Tourism demand forecasting with time series imaging: A deep learning model

To leverage computer vision technology to improve the accuracy of tourism demand forecasting, a model based on deep learning with time series imaging is proposed. The model consists of three parts: sequence image generation, image feature extraction, and model training. In the first part, the tourism demand data are encoded into images. In the second part, the convolution and pooling layers are used to extract features from the obtained images. In the final part, the extracted features are input into long short-term memory networks. Based on historical tourism demand data, the model for forecasting future tourism demand can be obtained. The performance of the proposed model is experimentally assessed through comparing against seven benchmark models.     

Forecasting Chinese cruise tourism demand with big data: An optimized machine learning approach

After more than ten years of exponential development, the growth rate of cruise tourist in China is slowing down. There is increasingly financial risk of investing in homeports, cruise ships and promotional activities. Therefore, forecasting Chinese cruise tourism demand is a prerequisite for investment decision-making and planning. In order to enhance forecasting performance, a least squares support vector regression model with gravitational search algorithm (LSSVR-GSA) is proposed for forecasting cruise tourism demand with big data, which are search query data (SQD) from Baidu and economic indexes. In the proposed model, hyper-parameters of the LSSVR model are optimized with GSA. By comparing these models with various settings, we find that LSSVR-GSA with selected mobile keywords and economic indexes can achieve the highest forecasting performance. The results indicate the proposed framework of the methodology is effective and big data can be helpful predictors for forecasting Chinese cruise tourism demand.     

Forecasting tourist arrivals using denoising and potential factors

Precise tourist demand forecasting is crucial owing to its relevance in tourism decision-making. This study proposes a novel model for tourist demand forecasting on the basis of denoising and potential factors. The denoising strategy is proposed to improve the tourist demand forecasting, and an effective and promising denoising method based on Hilbert–Huang transform is developed. Two case studies are conducted to verify the validity and predictability of the proposed model. Results indicate that denoising remarkably improves the forecasting accuracy, and the proposed denoising technique outperforms other approaches. Furthermore, the proposed model exhibits the most satisfactory forecasting performance among all benchmark models, as well as excellent scalability and stability.     

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.     

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.     

Visitor arrivals forecasts amid COVID-19: A perspective from the Asia and Pacific team

It is important to provide scientific assessments concerning the future of tourism under the uncertainty surrounding COVID-19. To this purpose, this paper presents a two-stage three-scenario forecast framework for inbound-tourism demand across 20 countries. The main findings are as follows: in the first-stage ex-post forecasts, the stacking models are more accurate and robust, especially when combining five single models. The second-stage ex-ante forecasts are based on three recovery scenarios: a mild case assuming a V-shaped recovery, a medium one with a V/U-shaped, and a severe one with an L-shaped. The forecast results show a wide range of recovery (10%–70%) in 2021 compared to 2019. This two-stage three-scenario framework contributes to the improvement in the accuracy and robustness of tourism demand 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。     

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.     

Knowledge mapping of tourism demand forecasting research

Utilizing a scientometric review of global trends and structure from 388 bibliographic records over two decades (1999–2018), this study seeks to advance the building of comprehensive knowledge maps that draw upon global travel demand studies. The study, using the techniques of co-citation analysis, collaboration network and emerging trends analysis, identified major disciplines that provide knowledge and theories for tourism demand forecasting, many trending research topics, the most critical countries, institutions, publications, and articles, and the most influential researchers. The increasing interest and output for big data and machine learning techniques in the field were visualized via comprehensive knowledge maps. This research provides meaningful guidance for researchers, operators and decision makers who wish to improve the accuracy of tourism demand forecasting.     

A decomposition-ensemble approach for tourism forecasting

With the frequent occurrence of irregular events in recent years, the tourism industry in some areas, such as Hong Kong, has suffered great volatility. To enhance the predictive accuracy of tourism demand forecasting, a decomposition-ensemble approach is developed based on the complete ensemble empirical mode decomposition with adaptive noise, data characteristic analysis, and the Elman's neural network model. Using Hong Kong tourism demand as an empirical case, this study firstly investigates how data characteristic analysis is used in a decomposition-ensemble approach. The empirical results show that the proposed model outperforms other models in both point and interval forecasts for different prediction horizons, indicating the effectiveness of the proposed approach for forecasting tourism demand, especially for time series with complexity.     

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.     

社会交往、旅游情境对旅游需求的影响研究--基于春节“黄金周”的实证分析

以往关于消费者需求行为的研究多基于传统经济学框架的设定展开,认为影响消费者旅游需求的主要因素包含收入、目的地吸引力、交通等,普遍忽视了消费过程中的社会交往和具体情境。文章在理论分析的基础上,提出了若干命题,将消费者收入、社会交往和旅游情境等因素纳入旅游消费者需求的模型中,提出了若干研究命题。依据2014年春节“黄金周”旅游需求调研数据,采用排序选择模型验证了相关命题的正确性。计量结果表明,个人经济因素对我国消费者春节“黄金周”旅游需求影响较小,家庭结构、同伴等社会交往因素是影响其旅游需求的重要因素;我国消费者对于春节“黄金周”出游过程中遭遇的拥堵、旅游市场混乱等旅游情境问题具有一定的容忍度,但是,严重供需失衡导致的旅游情境问题依然对其旅游需求产生了显著的影响。     

Modelling the interdependence of tourism demand: The global vector autoregressive approach

This study develops a global vector autoregressive (global VAR or GVAR) model to quantify the cross-country co-movements of tourism demand and simulate the impulse responses of shocks to the Chinese economy. The GVAR model overcomes the endogeneity and over-parameterisation issues found in many tourism demand models. The results show the size of co-movements in tourism demand across 24 major countries in different regions. In the event of negative shocks to China’s real income and China’s tourism price variable, almost all of these countries would face fluctuations in their international tourism demand and in their tourism prices in the short run. In the long run, developing countries and China’s neighbouring countries would tend to be more negatively affected than developed countries.     

Modeling domestic holiday tourism demand in Australia: Problems and solutions

An econometric model is very useful for understanding the underlying relationship between tourism demand and economic variables such as income and travel prices. However, a long time series horizon of data is essential to run an econometric model that is consistent with economic theory. Although time series data on the number of domestic trips and visitor nights in Australia are available since 1978–79, breaks in the time series in different years have made it difficult to estimate a domestic holiday demand model. It is because the data series in different periods are not directly comparable. In this study, a simple data adjustment technique has been used to obtain comparable data series. Among several econometric demand models, a single equation multivariate time series demand model in a double log linear functional form was found to be the most appropriate and practical model to estimate and analyze the demand parameters of domestic holiday travel in Australia. However, the model with variables in level terms was observed having the “spurious regression problem” which has been corrected using the cointegration and error correction mechanisms. The estimated income and price elasticity of domestic holiday travel demand are consistent with economic theory and therefore can be used for forecasting and other purposes.     

Predictivity of tourism demand data

As tourism researchers continue to search for solutions to determine the best possible forecasting performance, it is important to understand the maximum predictivity achieved by models, as well as how various data characteristics influence the maximum predictivity. Drawing on information theory, the predictivity of tourism demand data is quantitatively evaluated and beneficial for improving the performance of tourism demand forecasting. Empirical results from Hong Kong tourism demand data show that 1) the predictivity could largely help the researchers estimate the best possible forecasting performance and understand the influence of various data characteristics on the forecasting performance.; 2) the predictivity can be used to assess the short effect of external shock — such as SARS over tourism demand forecasting.     

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.     

The demand for tourism in North East England with special reference to Northumbria: an empirical analysis

This paper examines the trends of tourism in North East England. In particular, we focus on the area of Northumbria to show the potential of tourism for economic development in the region. Within this analysis, we concentrate on the demand for tourism in the region, and in particular we are concerned with tourism by UK residents in Northumbria (‘domestic’ tourism). The long run relationship between domestic tourism demand, and a number of economic factors effecting this demand, is considered using the Johansen and Juselius (1990, 1992) Multivariate Cointegration analysis. An error-correction model is then proposed for short-run forecasting of domestic demand for tourism in Northumbria.     

Multi-attraction,hourly tourism demand forecasting

Forecasting tourism demand for multiple tourist attractions on an hourly basis provides important insights for effective and efficient management, such as staffing and resource optimization. However, existing forecasting models are not well equipped to hand the hourly data, which is dynamic and nonlinear. This study develops an improved, artificial intelligent-based model, known as Correlated Time Series oriented Long Short-Term Memory with Attention Mechanism, to solve this problem. The validity of the model is verified through a forecasting exercise for 77 attractions in Beijing, China. The results show that our model significantly outperforms the baseline models. The study advances the tourism demand forecasting literature and offers practical implications for resource optimization while enhancing staff and customer satisfaction.     

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.     

Tourism demand for Italy and the business cycle

This study provides a strategy for modelling the effect of the business cycle on tourism demand under the rationale that tourism cycles are heavily influenced by lagged effects of the overall business cycle. Using quarterly data on overnight stays in Italian hotels, both domestic and inbound between 1985 and 2004, we adopt a structural time series approach to evaluate two alternative models, the first with a latent cycle component (LCC) and the second based on specific economic explanatory variables (XCV). The two models are compared in terms of explanatory power, best-fit, residual diagnostics and forecasting ability. The results show similar performances. The policy implication is that the XCV model can be used for calibrating countercyclical interventions in tourism policy.