Intelligence in Tourism Management: A Hybrid FOA-BP Method on Daily Tourism Demand Forecasting with Web Search Data
Author
Abstract
Suggested Citation
Download full text from publisher
References listed on IDEAS
- Thomas Dimpfl & Stephan Jank, 2016.
"Can Internet Search Queries Help to Predict Stock Market Volatility?,"
European Financial Management, European Financial Management Association, vol. 22(2), pages 171-192, March.
- Dimpfl, Thomas & Jank, Stephan, 2011. "Can Internet search queries help to predict stock market volatility?," University of Tübingen Working Papers in Business and Economics 18, University of Tuebingen, Faculty of Economics and Social Sciences, School of Business and Economics.
- Dimpfl, Thomas & Jank, Stephan, 2011. "Can internet search queries help to predict stock market volatility?," CFR Working Papers 11-15, University of Cologne, Centre for Financial Research (CFR).
- Luis A. Gil-Alana & Juncal Cunado & Fernando Perez de Gracia, 2008.
"Tourism in the Canary Islands: forecasting using several seasonal time series models,"
Journal of Forecasting, John Wiley & Sons, Ltd., vol. 27(7), pages 621-636.
- Juncal Cuñado & Luis A. Gil-Alaña, 2007. "Tourism in the Canary Islands: Forecasting Using Several Seasonal Time Series Models," Faculty Working Papers 02/07, School of Economics and Business Administration, University of Navarra.
- Jeremy Ginsberg & Matthew H. Mohebbi & Rajan S. Patel & Lynnette Brammer & Mark S. Smolinski & Larry Brilliant, 2009. "Detecting influenza epidemics using search engine query data," Nature, Nature, vol. 457(7232), pages 1012-1014, February.
- Yang, Xin & Pan, Bing & Evans, James A. & Lv, Benfu, 2015. "Forecasting Chinese tourist volume with search engine data," Tourism Management, Elsevier, vol. 46(C), pages 386-397.
- Francesco, D'Amuri, 2009. "Predicting unemployment in short samples with internet job search query data," MPRA Paper 18403, University Library of Munich, Germany.
Citations
Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
Cited by:
- Haodong Sun & Yang Yang & Yanyan Chen & Xiaoming Liu & Jiachen Wang, 2023. "Tourism demand forecasting of multi-attractions with spatiotemporal grid: a convolutional block attention module model," Information Technology & Tourism, Springer, vol. 25(2), pages 205-233, June.
- Guanghai Zhang & Hongying Yuan, 2022. "Spatio-Temporal Evolution Characteristics and Spatial Differences in Urban Tourism Network Attention in China: Based on the Baidu Index," Sustainability, MDPI, vol. 14(20), pages 1-15, October.
- Abang Zainoren Abang Abdurahman & Wan Fairos Wan Yaacob & Syerina Azlin Md Nasir & Serah Jaya & Suhaili Mokhtar, 2022. "Using Machine Learning to Predict Visitors to Totally Protected Areas in Sarawak, Malaysia," Sustainability, MDPI, vol. 14(5), pages 1-16, February.
- Jian-Wu Bi & Tian-Yu Han & Yanbo Yao, 2024. "Collaborative forecasting of tourism demand for multiple tourist attractions with spatial dependence: A combined deep learning model," Tourism Economics, , vol. 30(2), pages 361-388, March.
Most related items
These are the items that most often cite the same works as this one and are cited by the same works as this one.- Imene Ben El Hadj Said & Skander Slim, 2022. "The Dynamic Relationship between Investor Attention and Stock Market Volatility: International Evidence," JRFM, MDPI, vol. 15(2), pages 1-25, February.
- Shenzhen Tian & Xueming Li & Jun Yang & Hui Wang & Jianke Guo, 2023. "Spatiotemporal evolution of pseudo human settlements: case study of 36 cities in the three provinces of Northeast China from 2011 to 2018," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 25(2), pages 1742-1772, February.
- D'Amuri, Francesco & Marcucci, Juri, 2009.
"‘Google it!’ Forecasting the US unemployment rate with a Google job search index,"
ISER Working Paper Series
2009-32, Institute for Social and Economic Research.
- D'Amuri, Francesco/FD & Marcucci, Juri/JM, 2009. ""Google it!" Forecasting the US unemployment rate with a Google job search index," MPRA Paper 18248, University Library of Munich, Germany.
- Francesco D’Amuri & Juri Marcucci, 2010. "“Google it!”Forecasting the US Unemployment Rate with a Google Job Search index," Working Papers 2010.31, Fondazione Eni Enrico Mattei.
- Jianchun Fang & Wanshan Wu & Zhou Lu & Eunho Cho, 2019. "Using Baidu Index To Nowcast Mobile Phone Sales In China," The Singapore Economic Review (SER), World Scientific Publishing Co. Pte. Ltd., vol. 64(01), pages 83-96, March.
- Papadamou, Stephanos & Fassas, Athanasios P. & Kenourgios, Dimitris & Dimitriou, Dimitrios, 2023. "Effects of the first wave of COVID-19 pandemic on implied stock market volatility: International evidence using a google trend measure," The Journal of Economic Asymmetries, Elsevier, vol. 28(C).
- Long Wen & Chang Liu & Haiyan Song, 2019. "Forecasting tourism demand using search query data: A hybrid modelling approach," Tourism Economics, , vol. 25(3), pages 309-329, May.
- repec:ipg:wpaper:2014-405 is not listed on IDEAS
- Hulya Bakirtas & Vildan Gulpinar Demirci, 2022. "Can Google Trends data provide information on consumer’s perception regarding hotel brands?," Information Technology & Tourism, Springer, vol. 24(1), pages 57-83, March.
- Massimiliano Caporin & Francesco Poli, 2017. "Building News Measures from Textual Data and an Application to Volatility Forecasting," Econometrics, MDPI, vol. 5(3), pages 1-46, August.
- Zhongchen Song & Tom Coupé, 2023.
"Predicting Chinese consumption series with Baidu,"
Journal of Chinese Economic and Business Studies, Taylor & Francis Journals, vol. 21(3), pages 429-463, July.
- Zhongchen Song & Tom Coupé, 2022. "Predicting Chinese consumption series with Baidu," Working Papers in Economics 22/19, University of Canterbury, Department of Economics and Finance.
- Zhang, Chuan & Tian, Yu-Xin & Fan, Zhi-Ping, 2022. "Forecasting sales using online review and search engine data: A method based on PCA–DSFOA–BPNN," International Journal of Forecasting, Elsevier, vol. 38(3), pages 1005-1024.
- Qadan, Mahmoud & Nama, Hazar, 2018. "Investor sentiment and the price of oil," Energy Economics, Elsevier, vol. 69(C), pages 42-58.
- Rivera, Roberto, 2016. "A dynamic linear model to forecast hotel registrations in Puerto Rico using Google Trends data," Tourism Management, Elsevier, vol. 57(C), pages 12-20.
- Hamid, Alain & Heiden, Moritz, 2015. "Forecasting volatility with empirical similarity and Google Trends," Journal of Economic Behavior & Organization, Elsevier, vol. 117(C), pages 62-81.
- Bai, Lijuan & Yan, Xiangbin & Yu, Guang, 2019. "Impact of CEO media appearance on corporate performance in social media," The North American Journal of Economics and Finance, Elsevier, vol. 50(C).
- McLaren, Nick & Shanbhogue, Rachana, 2011. "Using internet search data as economic indicators," Bank of England Quarterly Bulletin, Bank of England, vol. 51(2), pages 134-140.
- Fantazzini, Dean & Shangina, Tamara, 2019.
"The importance of being informed: forecasting market risk measures for the Russian RTS index future using online data and implied volatility over two decades,"
Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 55, pages 5-31.
- Fantazzini, Dean & Shangina, Tamara, 2019. "The importance of being informed: forecasting market risk measures for the Russian RTS index future using online data and implied volatility over two decades," MPRA Paper 95992, University Library of Munich, Germany.
- Campos, I. & Cortazar, G. & Reyes, T., 2017. "Modeling and predicting oil VIX: Internet search volume versus traditional mariables," Energy Economics, Elsevier, vol. 66(C), pages 194-204.
- Yang, Yang & Fan, Yawen & Jiang, Lan & Liu, Xiaohui, 2022. "Search query and tourism forecasting during the pandemic: When and where can digital footprints be helpful as predictors?," Annals of Tourism Research, Elsevier, vol. 93(C).
- Thomas Dimpfl & Tobias Langen, 2019. "How Unemployment Affects Bond Prices: A Mixed Frequency Google Nowcasting Approach," Computational Economics, Springer;Society for Computational Economics, vol. 54(2), pages 551-573, August.
- Bonaparte, Yosef & Bernile, Gennaro, 2023. "A new “Wall Street Darling?” effects of regulation sentiment in cryptocurrency markets," Finance Research Letters, Elsevier, vol. 52(C).
More about this item
Keywords
tourism management; hybrid method; fruit fly optimization algorithm; neural network; web search data; forecast of daily tourism demand; optimization method;All these keywords.
Statistics
Access and download statisticsCorrections
All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jmathe:v:7:y:2019:i:6:p:531-:d:238766. See general information about how to correct material in RePEc.
If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.
If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .
If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .
Please note that corrections may take a couple of weeks to filter through the various RePEc services.