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.     

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。     

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.     

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 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 demand with multisource big data

Based on internet big data from multiple sources (i.e., the Baidu search engine and two online review platforms, Ctrip and Qunar), this study forecasts tourist arrivals to Mount Siguniang, China. Key findings of this empirical study indicate that (a) tourism demand forecasting based on internet big data from a search engine and online review platforms can significantly improve forecasting performance; (b) compared with tourism demand forecasting based on single-source data from a search engine, demand forecasting based on multisource big data from a search engine and online review platforms demonstrates better performance; and (c) compared with tourism demand forecasting based on online review data from a single platform, forecasting performance based on multiple platforms is significantly better.     

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.     

Can multi-source heterogeneous data improve the forecasting performance of tourist arrivals amid COVID-19? Mixed-data sampling approach

The coronavirus disease (COVID-19) pandemic has already caused enormous damage to the global economy and various industries worldwide, especially the tourism industry. In the post-pandemic era, accurate tourism demand recovery forecasting is a vital requirement for a thriving tourism industry. Therefore, this study mainly focuses on forecasting tourist arrivals from mainland China to Hong Kong. A new direction in tourism demand recovery forecasting employs multi-source heterogeneous data comprising economy-related variables, search query data, and online news data to motivate the tourism destination forecasting system. The experimental results confirm that incorporating multi-source heterogeneous data can substantially strengthen the forecasting accuracy. Specifically, mixed data sampling (MIDAS) models with different data frequencies outperformed the benchmark models.     

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 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.     

Examining the link between meetings,incentive, exhibitions,and conventions (MICE) and tourism demand using generalized methods of moments (GMM): the case of Singapore

ABSTRACT

This study investigates the impact of meetings, incentive, exhibitions, and conventions (MICE) on tourism demand in Singapore over a period of 10 years (2003–2012). Past studies have shown that MICE matters a great deal to host destinations but researchers have rarely conducted any empirical research to verify the significance of this sector to tourism demand. Our study intends to fill the gap by using Difference and System generalized methods of moments (GMM) estimators for dynamic panel models. Tourism demand is measured by tourist arrivals from the top 30 origins, and the influence of real income of the tourist generating country and real exchange rate is also examined. The GMM results show a significant positive relationship between tourism demand and MICE (with international meetings as proxies). Additionally, the findings reveal that tourism demand growth is significantly positive (negative) with respect to changes in income (relative prices). The coefficient of lagged tourist arrivals indicates a high level of habit persistence and revisiting.     

Learning and Ability to Pay

Summary

The decision whether to use time series or econometric methods to forecast demand is not clear. The literature reviewed only indicates that models should be simple and ideally be able to evolve over time. In 1997 two models were proposed to forecast the numbers from Britain skiing in Europe. The first used a learning curve approach and forecast a stationary market, whilst the second used a Varying Coefficient Model linking sales and ability to pay and forecast a gradually expanding market. This paper reviews the outcomes 1996-2000, the forecast performance of the two models and the stability of the structure of both when updated. It unequivocally suggests that the learning curve approach produced better forecasts. In the penultimate section a model that attempts to combine both approaches is developed. In this context the role of “historic” data is discussed. The paper concludes that the best forecasting approach will depend upon whether the market is stable and that the weight given to data must reflect the information content of that data.     

Modelling and forecasting the demand for Hong Kong tourism

The main objectives of this paper are to identify the factors which contribute to the demand for Hong Kong tourism with the aid of econometric models and to generate forecasts of international tourism arrivals to Hong Kong for the period 2001–2008. The general-to-specific modelling approach is followed to model and forecast the demand for Hong Kong tourism by residents from the 16 major origin countries/regions and the empirical results reveal that the most important factors that determine the demand for Hong Kong tourism are the costs of tourism in Hong Kong, the economic condition (measured by the income level) in the origin countries/regions, the costs of tourism in the competing destinations and the ‘word of mouth’ effect. The demand elasticities and forecasts of tourism arrivals obtained from the demand models form the basis of policy formulations for the tourism industry in Hong Kong.     

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.     

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

In a context in which the tourism industry is jeopardised by the COVID-19 pandemic, and potentially by other pandemics in the future, the capacity to produce accurate forecasts is crucial to stakeholders and policy-makers. This paper attempts to forecast the recovery of tourism demand for 2021 in 20 destinations worldwide. An original scenario-based judgemental forecast based on the definition of a Covid-19 Risk Exposure index is proposed to overcome the limitations of traditional forecasting methods. Three scenarios are proposed, and ex ante forecasts are generated for each destination using a baseline forecast, the developed index and a judgemental approach. The limitations and potential developments of this new forecasting model are then discussed.     

Modelling Asian incoming tourism: A shift‐share approach

This paper provides insights into the relative competitive advantage of Asian regions in tourism. The study employs the shift‐share technique which decomposes the growth in tourist arrivals to selected receiving regions from different generating regions of the world over a prescribed time period. Each receiving region's performance will be compared to the overall performance of the area (i.e., aggregated benchmark). As a result of this comparing decomposition, the relative competitive advantage of each receiving region in attracting tourists can be determined. The results could be helpful to Asian decision makers trusted with the development and implementation of tourism strategies.     

Forecasting tourism demand with denoised neural networks

The automated Neural Network Autoregressive (NNAR) algorithm from the forecast package in R generates sub-optimal forecasts when faced with seasonal tourism demand data. We propose denoising as a means of improving the accuracy of NNAR forecasts via an application into forecasting monthly tourism demand for ten European countries. Initially, we fit NNAR models on both raw and denoised (with Singular Spectrum Analysis) tourism demand series, generate forecasts and compare the results. Thereafter, the denoised NNAR forecasts are also compared with parametric and nonparametric benchmark forecasting models. Contrary to the deseasonalising hypothesis, we find statistically significant evidence which supports the denoising hypothesis for improving the accuracy of NNAR forecasts. Thus, it is noise and not seasonality which hinders NNAR forecasting capabilities.     

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.     

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.