Forecasting tourism demand with composite search index

Li, Xin; Pan, Bing; Law, Rob; Huang, Xiankai

doi:10.1016/j.tourman.2016.07.005

Cited by 285 publications

(249 citation statements)

References 42 publications

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“…On the other hand, existing non-linear prediction methods are difficult to adapt to the increasing experimental data, and unable to extract feature information automatically, affecting the forecasting accuracy. With the all-round development of the Internet, a large amount of online query index generated by the consumer information search provides a new direction for an overnight traffic forecasting of tourist hotels [3]. This study addresses the aforementioned problems and expands the previous research by introducing appropriate nonlinear forecasting methods and constructing deep learning (DL) forecasting frameworks.…”

Section: Introductionmentioning

confidence: 99%

Forecasting Hotel Accommodation Demand Based on LSTM Model Incorporating Internet Search Index

Zhang

Wang

et al. 2019

Sustainability

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Accurate forecasting of the hotel accommodation demands is extremely critical to the sustainable development of tourism-related industries. In view of the ever-increasing tourism data, this paper constructs a deep learning framework to handle the prediction problem in the hotel accommodation demands. Taking China's Hainan province as an empirical example, the internet search index is used from August 2008 to May 2019 to forecast the overnight passenger flows for hotels accommodation in Hainan Province, China. Forecasting results indicate that compared to benchmark models, the constructed forecasting method can effectively simulate dynamic characteristics of the overnight passenger flows for the hotel accommodation and significantly improve the forecasting performance of the model. Forecasting results can provide necessary references for decision-making in tourism-related industries, and this forecasting framework can also be extended to other similar complex time series forecasting problems.

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Section: Introductionmentioning

confidence: 99%

Forecasting Hotel Accommodation Demand Based on LSTM Model Incorporating Internet Search Index

Zhang

Wang

et al. 2019

Sustainability

View full text Add to dashboard Cite

show abstract

“…Most publications fall under the healthcare (see for example Ginsberg et al, 2009;Yang et al, 2011;Dehkordy et al, 2014) and economy (see for example Choi & Varian, 2009ª;Schmidt & Vosen, 2009;Baker & Fradkin, 2011;Bughin, 2015) fields, but work was also performed in finance (Smith, 2012), communication and marketing (Granka, 2010); religion (Scheitle, 2011); education (Vaughan-Frias et al, 2013), and cinema (Judge & Hand, 2010). The hospitality and tourism sector was analysed in the studies developed by Chamberlin, 2010;Choi and Varian, 2009b;Shimshoni et al, 2009;Suhoy, 2009;Smith and White, 2011;Artola and Galán, 2012;Gawlik et al, 2011;Saidi et al, 2010;Pan et al, 2012;Concha et al, 2015;Kallasidis, 2015;Bangwayo-Skeete and Skeete (2015); Dinis et al 2013Dinis et al , 2015); Jackman and Naitram, 2015); Yang et al (2015); Rivera (2016);and Li et al, 2017).…”

Section: Literature Reviewmentioning

confidence: 99%

Similarities and correlation between resident tourist overnights and Google Trends information in Portugal and its tourism regions

Dinis

Costa

Pacheco

2017

TMS

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Over the last years, we observed an exponential growth in the number of tourism consumers that use the Internet as a source of information during a destination selection process. Google Trends is a tool that displays data, almost in real time, on the interest of people in a particular topic based on search trends. This paper demonstrates that Google Trends is a tool that can provide useful and relevant information about the interests of individuals in relation to domestic tourism destinations at national and regional levels. Our findings indicate that overnights spent in hotel establishments by the residents in Portugal are strongly correlated with the Google index, mainly in mainland Portugal, Alentejo and Algarve regions, and that the results improve when more municipalities names and the national or the regional tourism brands are included as search terms.Keywords: Google Trends, domestic tourism, search data, Portugal, big data. ResumoAo longo dos últimos anos, tem-se verificado um crescimento exponencial no número de consumidores de turismo que utiliza a Internet como fonte de informação no processo de seleção do destino turístico. O Google Trends é uma ferramenta que disponibilizada dados, quase em tempo real, sobre o interesse das pessoas num determinado tema com base nas tendências de pesquisa. Este artigo demonstra que o Google Trends é uma ferramenta que disponibiliza informação útil e relevante sobre os interesses dos indivíduos em relação aos destinos de turismo doméstico ao nível nacional e regional. Os resultados indicam que as dormidas dos residentes em Portugal nos estabelecimentos hoteleiros estão fortemente correlacionadas com o índice do Google, principalmente em Portugal continental, na região do Alentejo e do Algarve, e que os resultados melhoram quando são incluídos como termos de pesquisa mais nomes de municípios e a marca de turismo nacional ou das regiões de turismo. Palavras-chave:Google Trends, turismo doméstico, dados de pesquisa, Portugal, big data.

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“…The results show that incorporating the web traffic data can improve forecasting accuracy. Li, Pan, Law, and Huang () adopt search engine query volumes to forecast tourism demand for a destination. Their results suggest that the proposed method improves forecast accuracy more than a traditional time‐series model and a PCA‐based index model.…”

Section: Literature Reviewmentioning

confidence: 99%

An integrative framework for collaborative forecasting in tourism supply chains

Zhang

Song

2017

Journal of Tourism Research

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Tourism practitioners must often rely on each other in a tourism supply chain (TSC). Demand forecasting plays a key role in shaping the activities of TSC practitioners. In the past 4 decades, researchers have developed many techniques for advanced tourism demand forecasting, but practitioners have had little interest in them. To bridge this gap, we examine the nature of the forecasting tasks of TSC practitioners in Hong Kong and propose a collaborative TSC forecasting framework that not only integrates tourism demand forecasting methods with practitioners' knowledge, but also facilitates information sharing between TSC practitioners to increase industry collaboration and improve forecasting performance.

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Forecasting tourism demand with composite search index

Cited by 285 publications

References 42 publications

Forecasting Hotel Accommodation Demand Based on LSTM Model Incorporating Internet Search Index

Forecasting Hotel Accommodation Demand Based on LSTM Model Incorporating Internet Search Index

Similarities and correlation between resident tourist overnights and Google Trends information in Portugal and its tourism regions

An integrative framework for collaborative forecasting in tourism supply chains

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