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The importance of accurate forecasts of tourism demand for managerial decision making is widely recognized (see, for example, Archer 1987), and this study examines the literature on the accuracy of tourism forecasts generated by different forecasting techniques. In fact, although there are many possible forecasting methods, in practice relatively few of these have been used for tourism forecasting.

This study aims to develop a robust long short-term memory (LSTM)-based forecasting model for daily international tourist arrivals at Incheon International Airport (ICN), incorporating multiple predictors including exchange rates, West Texas Intermediate (WTI) oil prices, Korea composite stock price index data and new COVID-19 cases. By leveraging deep learning techniques and diverse data sets, the research seeks to enhance the accuracy and reliability of tourism demand predictions, contributing significantly to both theoretical implications and practical applications in the field of hospitality and tourism.

This study introduces an innovative approach to forecasting international tourist arrivals by leveraging LSTM networks. This advanced methodology addresses complex managerial issues in tourism management by providing more accurate forecasts. The methodology comprises four key steps: collecting data sets; preprocessing the data; training the LSTM network; and forecasting future international tourist arrivals. The rest of this study is structured as follows: the subsequent sections detail the proposed LSTM model, present the empirical results and discuss the findings, conclusions and the theoretical and practical implications of the study in the field of hospitality and tourism.

This research pioneers the simultaneous use of big data encompassing five factors – international tourist arrivals, exchange rates, WTI oil prices, KOSPI data and new COVID-19 cases – for daily forecasting. The study reveals that integrating exchange rates, oil prices, stock market data and COVID-19 cases significantly enhances LSTM network forecasting precision. It addresses the narrow scope of existing research on predicting international tourist arrivals at ICN with these factors. Moreover, the study demonstrates LSTM networks’ capability to effectively handle multivariable time series prediction problems, providing a robust basis for their application in hospitality and tourism management.

This research pioneers the integration of international tourist arrivals, exchange rates, WTI oil prices, KOSPI data and new COVID-19 cases for forecasting daily international tourist arrivals. It bridges the gap in existing literature by proposing a comprehensive approach that considers multiple predictors simultaneously. Furthermore, it demonstrates the effectiveness of LSTM networks in handling multivariable time series forecasting problems, offering practical insights for enhancing tourism demand predictions. By addressing these critical factors and leveraging advanced deep learning techniques, this study contributes significantly to the advancement of forecasting methodologies in the tourism industry, aiding decision-makers in effective planning and resource allocation.

本研究旨在开发一种基于LSTM的强大预测模型, 用于预测仁川国际机场的日常国际游客抵达量, 结合多种预测因素, 包括汇率、WTI原油价格、韩国综合股价指数 (KOSPI) 数据和新冠疫情病例。通过利用深度学习技术和多样化数据集, 研究旨在提升旅游需求预测的准确性和可靠性, 对酒店与旅游领域的理论和实际应用有重要贡献。

本研究通过利用长短期记忆（LSTM）网络引入创新方法, 预测国际游客抵达量。这一先进方法解决了旅游管理中的复杂管理问题, 提供了更精确的预测。方法论包括四个关键步骤: (1) 收集数据集; (2) 数据预处理; (3) 训练LSTM网络; 以及 (4) 预测未来的国际游客抵达量。本文的其余部分结构如下：后续部分详细介绍了提出的LSTM模型, 呈现了实证结果, 并讨论了研究的发现、结论以及在酒店与旅游领域的理论和实际意义。

本研究首次同时使用包括国际游客抵达量、汇率、原油价格、股市数据和新冠疫情病例在内的大数据进行日常预测。研究显示, 整合汇率、原油价格、股市数据和新冠疫情病例显著增强了LSTM网络的预测精度。研究填补了现有研究在使用这些因素预测仁川国际机场国际游客抵达量的狭窄范围。此外, 研究证明了LSTM网络在处理多变量时间序列预测问题上的能力, 为其在酒店与旅游管理中的应用提供了坚实基础。

本研究首次将国际游客抵达量、汇率、WTI原油价格、KOSPI数据和新冠疫情病例整合到日常国际游客抵达量的预测中。它通过提出同时考虑多个预测因素的全面方法, 弥合了现有文献的差距。此外, 研究展示了LSTM网络在处理多变量时间序列预测问题方面的有效性, 为增强旅游需求预测提供了实用见解。通过处理这些关键因素并利用先进的深度学习技术, 本研究在旅游业预测方法的进步中做出了重要贡献, 帮助决策者进行有效的规划和资源配置。

Given the unique nature of business events tourism, this paper evaluates the forecasting performance of various models using search query data (SQD) to forecast convention attendance.

This research uses monthly and quarterly business event attendance data from both the U.S. (Las Vegas) and China (Macau) markets. Using SQD as the input, we evaluated and compared the cutting-edge forecasting models including Prophet and Long Short-Term Memory (LSTM).

The study reveals that Prophet outperforms complex neural network models in forecasting business event tourism demand. Keywords related to convention facilities, conventions or exhibitions, and transportation are proven to be useful in forecasting business travel demand.

Prophet is an accessible forecasting model for event-tourism practitioners, especially useful in the volatile business event tourism sector. Using verified search keywords in models helps understand traveler motivations and aids event planning.

Our study is among the first to empirically evaluate the performance of forecasting models for business travel demand. In comparison with other mainstream forecasting models, our study extends the scope to examine both the U.S. and Chinese markets.

Online reviews serve as valuable sources of information, reflecting tourists’ attentions, preferences and sentiments. However, although the existing research has demonstrated that incorporating online review data can enhance the performance of tourism demand forecasting models, the reliability of online review data and consumers’ decision-making process have not been given adequate attention. To address the aforementioned problem, the purpose of this study is to forecast tourism demand using online review data derived from the analysis of review helpfulness.

The authors propose a novel “identification-first, forecasting-second” framework. This framework prioritizes the identification of helpful reviews through a comprehensive analysis of review helpfulness, followed by the integration of helpful online review data into the forecasting system. Using the SARIMAX model with helpful online review data sourced from TripAdvisor, this study forecasts tourist arrivals in Hong Kong during the period from August 2012 to June 2019. The SNAÏVE/SARIMA model was used as the benchmark model. Additionally, artificial intelligence models including long short-term memory, back propagation neural network, extreme learning machine and random forest models were used to assess the robustness of the results.

The results demonstrate that online review data are subject to noise and bias, which can adversely affect the accuracy of predictions when used directly. However, by identifying helpful online reviews beforehand and incorporating them into the forecasting process, a notable enhancement in predictive performance can be realized.

First, to the best of the authors’ knowledge, this study is one of the first to focus on the data issue of online reviews on tourism arrivals forecasting. Second, this study pioneers the integration of the consumer decision-making process into the domain of tourism demand forecasting, marking one of the earliest endeavors in this area. Third, this study makes a novel attempt to identify helpful online reviews based on reviews helpfulness analysis.

The aim of this paper is to provide a narrative review of previous research on tourism demand modelling and forecasting and potential future developments.

A narrative approach is taken in this review of the current body of knowledge.

Significant methodological advancements in tourism demand modelling and forecasting over the past two decades are identified.

The distinct characteristics of the various methods applied in the field are summarised and a research agenda for future investigations is proposed.

本文旨在对先前关于旅游需求建模和预测的研究进行叙述性回顾并对未来潜在发展进行展望。

本文采用叙述性回顾方法对当前知识体系进行了评论。

本文确认了过去二十年旅游需求建模和预测方法论方面的重要进展。

本文总结了该领域应用的各种方法的独特特征, 并对未来研究提出了建议。

El objetivo de este documento es ofrecer una revisión narrativa de la investigación previa sobre modelización y previsión de la demanda turística y los posibles desarrollos futuros.

En esta revisión del marco actual de conocimientos sobre modelización y previsión de la demanda turística y los posibles desarrollos futuros,se adopta un enfoque narrativo.

Se identifican avances metodológicos significativos en la modelización y previsión de la demanda turística en las dos últimas décadas.

Se resumen las características propias de los diversos métodos aplicados en este campo y se propone una agenda de investigación para futuros trabajos.

Incorporating dynamic spatial effects exhibits considerable potential in improving the accuracy of forecasting tourism demands. This study aims to propose an innovative deep learning model for capturing dynamic spatial effects.

A novel deep learning model founded on the transformer architecture, called the spatiotemporal transformer network, is presented. This model has three components: the temporal transformer, spatial transformer and spatiotemporal fusion modules. The dynamic temporal dependencies of each attraction are extracted efficiently by the temporal transformer module. The dynamic spatial correlations between attractions are extracted efficiently by the spatial transformer module. The extracted dynamic temporal and spatial features are fused in a learnable manner in the spatiotemporal fusion module. Convolutional operations are implemented to generate the final forecasts.

The results indicate that the proposed model performs better in forecasting accuracy than some popular benchmark models, demonstrating its significant forecasting performance. Incorporating dynamic spatiotemporal features is an effective strategy for improving forecasting. It can provide an important reference to related studies.

The proposed model leverages high-frequency data to achieve accurate predictions at the micro level by incorporating dynamic spatial effects. Destination managers should fully consider the dynamic spatial effects of attractions when planning and marketing to promote tourism resources.

This study incorporates dynamic spatial effects into tourism demand forecasting models by using a transformer neural network. It advances the development of methodologies in related fields.

纳入动态空间效应在提高旅游需求预测的准确性方面具有相当大的潜力。本研究提出了一种捕捉动态空间效应的创新型深度学习模型。

本研究提出了一种基于变压器架构的新型深度学习模型, 称为时空变压器网络。该模型由三个部分组成：时空转换器、空间转换器和时空融合模块。时空转换器模块可有效提取每个景点的动态时间依赖关系。空间转换器模块可有效提取景点之间的动态空间相关性。提取的动态时间和空间特征在时空融合模块中以可学习的方式进行融合。通过卷积运算生成最终预测结果。

结果表明, 与一些流行的基准模型相比, 所提出的模型在预测准确性方面表现更好, 证明了其显著的预测性能。纳入动态时空特征是改进预测的有效策略。它可为相关研究提供重要参考。

所提出的模型利用高频数据, 通过纳入动态空间效应, 在微观层面上实现了准确预测。旅游目的地管理者在规划和营销推广旅游资源时, 应充分考虑景点的动态空间效应。

本研究通过使用变压器神经网络, 将动态空间效应纳入旅游需求预测模型。它推动了相关领域方法论的发展。

La incorporación de efectos espaciales dinámicos ofrece un considerable potencial para mejorar la precisión de la previsión de la demanda turística. Este estudio propone un modelo innovador de aprendizaje profundo para capturar los efectos espaciales dinámicos.

Se presenta un novedoso modelo de aprendizaje profundo basado en la arquitectura transformadora, denominado red de transformador espaciotemporal. Este modelo tiene tres componentes: el transformador temporal, el transformador espacial y los módulos de fusión espaciotemporal. El módulo transformador temporal extrae de manera eficiente las dependencias temporales dinámicas de cada atracción. El módulo transformador espacial extrae eficientemente las correlaciones espaciales dinámicas entre las atracciones. Las características dinámicas temporales y espaciales extraídas se fusionan de manera que se puede aprender en el módulo de fusión espaciotemporal. Se aplican operaciones convolucionales para generar las previsiones finales.

Los resultados indican que el modelo propuesto obtiene mejores resultados en la precisión de las previsiones que algunos modelos de referencia conocidos, lo que demuestra su importante capacidad de previsión. La incorporación de características espaciotemporales dinámicas supone una estrategia eficaz para mejorar las previsiones. Esto puede proporcionar una referencia importante para estudios afines.

El modelo propuesto aprovecha los datos de alta frecuencia para lograr predicciones precisas a nivel micro incorporando efectos espaciales dinámicos. Los gestores de destinos deberían tener plenamente en cuenta los efectos espaciales dinámicos de las atracciones en la planificación y marketing para la promoción de los recursos turísticos.

Este estudio incorpora efectos espaciales dinámicos a los modelos de previsión de la demanda turística mediante el empleo de una red neuronal transformadora. Supone un avance en el desarrollo de metodologías en campos afines.

This paper aims to analyze the research highlights, evolutionary process and future research directions in the field of tourism forecasting.

This study used CiteSpace to conduct a bibliometric analysis of 1,213 tourism forecasting articles.

The results show that tourism forecasting research has experienced three stages. The institutional collaboration includes transnational collaboration and domestic institutional collaboration. Collaboration between countries still needs to be strengthened. The authors’ collaboration is mainly based on on-campus collaboration. Articles with high co-citation are primarily published in core tourism journals and other relevant publications. The research content mainly pertains to tourism demand, revenue management, hotel demand and tourist volumes. Ex ante forecasting during the COVID-19 pandemic has broadened existing tourism forecasting research. The future forecasting research focuses on the rational use of big data, improving the accuracy of models and enhancing the credibility of forecasting results.

This paper uses CiteSpace to analyze tourism forecasting articles to obtain future research trends, which supplements existing research and provides directions for future research.

本文旨在分析旅游预测领域的研究重点、演化过程和未来的研究方向。

本研究使用 CiteSpace 软件对 1213 篇旅游预测文章进行了文 献计量学分析。

结果表明, 旅游预测研究经历三个阶段。机构合作包含国际机构合作和 国内机构合作, 需要持续加强国家之间的合作, 作者之间的合作多以校内合作为 主。高引用文章不仅发表在旅游领域的核心期刊还发表在其他专业的核心期刊上。 旅游预测研究的主要内容为旅游需求、收入管理、酒店需求和游客量。新冠疫情 期间的事前预测拓宽了现有的旅游预测研究。未来预测的研究重点在于合理利用 大数据, 提高模型的准确定以及提高预测结果的可信度。

本文使用 CiteSpace 分析旅游预测文章得到未来研究趋势, 既是对 现有研究的补充, 又为今后的研究提供方向。

Este artículo pretende analizar los aspectos más destacados de la investigación, el proceso evolutivo y las futuras orientaciones de la investigación en el campo de la previsión turística.

Este estudio utilizó CiteSpace para realizar un análisis bibliométrico de 1213 artículos sobre previsión turística.

Los resultados muestran que la investigación sobre previsión turística ha experimentado tres etapas. La colaboración institucional incluye la colaboración transnacional y la colaboración institucional nacional. La colaboración entre países aún debe reforzarse. La colaboración entre autores se basa principalmente en la colaboración dentro del campus. Los artículos con una alta cocitación se publican principalmente en las principales revistas de turismo y en otras publicaciones relevantes. El contenido de la investigación se refiere principalmente a la demanda turística, el revenue management, la demanda hotelera y los volúmenes turísticos. La previsión previa y durante la pandemia de la COVID-19 ha ampliado la investigación existente sobre previsión turística. La futura investigación sobre previsiones se centra en el uso racional de los big data, la mejora de la precisión de los modelos y el aumento de la credibilidad de los resultados de las previsiones.

Este artículo utiliza CiteSpace para analizar artículos de previsión turística con el fin de obtener futuras tendencias de investigación, lo que complementa la investigación existente y proporciona orientaciones para futuras investigaciones.

The purpose of this paper is to review recent studies published from 2007 to 2015 on tourism and hotel demand modeling and forecasting with a view to identifying the emerging topics and methods studied and to pointing future research directions in the field.

Articles on tourism and hotel demand modeling and forecasting published mostly in both science citation index and social sciences citation index journals were identified and analyzed.

This review finds that the studies focused on hotel demand are relatively less than those on tourism demand. It is also observed that more and more studies have moved away from the aggregate tourism demand analysis, whereas disaggregate markets and niche products have attracted increasing attention. Some studies have gone beyond neoclassical economic theory to seek additional explanations of the dynamics of tourism and hotel demand, such as environmental factors, tourist online behavior and consumer confidence indicators, among others. More sophisticated techniques such as nonlinear smooth transition regression, mixed-frequency modeling technique and nonparametric singular spectrum analysis have also been introduced to this research area.

The main limitation of this review is that the articles included in this study only cover the English literature. Future review of this kind should also include articles published in other languages. The review provides a useful guide for researchers who are interested in future research on tourism and hotel demand modeling and forecasting.

This review provides important suggestions and recommendations for improving the efficiency of tourism and hospitality management practices.

The value of this review is that it identifies the current trends in tourism and hotel demand modeling and forecasting research and points out future research directions.

This paper aims to compare the forecasting performance of different models with and without big data predictors from search engines and social media.

Using daily tourist arrival data to Mount Longhu, China in 2018 and 2019, the authors estimated ARMA, ARMAX, Markov-switching auto-regression (MSAR), lasso model, elastic net model and post-lasso and post-elastic net models to conduct one- to seven-days-ahead forecasting. Search engine data and social media data from WeChat, Douyin and Weibo were incorporated to improve forecasting accuracy.

Results show that search engine data can substantially reduce forecasting error, whereas social media data has very limited value. Compared to the ARMAX/MSAR model without big data predictors, the corresponding post-lasso model reduced forecasting error by 39.29% based on mean square percentage error, 33.95% based on root mean square percentage error, 46.96% based on root mean squared error and 45.67% based on mean absolute scaled error.

Results highlight the importance of incorporating big data predictors into daily demand forecasting for tourism attractions.

This study represents a pioneering attempt to apply the regularized regression (e.g. lasso model and elastic net) in tourism forecasting and to explore various daily big data indicators across platforms as predictors.

Given that the use of Google Trends data is helpful to improve forecasting performance, this study aims to investigate whether the precision of forecast combination can benefit from the use of Google Trends Web search index along with the encompassing set.

Grey prediction models generate single-model forecasts, while Google Trends index serves as an explanatory variable for multivariate models. Then, three combination sets, including sets of univariate models (CUGM), all constituents (CAGM) and constituents that survive the forecast encompassing tests (CSET), are generated. Finally, commonly used combination methods combine the individual forecasts for each combination set.

The tourism volumes of four frequently searched-for cities in Taiwan are used to evaluate the accuracy of three combination sets. The encompassing tests show that multivariate grey models play a role to be reckoned with in forecast combinations. Furthermore, the empirical results indicate the usefulness of Google Trends index and encompassing tests for linear combination methods because linear combination methods coupled with CSET outperformed that coupled with CAGM and CUGM.

With Google Trends Web search index, the tourism sector may benefit from the use of linear combinations of constituents that survive encompassing tests to formulate business strategies for tourist destinations. A good forecasting practice by estimating ex ante forecasts post-COVID-19 can be further provided by scenario forecasting.

To improve the accuracy of combination forecasting, this research verifies the correlation between Google Trends index and combined forecasts in tourism along with encompassing tests.

過去的研究顯示 Google 搜尋趨勢資料有助於改善旅遊需求預測的準確度，本研究就此進一步探討 Google 搜尋趨勢網頁搜尋指標與涵蓋性檢定的使用對於組合預測準確度所造成的影響。

本研究以 Google 搜尋趨勢指標做為多變量灰色預測模式的解釋變數，並以單變量與多變量灰色模式產生各別預測值。在分別產生由所有的單變量模式 (CUGM)所有的模式 (CAGM), 以及經過涵蓋性檢定所留存下來之模式 (CSET) 所組成之集合後，就各別的組合集以常用的組合方法產生預測值。

以台灣的四個熱搜旅遊城市的旅遊人數進行三個組合集的預測準確度分析。涵蓋性檢定顯示多變量灰色模式在組合預測中扮演重要的角色，而結果亦呈現線性組合方法在 CSET優於在 CUGM 與 CAGM 的準確度，突顯搜尋趨勢指標與涵蓋性檢定對於線性組合方法的有用性。

藉由 Google 搜尋趨勢網頁搜尋指標與涵蓋性檢定，旅遊部門應可透過線性組合方法的預測規劃旅遊目的地的經營策略。新冠疫情下於各季的事前預測亦可結合情境預測具體呈現。

為提升組合預測在旅遊需求的預測準確度，本研究結合涵蓋性檢定以分析 Google 搜尋趨勢指標與組合預測準確度之間的關聯性。

旅遊需求,涵蓋性檢定,Google 搜尋趨勢,灰色預測,組合預測

研究型论文

Dado que el uso de los datos de Google Trends es útil para mejorar la precisión de las predicciones, este estudio examina si el uso del índice de búsqueda web de Google Trends combinado con la agregación de relevancia puede mejorar la precisión del predictor.

El modelo predictivo gris genera predicciones bajo un único modelo, mientras que el modelomultivariado utiliza el indicador Google Trends como variable explicativa. Se generaron tresensamblajes generales, incluido el Modelo armónico único (CUGM), los ensamblajes de todos loscomponentes (CAGM) y la prueba de presencia de componentes con predicción (CSET). Laspredicciones individuales encada grupo luego se combinan utilizando métodos de correlación deuso común.

Utilizando el número de turistas en las cuatro ciudades más investigadas de Taiwán, los tresgrupos combinados se clasificaron según su precisión. Las pruebas incluidas muestran que losmodelos multivariados en escala de grises son importantes para la predicción. Además, losresultados de las pruebas muestran que el índice de Google Trends y las pruebas que incluyenmétodos de suma lineal son útiles porque los métodos combinados con CSET funcionan majorque los métodos combinados con CSET. CAGM y VCUG.

La industria de viajes puede usar el índice de búsqueda web de Google Trends para desarrollarestrategias comerciales para atracciones basadas en un conjunto lineal de componentes.

Con el objetivo de mejorar la precisión de los pronósticos agregados, este estudio investiga larelación entre el índice de tendencias de Google y las expectativas generales de viaje junto con laevidencia global.

Demanda de viajes, Experiencia global, Tendencias de Google, Predicción gris

Trabajo de investigación