By Zheng Xiang, Daniel R. Fesenmaier

This ebook offers innovative examine at the improvement of analytics in commute and tourism. It introduces new conceptual frameworks and dimension instruments, in addition to purposes and case reviews for vacation spot advertising and administration. it really is divided into 5 components: half one on trip call for analytics makes a speciality of conceptualizing and imposing trip call for modeling utilizing vast info. It illustrates new how one can determine, generate and make the most of huge amounts of information in tourism call for forecasting and modeling. half makes a speciality of analytics in shuttle and way of life, providing contemporary advancements in wearable desktops and physiological size units, and the consequences for our figuring out of on-the-go tourists and tourism layout. half 3 embraces tourism geoanalytics, correlating social media and geo-based information with tourism facts. half 4 discusses web-based and social media analytics and offers the most recent advancements in using user-generated content material on the web to appreciate a few managerial difficulties. the ultimate half is a set of case stories utilizing web-based and social media analytics, with examples from the Sochi Olympics on Twitter, leveraging on-line experiences within the inn undefined, and comparing vacation spot communications and marketplace intelligence with on-line inn stories. The chapters during this part jointly describe a number of diverse methods to figuring out marketplace dynamics in tourism and hospitality.

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New York: OR/MS Today. Schumacher, C. (2010). Factor forecasting using international targeted predictors: The case of German GDP. Economics Letters, 107(2), 95–98. , & Breitung, J. (2008). Real-time forecasting of German GDP based on a large factor model with monthly and quarterly data. International Journal of Forecasting, 24(3), 386–398. Shi, Y. (2014). Big data: History, current status, and challenges going forward. Bridge, 44(4), 6–11. -D. (2012). Big data: Big gaps of knowledge in the field of internet science.

One of the solutions is to shrink the big data and get the most important information in a suitable format that can be easily applied to the traditional forecasting model. Factor models are the most common and popular statistical and data mining technique used for big data forecasting; neural networks and Bayesian models are two other popular choices. In this chapter, we focus on the factor models. (1) Mixed frequency model with big data There has been some research success using big data for tourism forecasting.

MIT Sloan Management Review, 52(2), 21–31. , Yang, L. , & Cuzzocrea, A. (2015). Big data: Algorithms, analytics, and applications. Boca Raton, FL: CRC Press. Madden, S. (2012). From databases to big data. IEEE Internet Computing, 3, 4–6. , & Schumacher, C. (2010). Factor MIDAS for nowcasting and forecasting with ragged-edge data: A model comparison for German GDP. Oxford Bulletin of Economics and Statistics, 72(4), 518–550. Marko, K. (2015). Using big data and machine learning to enrich customer experiences.

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