Forecasting Demand for Australian Passports

We forecast demand for Australian passports using a number of univariate and multivariate forecasting models, and assess their relative predictive ability over a number of forecasting horizons and evaluation measures. Our key result is to use different forecasting models for predicting passport demand in the short- versus medium- to long-run. Specifically, to forecast Adult-and-Senior passport demand in the short-term (i.e. up to 12 months) univariate ARIMA models are preferred, while for the longer term forecasts multivariate models with exogenous variables outperform, although only marginally. To forecast passport demand for Minors (less than 18 years old) ARIMA models perform well both in the short-term and the long-term, although ARIMA with explanatory variables outperforms slightly.     

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.     

Forecasting tourist arrivals with machine learning and internet search index

Previous studies have shown that online data, such as search engine queries, is a new source of data that can be used to forecast tourism demand. In this study, we propose a forecasting framework that uses machine learning and internet search indexes to forecast tourist arrivals for popular destinations in China and compared its forecasting performance to the search results generated by Google and Baidu, respectively. This study verifies the Granger causality and co-integration relationship between internet search index and tourist arrivals of Beijing. Our experimental results suggest that compared with benchmark models, the proposed kernel extreme learning machine (KELM) models, which integrate tourist volume series with Baidu Index and Google Index, can improve the forecasting performance significantly in terms of both forecasting accuracy and robustness analysis.     

Combining time-series and econometric forecast of tourism activity

It has always been difficult to model the travel industry because tourism involves such a diverse set of activities. However, various regional decision makers have become increasingly interested in predicting the flows of visitors through their market. Accurate forecasts of the number of tourists' arrivals, their length of stay, and their expenditures improve planning and inventory control. Stochastic time-series models have compared favorably with econometric models at the aggregate level while some naive automatic forecasting tools have fared well in comparison when predicting industry-level behavior. Several approaches have been developed to improve forecast accuracy. This paper presents parsimonious methods of improving accuracy by combining various forecasting techniques. The Box-Jenkins stochastic time-series method is combined with a traditional econometric technique to forecast airline visitors to the State of Florida.     

A dynamic linear model to forecast hotel registrations in Puerto Rico using Google Trends data

Recently, studies have used search query volume (SQV) data to forecast a given process of interest. However, Google Trends SQV data comes from a periodic sample of queries. As a result, Google Trends data is different every week. We propose a Dynamic Linear Model that treats SQV data as a representation of an unobservable process. We apply our model to forecast the number of hotel nonresident registrations in Puerto Rico using SQV data downloaded in 11 different occasions. The model provides better inference on the association between the number of hotel nonresident registrations and Google Trends SQV than using Google Trends data retrieved only on one occasion. Furthermore, our model results in more realistic prediction intervals of forecasts. However, compared to simpler models we only find evidence of better performance for our model when making forecasts on a horizon of over 6 months.     

A management orientated approach to combination forecasting of tourism demand

Volatility, exponential smoothing, regression and Naïve 2 models are considered singly and in combination in terms of forecasting demand for international tourism. These models generate accurate predictions of tourism flows, but their prime utility is when combined with other models. Usually, models are combined by means of purely statistical criteria. We show that goal programming (GP) offers an alternative, flexible approach to model combination. GP offers planners a practical solution to tourism forecasting problems, since the method is more adaptable than conventional minimisation of prediction error, by permitting practitioners to prioritise a series of management related goals. Forecasters can focus on longer- and short-term goals, minimising forecast under- and over-estimation and/or concentrate on prediction errors in tourism flows at various times of the year.     

Density tourism demand forecasting revisited

This study used scoring rules to evaluate density forecasts generated by different time-series models. Based on quarterly tourist arrivals to Hong Kong from ten source markets, the empirical results suggest that density forecasts perform better than point forecasts. The seasonal autoregressive integrated moving average (SARIMA) model was found to perform best among the competing models. The innovation state space models for exponential smoothing and the structural time-series models were significantly outperformed by the SARIMA model. Bootstrapping improved the density forecasts, but only over short time horizons.This article also launches the Annals of Tourism Research Curated Collection on Tourism Demand Forecasting, a special selection of research in this field.     

Modeling Tourist Flows to Indonesia and Malaysia

Summary

The purpose of this study was to examine the major factors that influence the flow patterns of tourists from six important tourist-generating countries to Indonesia and Malaysia. The primary determinants included in the demand models were income, prices, and time trend. Two models that employed different indicators for the price variable were estimated; one with exchange rates in addition to relative prices, whereas the other included only an exchange rate adjusted-relative price variable. Annual time-series data covering the period 1980 to 1997 were used for estimation. The results generally indicated that the factors provide reasonably good explanations for the demand for Indonesian and Malaysian tourism. The measure of thejoint effect of the changes in exchange rates and relative prices also seems to be a better indicator for the price variable for both destination countries. The study has important marketing implications for the tourism industries in Indonesia and Malaysia.     

Time-series forecasting: a testing of applications to the food-service industry

This paper examines the use of an intuitively appealing forecasting model for the food-service industry. After examining the current state of forecasting a brief introduction to the time-series method is undertaken. Finally, the simplest form of the model is applied in a case study.     

Forecasting China's Monthly Inbound Travel Demand

Summary

China is currently expecting a growth in inbound travel demand as the result of China's “open door policy,” participation in World Trade Organization (WTO), success in hosting the Olympics in Beijing in the year 2008 and political stability. This paper focused on two issues: (1) forecasting China's monthly inbound travel demand and (2) seasonally and seasonal ARIMA model selection for monthly tourism time-series. In this paper following seasonal ARIMA models were considered: the seasonal ARIMA model with first differences and 11 seasonal dummy variables, the conventional seasonal ARIMA model with first and the fourth differences. In order to select the best forecasting model, finally both seasonal ARIMA models were compared with the AR model with fourth differences, the basic structural model (BSM) and the naive “No Change” model. In the one-step ahead forecasting comparison, the conventional seasonal ARIMA model with first and the fourth differences becomes the best forecasting model for both inbound foreign visitor demand and total visitor demand. This may be due to the nature of monthly seasonal variations in visitor arrivals, which is less marked. Our forecasts indicate that China foreign visitor arrivals and total visitor arrivals are expected to grow by 14% and 27% respectively from 2002 to 2005.     

Forecasting tourism revisited

This article examines the accuracy and efficiency of forecasting techniques by applying time series regression to forecasting visitor arrivals. Past studies have shown that simpler time series techniques perform as well or better than complex forecasting models. An assessment of visitor forecasts developed at regional, destination and individual market levels suggests that time series regression performs well in producing annual forecasts of visitors which can also serve as a baseline for evaluating the net returns from applying more complex techniques. Tourism managers should appreciate the usefulness of simpler formal methods in developing forecasts of visitors.     

The relevance of business cycles in forecasting international tourist arrivals

Casual empiricism suggests that there may be a cyclical trend associated with international tourist arrivals in which variation around the linear trend can be formed by the interaction with other cyclical phenomena. This paper employs a simple model that incorporates a linear trend and sine function to capture these two characteristics in forecasting international tourist arrivals in Hong Kong. The model is extended to include a set of sine functions through the application of Fourier analysis to account for situations in which more than one phenomenon may be present in the time series. The forecasting accuracy of the model is compared with other forecasting approaches. Evaluation of the results using the mean absolute percentage error measure show that the forecasting performance of the extended model with a linear trend and two sine functions is superior in terms of accuracy when compared with other forecasting models.     

Tourism demand forecasting models: Choice of appropriate variable to represent tourists' cost of living

Price is generally regarded as a major determinant of demand. Tourism has two price elements - the cost of travel to the destination and the cost of living for tourists in the destination. Previous studies where econometric forecasting models have been developed for international tourism demand usually take the consumer price index in a country to be a proxy for the cost of tourism in that country owing to lack of appropriate data. This article attempts to evaluate the performance of proxy variables compared with a specific tourists' cost of living variable within the context of tourism demand forecasting models.     

Impact of decomposition on time series bagging forecasting performance

Time series bagging has been deemed an effective way to improve unstable modelling procedures and subsequent forecasting accuracy. However, the literature has paid little attention to decomposition in time series bagging. This study investigates the impacts of various decomposition methods on bagging forecasting performance. Eight popular decomposition approaches are incorporated into the time series bagging procedure to improve unstable modelling procedures, and the resulting bagging methods' forecasting performance is evaluated. Using the world's top 20 inbound destinations as an empirical case, this study generates one-to eight-step-ahead tourism forecasts and compares them against benchmarks, including non-bagged and seasonal naïve models. For short-term forecasts, bagging constructed via seasonal extraction in autoregressive integrated moving average time series decomposition outperforms other methods. An autocorrelation test shows that efficient decomposition reduces variance in bagging forecasts.     

Forecasting in Short-Term Planning and Management for a Casino Buffet Restaurant

SUMMARY

Demand fluctuation accounts for an important consideration in a restaurant's daily operational decisions. Good short-term planning and management require accurate forecasts of daily demand. The objective of this study is three-fold: (1) to apply, evaluate, and compare different methods of forecasting customer counts for an on-premises buffet restaurant of a local casino in Las Vegas, (2) to describe and propose a combined forecasting approach for this casino buffet restaurant, and (3) to explore the concept of revenue and capacity management for this buffet restaurant. Eight forecasting models were tested and evaluated by two common error measures. The results suggest that a double moving average model was the most accurate model with the smallest MAPE and RMSPE. Extensive discussions on forecasting and planning/management in buffet operations are provided along with recommended future research.     

A review of research on tourism demand forecasting: Launching the Annals of Tourism Research Curated Collection on tourism demand forecasting

This study reviews 211 key papers published between 1968 and 2018, for a better understanding of how the methods of tourism demand forecasting have evolved over time. The key findings, drawn from comparisons of method-performance profiles over time, are that forecasting models have grown more diversified, that these models have been combined, and that the accuracy of forecasting has been improved. Given the complexity of determining tourism demand, there is no single method that performs well for all situations, and the evolution of forecasting methods is still ongoing.This article also launches the Annals of Tourism Research Curated Collection on tourism demand forecasting, which contains past and hot off the press work on the topic and will continue to grow as new articles on the topic appear in Annals.     

A piecewise linear approach to modeling and forecasting demand for Macau tourism

In this paper, we construct and use a piecewise linear method to model and forecast, on a monthly basis, the demand for Macau tourism. Data over the period January 1991–December 2005 and a seasonally adjusted series for tourism demand are used. The study examines 4 forecasting horizons ranging from 6 to 24 months in advance. Mean absolute percentage errors and root mean square errors are adopted as criteria for evaluating the accuracy of the forecasting exercises. Finally, the forecasts of piecewise linear model are compared with those of autoregressive trend model, seasonal autoregressive integrated moving average and its arch-rival fractionally integrated autoregressive moving average models. The piecewise linear model is more accurate than the three benchmark models tested and the improvement is practically significant.     

Forecasting tourism demand with ARMA-based methods

The forecast of tourism volume in the form of arrivals is of special importance for tourism and other hospitality industries because it is an indicator of future demand, thereby providing basic information for subsequent planning and policy making. In this paper, three univariate ARMA-based models are applied to tourism demand, as represented by the number of world-wide visitors to Hong Kong, Japan, Korea, Taiwan, Singapore, Thailand, the Philippines, Australia and New Zealand. The study employs both monthly and quarterly time series generated from nine principal tourist destinations in Asian-Pacific region in the forecasting exercise to ensure the reliability of the forecasting evaluation. Forecasting performance based on disaggregated arrival series in a particular destination is examined as well. The general impression is that the ARMA-based models perform very well and in some cases the magnitude of mean absolute percentage error is lower than 2% level.     

The Effects of Exchange Rate,Income and Habit on Japanese Travel to Canada

Using three econometric models, this study examined the effects of habit, income, and exchange rate on the Canadian tourism industry. While the three variables were found to be significant in the explanation of tourist arrivals in earlier studies, the present study explored in greater details the extent and nature of their influences. The findings indicated that exchange rate was more powerful as an explanatory variable than income in both forecasting ability and stability. On the other hand, the habit variable was consistently important throughout all the estimation periods. The implications of the findings were discussed.     

Daily tourism volume forecasting for tourist attractions

A novel approach based on long short-term memory (LSTM) networks that can incorporate multivariate time series data, including historical tourism volume data, search engine data and weather data, is proposed for forecasting the daily tourism volume of tourist attractions. The proposed approach is applied to forecast the daily tourism volume of Jiuzhaigou and Huangshan Mountain Area, two famous tourist attractions in China. Through these two applications, the validity of the proposed approach is verified. In addition, the forecasting power of the approach with historical data, search engine data and weather data is stronger than that without search engine data or without both search engine data and weather data, which provides evidence that search engine data and weather data are of great significance to tourism volume forecasting.