Journal ArticleDOI
Forecasting tourism demand with composite search index
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TLDR
Findings suggest that the proposed framework and procedure for creating a composite search index adopted in a generalized dynamic factor model improves the forecast accuracy better than two benchmark models: a traditional time series model and a model with an index created by principal component analysis.About:
This article is published in Tourism Management.The article was published on 2017-04-01. It has received 238 citations till now. The article focuses on the topics: Search engine indexing & Index (economics).read more
Citations
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The impact of online user reviews on hotel room sales [Summary]
TL;DR: Wang et al. as discussed by the authors developed a fixed effect log-linear regression model to assess the influence of online reviews on the number of hotel room bookings, which indicated a significant relationship between online consumer reviews and business performance of hotels.
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Big data in tourism research: A literature review
TL;DR: This paper might be the first attempt to present a comprehensive literature review on different types of big data in tourism research, and facilitates a thorough understanding of this sunrise research and offers valuable insights into its future prospects.
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A review of research on tourism demand forecasting: Launching the Annals of Tourism Research Curated Collection on tourism demand forecasting
TL;DR: This paper reviewed 211 key papers published between 1968 and 2018 for a better understanding of how the methods of tourism demand forecasting have evolved over time, and found that forecasting models have grown more diversified, that these models have been combined, and that the accuracy of forecasting has been improved.
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Tourism demand forecasting: A deep learning approach
TL;DR: The construction and identification of highly relevant features from the proposed deep network architecture provide practitioners with a means of understanding the relationships between various tourist demand forecasting factors and tourist arrival volumes.
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Progress in information technology and tourism management: 30 years on and 20 years after the internet - Revisiting Buhalis & Law's landmark study about eTourism
TL;DR: In this article, the authors reviewed the state of eTourism research in terms of its significance to academic literature linking Information and Communication Technologies (ICTs) and tourism and highlighted the changes that this sector has experienced since then.
References
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Journal ArticleDOI
Time series analysis
TL;DR: A ordered sequence of events or observations having a time component is called as a time series, and some good examples are daily opening and closing stock prices, daily humidity, temperature, pressure, annual gross domestic product of a country and so on.
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Business intelligence and analytics: from big data to big impact
TL;DR: This introduction to the MIS Quarterly Special Issue on Business Intelligence Research first provides a framework that identifies the evolution, applications, and emerging research areas of BI&A, and introduces and characterized the six articles that comprise this special issue in terms of the proposed BI &A research framework.
Book
Big Data: A Revolution That Will Transform How We Live, Work, and Think
TL;DR: Big data improves health care, advances better education, and helps predict societal change from urban sprawl to the spread of the flu, and is roaring through all sectors of the economy and all areas of life.
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The Generalized Dynamic-Factor Model: Identification and Estimation
TL;DR: In this article, a generalized dynamic factor model with infinite dynamics and nonorthogonal idiosyncratic components is proposed, which generalizes the static approximate factor model of Chamberlain and Rothschild (1983), as well as the exact factor model a la Sargent and Sims (1977).