Forecasting tourism: a combined approach

In this article, we employ a combined seasonal nonseasonal ARIMA and sine wave nonlinear regression forecast model to predict international tourism arrivals, as represented by the number of world-wide visitors to Singapore. Compared with a similar study of the accuracy of international tourist arrivals forecasts by Chan (Journal of Travel Research, 1993, 31, 58–60)¹ and Chu (Journal of Travel Research, 1998, 36, 79–84)² using other univariate time series models, our proposed model has the smallest mean absolute percentage error.     

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.     

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.     

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。     

Vulnerability and resilience of the tourism sector in India: Effects of natural disasters and internal conflict

Tourism resilience studies often focus on a single shock event. In reality, the same destination may face different kinds of shocks. It is important to compare the relative effect and resilience to different shocks.Using a panel dataset for 22 Indian states, we build random effect models to understand the impact of natural disasters and political conflict on domestic and foreign tourist arrivals. Severe conflict events affect domestic tourist arrivals negatively, while natural disasters do not. In contrast, natural disasters affect international tourist arrivals negatively but conflicts do not.We study resilience by identifying breaks in tourist arrivals and noting corresponding recovery times. Breaks were observed in more states for the international segment compared to domestic segment. Recovery times was also greater for international rather than domestic tourists. Thus domestic tourists seem to be more resilient compared to international tourists. Our study provides useful insights that may have policy implications.     

Cross country relations in European tourist arrivals

This paper introduces an optimized Multivariate Singular Spectrum Analysis (MSS) algorithm for identifying leading indicators. Exploiting European tourist arrivals data, we analyse cross country relations for European tourism demand. Cross country relations have the potential to aid in planning and resource allocations for future tourism demand by taking into consideration the variation in tourist arrivals across other countries in Europe. Our findings indicate with statistically significant evidence that there exists cross country relations between European tourist arrivals which can help in improving the predictive accuracy of tourism demand. We also find that MSSA has the capability of not only identifying leading indicators, but also forecasting tourism demand with far better accuracy in comparison to its univariate counterpart, Singular Spectrum Analysis.     

Terrorism,Seasonality, and International Air Tourist Arrivals in Central Florida

The Orlando International Airport (OIA) is growing in its importance in the regional tourism of Central Florida. This study provides some explanation for the fast growth of the OIA international passenger traffic by developing a multiple regression model. Five variables are identified as significantly related to the passenger arrivals at the OIA. The positive relationship between the economic performance of other industrialized countries and the OIA international arrivals is consistence with the hypothesis that income is positive determinant in travel decision. The increasing hijacking incidents in Europe and the Middle East is found to have a destination substitution effect between Orlando and European/Middle East. Hijacking may have encouraged Canadian and European tourist to switch from European/Middle East destinations to the United States in general, and to Orlando in particular. The composite tourism supply variable, represented by the number of Orlando hotel/model rooms, is found to be positively related to the OIA international arrivals. The two dummy variables of seasonality represent the natural component of the tourism supply of Orlando, its pleasant winter and early spring sunshine. The model indicates that this natural factor contributes significantly to the international passenger arrivals.     

The good,the bad and the ugly on COVID-19 tourism recovery

This paper is to produce different scenarios in forecasts for international tourism demand, in light of the COVID-19 pandemic. By implementing two distinct methodologies (the Long Short Term Memory neural network and the Generalized Additive Model), based on recent crises, we are able to calculate the expected drop in the international tourist arrivals for the next 12 months. We use a rolling-window testing strategy to calculate accuracy metrics and show that even though all models have comparable accuracy, the forecasts produced vary significantly according to the training data set, a finding that should be alarming to researchers. Our results indicate that the drop in tourist arrivals can range between 30.8% and 76.3% and will persist at least until June 2021.     

Visitor arrivals forecasts amid COVID-19: A perspective from the Africa team

COVID-19 disrupted international tourism worldwide, subsequently presenting forecasters with a challenging conundrum. In this competition, we predict international arrivals for 20 destinations in two phases: (i) Ex post forecasts pre-COVID; (ii) Ex ante forecasts during and after the pandemic up to end 2021. Our results show that univariate combined with cross-sectional hierarchical forecasting techniques (THieF-ETS) outperform multivariate models pre-COVID. Scenarios were developed based on judgemental adjustment of the THieF-ETS baseline forecasts. Analysts provided a regional view on the most likely path to normal, based on country-specific regulations, macroeconomic conditions, seasonal factors and vaccine development. Results show an average recovery of 58% compared to 2019 tourist arrivals in the 20 destinations under the medium scenario; severe, it is 34% and mild, 80%.     

Using a Grey–Markov model optimized by Cuckoo search algorithm to forecast the annual foreign tourist arrivals to China

With the rapid development of the international tourism industry, it has been a challenge to forecast the variability in the international tourism market since the 2008 global financial crisis. In this paper, a novel CMCSGM(1, 1) forecasting model is proposed to address how forecasting precision is affected by the volatility of the tourism market. The Markov-chain grey model is adopted for its emphasis on the small-sample observations and exponential distribution samples. Additionally, the optimal input subset method and the Cuckoo search optimization algorithm are applied to improve the performance of the Markov-chain grey model. The experimental study of the forecasting of the annual foreign tourist arrivals to China indicates that the proposed CMCSGM(1, 1) model is considerably more efficient and accurate than the conventional MCGM(1, 1) models.     

The Determinants of the Travel Demand on International Tourist Arrivals to Thailand

The purpose of this study is to develop a travel demand model of international tourist arrivals to Thailand and to assess the impact of crisis incidents on Thailand's tourism industry. A 20-year (1987–2006) annual time series data of “number of international tourist arrivals”, “exchange rate”, “promotion budget”, and dummy variables of “Asia financial crisis”, “special promotional campaigns”, “SARS” and “tsunami” were used to develop the travel demand model by performing a multiple regression analysis. The results showed that travel demand of international tourist arrivals to Thailand could be explained by “exchange rate”, “promotion budget”, “Asia financial crisis” and “SARS”.     

Modelling and forecasting daily international mass tourism to Peru

Peru is a South American country that is divided into two parts by the Andes Mountains. The rich historical, cultural and geographic diversity has led to the inclusion of ten Peruvian sites on UNESCO's World Heritage List. For the potentially negative impacts of mass tourism on the environment, and hence on future international tourism demand, to be managed appropriately require modelling growth rates and volatility adequately. The paper models the growth rate and volatility (or the variability in the growth rate) in daily international tourist arrivals to Peru from 1997 to 2007. The empirical results show that international tourist arrivals and their growth rates are stationary, and that the estimated symmetric and asymmetric conditional volatility models all fit the data extremely well. Moreover, the estimates resemble those arising from financial time series data, with both short and long run persistence of shocks to the growth rate in international tourist arrivals.     

Tourism demand forecasting with online news data mining

This study empirically tests the role of news discourse in forecasting tourist arrivals by examining Hong Kong. It employs structural topic modeling to identify key topics and their meanings related to tourism demand. The impact of the extracted news topics on tourist arrivals is then examined to forecast tourism demand using the seasonal autoregressive integrated moving average with the selected news topic variables method. This study confirms that including news data significantly improves forecasting performance. Our forecasting model using news topics also outperformed the others when the destination was experiencing social unrest at the local level. These findings contribute to tourism demand forecasting research by incorporating discourse analysis and can help tourism destinations address various externalities related to news media.     

The impact of the Asian financial crisis on Japanese demand for travel to Hong Kong: A study of various forecasting techniques

The Asian financial crisis has drawn worldwide attention because of its significant economic impact on local economics, especially on the economy of a tourism‐dependent destination. Unfortunately, there have been very few articles about the relationship of the Asian financial crisis and tourism demand forecasting. This relative lack of prior studies on the Asian financial crisis and tourism demand forecasting is particularly true in the context of Hong Kong. This article reports on a study that utilized officially published data to test the accuracy of forecasts of Japanese demand for travel to Hong Kong, measured in terms of the number of Japanese tourist arrivals. Seven commonly‐used tourism forecasting techniques were used to determine the forecasting accuracy. The quality of forecasting accuracy was measured in five dimensions. Experimental results indicated mixed results in terms of forecasting accuracy. Overall, artificial neural network outperformed other techniques in three of the five dimensions.     

Forecasting in a Mixed Up World: Nowcasting Hawaii Tourism

We evaluate the short term forecasting performance of methods that systematically incorporate high frequency information via covariates. Our study provides a thorough introduction of these methods to the tourism literature. We highlight the distinguishing features and limitations of each tool and evaluate their forecasting performance in two tourism-specific applications. The first uses monthly indicators to predict quarterly tourist arrivals to Hawaii; the second predicts quarterly labor income in the accommodations and food services sector. Our results indicate that compared to the exclusive use of low frequency aggregates, including timely intra-period data in the forecasting process results in significant gains in predictive accuracy. Anticipating growing popularity of these techniques among empirical analysts, we present practical implementation guidelines to facilitate their adoption.     

Visiting probability model: a new method for tourist volume forecasting

ABSTRACT

Tourist volume forecasting is an ongoing theme in tourism research. Current methods rely too much on the previous tourist arrivals data. Based on tourism system perspective, we propose a visiting probability model composed of five independent variables: the attractiveness of a destination, the travel time from a origin to the destination, the traffic expense to and from the destination, the physical fatigue travel time and the per capita disposable monthly income of the origin. The model provides a new method for forecasting the number of tourists from a specific origin without historical tourist arrivals data.     

Forecasting US Air Travelers to Europe,Caribbean and Asia

Travel and tourism are among the most important economic contributors to most, if not all, countries. According to the World Tourism Organization (WTO), the number of international arrivals showed remarkable growth, from 25 million international arrivals in 1950 to 699 million in 2000. This indicates an average annual growth rate of 7%. In the same period, tourism receipts recorded an average annual growth rate of 11%. In 2002, international tourism generated worldwide receipts of US$474 billion, corresponding to US$1.3 billion a day or US$675 per tourist arrival. In view of the important role that international tourism plays in the global economy, this research applied four time-series forecasting techniques to project the trend of US air travelers, a major source market, to Europe, Caribbean and Asia, the three leading outbound markets (second to Canada and Mexico) from the US in the period 2003–2005. Experimental outcomes reveal declining trends. This paper discusses the underlying factors for such trends. Lastly, this paper ends with a detailed discussion on policy implications.     

Identifying a destination’s optimal tourist market mix: Does a superior portfolio model exist?

Extant tourism research has used various portfolio model types to determine optimal tourist market mixes which simultaneously maximize total tourist expenditure and minimise the instability of international inbound tourism demand. We analyse the three portfolio models that have been applied in the tourism literature: two varieties of a levels model (that use the level of tourist arrivals, or bed nights to quantify tourist activity) and a growth rates model (that deploys the growth in the level of tourist activity). Applying these models using per capita expenditure in four distinctively different destination countries (Australia, Greece, Japan, and USA), we demonstrate that the Levels Model 1 is superior to the Levels Model 2 and the Growth Rates Model. It produces solutions that provide noticeably higher tourist expenditure with less instability of international tourism demand than the status quo. Theoretical contributions and practical implications for tourism policy makers and destination marketers are discussed.     

Evolution of international tourist flows from 1995 to 2018: A network analysis perspective

Tourist arrivals and tourism revenues have been extensively studied to evaluate international tourist flows, whereas the structure and evolution of these flows have received less attention. Based on international tourist arrival data from 221 countries/regions during the period 1995–2018, this study applies network analysis to explore the structure and evolution of international tourist flows, and the roles and functions of countries/regions in the international tourist flow network. The results of this study reveal that the network density of international tourist flows is increasing. Countries/regions in Europe, East Asia and North America generally occupy a significantly important position within the international tourist flow network, especially Germany and China. Those geographically close countries/regions demonstrate the same or similar roles and positions in international tourism. This study has significant implications for tourist destination management and marketing.