squared error (RMSE) are used as measures of forecast accuracy. As the best fitting ARIMA model is found to have the lowest RMSE, this model is used to obtain post-sample forecasts. Tourist arrivals data for 1990(1)–1996(4) are compared with the forecast performance of the ARIMA model for each origin market. The fitted ARIMA model forecasts tourist arrivals from Singapore for the period 1990(1)–1996(4) very well. Although the ARIMA model outperforms the seasonal ARIMA models for Hong Kong and Malaysia, the forecasts of tourist arrivals are not as accurate as in the case of Singapore.

Forecasting tourist arrivals

Various exponential smoothing models are estimated over the period 1975–1999 to forecast quarterly tourist arrivals to Australia from Hong Kong, Malaysia, and Singapore. The root mean squared error criterion is used as a measure of forecast accuracy. Prior to obtaining the one-quarter-ahead forecasts for the period 1998 to 2000, the individual arrival series are tested for unit roots to distinguish between stationary and non-stationary time series arrivals. The Holt–Winters Additive and Multiplicative Seasonal models outperform the Single, Double, and the Holt–Winters Non-Seasonal Exponential Smoothing models in forecasting. It is also found that forecasting the first differences of tourist arrivals performs worse than forecasting its various levels.

Forecasting tourism demand: An STM approach

Since tourism is the most important economic activity in many Caribbean countries, the forecast of tourism is the key to predicting overall macroeconomic performance in this region. This study utilized Structural Time Series Modeling to explain and forecast tourist arrivals to Barbados from its major generating markets. The procedure will allow one to extract the maximum amount of information contained in the series on tourist arrivals while at the same time to include any other information relevant to forecasting arrivals. This study found these models offered valuable insights into the stylized facts of tourism behavior and provided reliable out-of-sample forecasts.

Forecasting tourism demand in asian-pacific countries

This article scrutinizes issues relating to the forecasting of international tourist arrivals. The area of interest is Asian-Pacific countries. Six forecasting techniques are examined. The results show that the accuracy of the forecasts differs depending on the country being forecast, but that the seasonal-nonseasonal ARIMA model is overall the most accurate method for forecasting international tourist arrivals. This research also finds, that, judging from the value of Theil inequality coefficient, seasonal-nonseasonal ARIMA is superior to any other technique examined in this article. Finally, the issue of policy implication is also addressed.

The Use of Composite National Indicators for Tourism Forecasting

This paper identifies the usefulness of available national indicators in forecasting tourist arrivals to Australia from the USA, Japan, the UK and New Zealand. Income, unemployment, forward exchange rate, money supply, price ratio, industrial production, imports and exports of the tourist's country of origin are potential leading indicators of the tourist arrival series. A composite indicator is developed to forecast tourist arrivals. Further, disaggregated data are analysed to determine whether there is likely to be any increase in accuracy if the travel data is divided by travel type. The main travel types analysed are ‘holiday’, ‘visiting friends and relatives’ (VFR) and ‘business’ as well as total flow. To establish the causality between the indicator and international tourist arrivals to Australia, the performance of the transfer function model incorporating the national indicator is compared with ARIMA forecasts.

Forecasting tourist arrivals in Barbados

Importance of forecasting in tourism is not a controversial issue. Recently, there has been increased attention on forecasting models in tourism. The value of a forecasting model depends on the accuracy of out-of-sample forecasts. At present, there is no indication as to which model or class of models is suitable for forecasting tourism. This paper specifies, estimates, and validates an ARIMA model for forecasting long-stay visitors in Barbados. The accuracy of the short-term forecasts surpasses most recent forecasting studies. The implication of the study is that customized model building may be highly rewarding in terms of accurate forecasts compared to standard or simple methods.

Multimethod forecasts for tourism analysis

This paper examines the use of combined forecasting methods in tourism forecasting. The use of multiple methods allows more information to be incorporated into the forecasts. The objective of the paper is to show that forecasts of tourism arrivals obtained by combining forecasting models are more accurate (therefore more useful to practicing managers) than single-method forecasts. Several combined forecasts of tourist arrivals in Florida obtained and compared to single-method forecasts gained through econometric or time-series methods. Parameter interpretation and forecasting accuracy of both combined and single-method forecasts are discussed. The conclusion of the paper offers a commentary on the usefulness of multimethod approaches to forecasting.

TSM Use of the box Jenkins approach to forecast tourist arrivals: M.D. Geurts, T.A. Buchman, I.B. Ibrahim, Journal of Travel Research(Business Research Division, University of Colorado, Boulder, Colorado, 80302, USA), Volume XIV, Number 4, Spring 1976, pp. 5–8

TSM Use of the box Jenkins approach to forecast tourist arrivals: M.D. Geurts, T.A. Buchman, I.B. Ibrahim, Journal of Travel Research(Business Research Division, University of Colorado, Boulder, Colorado, 80302, USA), Volume XIV, Number 4, Spring 1976, pp. 5–8

Use of the Box-Jenkins Approach to Forecast Tourist Arrivals

Use of the Box-Jenkins Approach to Forecast Tourist Arrivals