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.     

Short-term forecasting of Japanese tourist inflow to South Korea using Google trends data

We utilize the Internet search data from Google Trends to provide short-term forecasts for the inflow of Japanese tourists to South Korea. We construct the Google variable in a systematic way by combining keywords to minimize mean squared or mean absolute forecasting errors. We augment the Google variable to the standard time-series forecasting models and compare their forecasting accuracies. We find that Google-augmented models perform much better than the standard time-series models in terms of short-term forecasting accuracy. In particular, Google models show better out-of-sample forecasting performance than in-sample forecasting.     

Forecasting turning points in tourism growth

Tourism demand exhibits growth cycles, and it is important to forecast turning points in these growth cycles to minimise risks to destination management. This study estimates logistic models of Hong Kong tourism demand, which are then used to generate both short- and long-term forecasts of tourism growth. The performance of the models is evaluated using the quadratic probability score and hit rates. The results show that the ways in which this information is used are crucial to the models’ predictive power. Further, we investigate whether combining probability forecasts can improve predictive accuracy, and find that combination approaches, especially nonlinear combination approaches, are sensitive to the quality of forecasts in the pool. In addition, model screening can improve forecasting performance.     

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.     

Cross-temporal coherent forecasts for Australian tourism

Key to ensuring a successful tourism sector is timely policy making and detailed planning. National policy formulation and strategic planning requires long-term forecasts at an aggregate level, while regional operational decisions require short-term forecasts, relevant to local tourism operators. For aligned decisions at all levels, supporting forecasts must be ‘coherent’, that is they should add up appropriately, across relevant demarcations (e.g., geographical divisions or market segments) and also across time. We propose an approach for generating coherent forecasts across both cross-sections and planning horizons for Australia. This results in significant improvements in forecast accuracy with substantial decision making benefits. Coherent forecasts help break intra- and inter-organisational information and planning silos, in a data driven fashion, blending information from different sources.This article also launches the Annals of Tourism Research Curated Collection on Tourism Demand Forecast, a special selection of research in this field.     

The combination of interval forecasts in tourism

Combination is an effective way to improve tourism forecasting accuracy. However, empirical evidence is limited to point forecasts. Given that interval forecasts can provide more comprehensive information, it is important to consider both point and interval forecasts for decision-making. Using Hong Kong tourism demand as an empirical case, this study is the first to examine if and how the combination can improve interval forecasting accuracy for tourism demand. Winkler scores are employed to measure interval forecasting performance. Empirical results show that combination improves the accuracy of tourism interval forecasting for different forecasting horizons. The findings provide government and industry practitioners with guidelines for producing accurate interval forecasts that benefit their policy-making for a wide array of applications in practice.This article also launches the Annals of Tourism Research Curated Collection on Tourism Demand Forecast, a special selection of research in this field.     

A Seasonal ARIMA Model of Tourism Forecasting: The Case of Taiwan

This paper aims to determine suitable SARIMA models to forecast the monthly outbound tourism departures of three major destinations from Taiwan to Hong Kong, Japan and the USA, respectively. The HEGY test is used to identify the deterministic seasonality in the data. The mean absolute per cent error (MAPE) is used to measure forecast accuracy. A low MAPE demonstrates the adequacy of the fitted SARIMA models. The results indicate that all series with first non-seasonal difference are needed to obtain the deterministic trend in outbound tourism series in Taiwan.     

Spatial-temporal forecasting of tourism demand

This study conducts spatial-temporal forecasting to predict inbound tourism demand in 29 Chinese provincial regions. Eight models are estimated among a-spatial models (autoregressive integrated moving average [ARIMA] model and unobserved component model [UCM]) and spatial-temporal models (dynamic spatial panel models and space-time autoregressive moving average [STARMA] models with different specifications of spatial weighting matrices). An ex-ante forecasting exercise is conducted with these models to compare their one-/two-step-ahead predictions. The results indicate that spatial-temporal forecasting outperforms the a-spatial counterpart in terms of average forecasting error. Auxiliary regression finds the relative error of spatial-temporal forecasting to be lower in regions characterized by a stronger level of local spatial association. Lastly, theoretical and practical implications are provided.This article also launches the Annals of Tourism Research Curated Collection on Tourism Demand Forecasting, a special selection of research in this field.     

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 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 of Hong Kong airport's passenger throughput

Hong Kong International Airport (HKIA) is one of the main gateways to Mainland China and the major aviation hub in Asia. An accurate airport traffic demand forecast allows for short and long-term planning and decision making regarding airport facilities and flight networks. This paper employs the Box–Jenkins Seasonal ARIMA (SARIMA) model and the ARIMAX model to forecast airport passenger traffic for Hong Kong, and projecting its future growth trend to 2015. Both models predict a steady growth in future airport passenger traffic at Hong Kong. In addition, scenario analysis suggests that Hong Kong airport's future passenger traffic will continue to grow in different magnitudes.     

Forecasting international tourism demand: a local spatiotemporal model

This study investigates whether tourism forecasting accuracy is improved by incorporating spatial dependence and spatial heterogeneity. One- to three-step-ahead forecasts of tourist arrivals were generated using global and local spatiotemporal autoregressive models for 37 European countries and the forecasting performance was compared with that of benchmark models including autoregressive moving average, exponential smoothing and Naïve 1 models. For all forecasting horizons, the two spatial models outperformed the non-spatial models. The superior forecasting performance of the local model suggests that the full reflection of spatial heterogeneity can improve the accuracy of tourism forecasting.     

Forecasting tourism demand: Developing a general nesting spatiotemporal model

This study proposes a general nesting spatiotemporal (GNST) model in an effort to improve the accuracy of tourism demand forecasts. The proposed GNST model extends the general nesting spatial (GNS) model into a spatiotemporal form to account for the spatial and temporal effects of endogenous and exogenous variables as well as unobserved factors. As a general specification of spatiotemporal models, the proposed model provides high flexibility in modelling tourism demand. Based on a panel dataset containing quarterly inbound visitor arrivals to 26 European destinations, this empirical study demonstrates that the GNST model outperforms both its non-spatial counterparts and spatiotemporal benchmark models. This finding confirms that spatial and temporal exogenous interaction effects contribute to improved forecasting performance.     

Are all tourism markets equal? Linkages between market-based tourism demand,quality of life,and economic development in Hong Kong

Tourism markets are heterogeneous, and their performance and effects can be better understood when considered separately. This paper investigates the linkages between tourism demand from several markets and quality of life, using Hong Kong as a case of study. The literature has, initially only considered a unilateral relationship running from aggregate tourism development to residents' quality of life, and a bilateral connection has only recently been recognized. The study contributes to the literature by considering a market-segmented (mainland China, Japan, the U.S., and other markets) approach to tourism demand, using a relatively underemphasized objectively-based method, and by providing building blocks for theoretical propositions. The methodology consists of unit root and cointegration testing, together with the application of the Three-Stage Least Squares method with the Seemingly Unrelated Regression approach on time-series data. The identified market-based differences can help academia and industry in better understanding the diverse markets and building a competitive edge.     

Forecasting occupancy rate with Bayesian compression methods

The curse of dimensionality is a challenge that researchers often face when dealing with large Vector Autoregressions (VARs). Different approaches have been proposed in the literature to address this issue. In this paper, we propose a new method based on the idea of compressed regression. In particular, we introduce two novel nonlinear compressed VARs to forecast the occupancy rate of hotels that compete within a narrow geographical area. We make the models more flexible through the introduction of neural networks, and compare their performance against several competing models. The empirical results show that the new compressed VARs outperform all other models, and their accuracy is preserved across nearly all forecast horizons from 1 to 36 months.This article also launches the Annals of Tourism Research Curated Collection on Tourism Demand Forecasting, a special selection of research in this field.     

基于人工神经网络的国际入境旅游需求的定量分析与预测--以日本对香港的国际旅游需求分析为例

旅游需求预测研究研究一直是旅游学研究的一个重要课题。本文尝试用人工神经网络模型的的3层BP模型来仿真模拟国际入境旅游需求，并以日本对香港的国际旅游需求为例进行模型验证。其输入层结点为SP、FR、POP、GDE、AH、MK，旅客量为输出节点，得出3层前馈反向传播神经网络模型。最后将模拟结果与目前常用的几种模型利用相同的数据源进行对比，最后发现人工神经网络模型模拟结果与目前常用的几种模型利用相同的数据源进行模拟的结果进行对比，最后发现人工神经网络模型的模拟结果与实际情况最为逼近。     

Tourism demand forecasting: A deep learning approach

Traditional tourism demand forecasting models may face challenges when massive amounts of search intensity indices are adopted as tourism demand indicators. Using a deep learning approach, this research studied the framework in forecasting monthly Macau tourist arrival volumes. The empirical results demonstrated that the deep learning approach significantly outperforms support vector regression and artificial neural network models. Moreover, the construction and identification of highly relevant features from the proposed deep network architecture provide practitioners with a means of understanding the relationships between various tourist demand forecasting factors and tourist arrival volumes.This article also launches the Annals of Tourism Research Curated Collection on Tourism Demand Forecasting, a special selection of research in this field     

旅游管理3M教学法的学生评价与效果分析

3M教学法是深圳旅游学院顺应旅游教育国际化的要求而提出的本科旅游教学改革模式。本文通过对 3M教学法实施情况的调查 ,以学生评价为基础对 3M教学模式的实施效果进行了分析 ,就进一步完善和提升 3M教学模式提出了建设性的创新策略     

Extending tourism competitiveness to human development

This study presents the Tourism Competitiveness Theory Hypothesis, anchored on a dynamic framework, and unifying the competitiveness theory with human development. The hypothesis rests on the recursive nature between tourism competitiveness and human development. The relationship was tested through a mixed-effect regression model in ten South American countries. The results suggest mutual reinforcement links mediated by the human development dimensions, especially health. The study also designed a typology model identifying four groups of countries that revealed distinct, non-linear behavioral patterns. Theoretical and managerial implications centered on the relevance of resource use, public allocation choices toward human development sectors, and the required sequencing to promote the mutual reinforcement nature embedded in the Tourism Competitiveness Theory Hypothesis.     

Symbolic regression for better specification

This note introduces the concept of symbolic regression (SR) to tourism and hospitality research. SR uses genetic programming to find the model that best fits the data without a need to pre-specify a functional form or to impose a certain model as a starting point. In other words, SR helps to uncover the intrinsic characteristics of the data at hand. Our view is that SR can serve as an improved method of testing for misspecification. In this note, we propose to derive the true functional form of the residual using SR. We then use this information to improve the forecasts of the linear regression model and, to perform hypothesis tests if needed.