Big data from dynamic pricing: A smart approach to tourism demand forecasting
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DOI: 10.1016/j.ijforecast.2020.11.006
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- Hyndman, Rob J. & Khandakar, Yeasmin, 2008.
"Automatic Time Series Forecasting: The forecast Package for R,"
Journal of Statistical Software, Foundation for Open Access Statistics, vol. 27(i03).
- Rob J. Hyndman & Yeasmin Khandakar, 2007. "Automatic time series forecasting: the forecast package for R," Monash Econometrics and Business Statistics Working Papers 6/07, Monash University, Department of Econometrics and Business Statistics.
- Hylleberg, S. & Engle, R. F. & Granger, C. W. J. & Yoo, B. S., 1990.
"Seasonal integration and cointegration,"
Journal of Econometrics, Elsevier, vol. 44(1-2), pages 215-238.
- Hylleberg, S. & Engle, R.F. & Granger, C.W.J. & Yoo, B.S., 1988. "Seasonal, Integration And Cointegration," Papers 6-88-2, Pennsylvania State - Department of Economics.
- Hyllerberg, S. & Engle, R.F. & Granger, C.W.J. & Yoo, B.S., 1988. "Seasonal Integration And Cointegration," Papers 0-88-2, Pennsylvania State - Department of Economics.
- Bangwayo-Skeete, Prosper F. & Skeete, Ryan W., 2015. "Can Google data improve the forecasting performance of tourist arrivals? Mixed-data sampling approach," Tourism Management, Elsevier, vol. 46(C), pages 454-464.
- Wai Kit Tsang & Dries F. Benoit, 2020. "Gaussian processes for daily demand prediction in tourism planning," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 39(3), pages 551-568, April.
- Voltes-Dorta, Augusto & Jiménez, Juan Luis & Suárez-Alemán, Ancor, 2014. "An initial investigation into the impact of tourism on local budgets: A comparative analysis of Spanish municipalities," Tourism Management, Elsevier, vol. 45(C), pages 124-133.
- Guizzardi, Andrea & Stacchini, Annalisa, 2015. "Real-time forecasting regional tourism with business sentiment surveys," Tourism Management, Elsevier, vol. 47(C), pages 213-223.
- Clark, Todd & McCracken, Michael, 2013.
"Advances in Forecast Evaluation,"
Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 2, chapter 0, pages 1107-1201,
Elsevier.
- Todd E. Clark & Michael W. McCracken, 2011. "Advances in forecast evaluation," Working Papers 2011-025, Federal Reserve Bank of St. Louis.
- Todd E. Clark & Michael W. McCracken, 2011. "Advances in forecast evaluation," Working Papers (Old Series) 1120, Federal Reserve Bank of Cleveland.
- Gaojun Zhang & Jinfeng Wu & Bing Pan & Junyi Li & Minjie Ma & Muzi Zhang & Jian Wang, 2017. "Improving daily occupancy forecasting accuracy for hotels based on EEMD-ARIMA model," Tourism Economics, , vol. 23(7), pages 1496-1514, November.
- Hyunyoung Choi & Hal Varian, 2012. "Predicting the Present with Google Trends," The Economic Record, The Economic Society of Australia, vol. 88(s1), pages 2-9, June.
- Liu, Yuan-Yuan & Tseng, Fang-Mei & Tseng, Yi-Heng, 2018. "Big Data analytics for forecasting tourism destination arrivals with the applied Vector Autoregression model," Technological Forecasting and Social Change, Elsevier, vol. 130(C), pages 123-134.
- Gunter, Ulrich & Önder, Irem, 2016. "Forecasting city arrivals with Google Analytics," Annals of Tourism Research, Elsevier, vol. 61(C), pages 199-212.
- Weatherford, Larry R. & Kimes, Sheryl E., 2003. "A comparison of forecasting methods for hotel revenue management," International Journal of Forecasting, Elsevier, vol. 19(3), pages 401-415.
- Candy Mei Fung Tang & Brian King & Stephen Pratt, 2017. "Predicting hotel occupancies with public data," Tourism Economics, , vol. 23(5), pages 1096-1113, August.
- Xiang, Zheng & Du, Qianzhou & Ma, Yufeng & Fan, Weiguo, 2017. "A comparative analysis of major online review platforms: Implications for social media analytics in hospitality and tourism," Tourism Management, Elsevier, vol. 58(C), pages 51-65.
- Song, Haiyan & Qiu, Richard T.R. & Park, Jinah, 2019. "A review of research on tourism demand forecasting," Annals of Tourism Research, Elsevier, vol. 75(C), pages 338-362.
- Li, Gang & Law, Rob & Vu, Huy Quan & Rong, Jia & Zhao, Xinyuan (Roy), 2015. "Identifying emerging hotel preferences using Emerging Pattern Mining technique," Tourism Management, Elsevier, vol. 46(C), pages 311-321.
- Taiga Saito & Akihiko Takahashi & Hiroshi Tsuda, 2016. "Optimal Room Charge and Expected Sales under Discrete Choice Models with Limited Capacity (Forthcoming in "International Journal of Hospitality Management")," CARF F-Series CARF-F-380, Center for Advanced Research in Finance, Faculty of Economics, The University of Tokyo.
- Li, Xin & Pan, Bing & Law, Rob & Huang, Xiankai, 2017. "Forecasting tourism demand with composite search index," Tourism Management, Elsevier, vol. 59(C), pages 57-66.
- 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.
- Falk, Martin, 2010. "A dynamic panel data analysis of snow depth and winter tourism," Tourism Management, Elsevier, vol. 31(6), pages 912-924.
- 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.
- Chua, Alvin & Servillo, Loris & Marcheggiani, Ernesto & Moere, Andrew Vande, 2016. "Mapping Cilento: Using geotagged social media data to characterize tourist flows in southern Italy," Tourism Management, Elsevier, vol. 57(C), pages 295-310.
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- Ulrich Gunter, 2021. "Improving Hotel Room Demand Forecasts for Vienna across Hotel Classes and Forecast Horizons: Single Models and Combination Techniques Based on Encompassing Tests," Forecasting, MDPI, vol. 3(4), pages 1-36, November.
- Silva, Emmanuel Sirimal & Hassani, Hossein, 2022. "‘Modelling’ UK tourism demand using fashion retail sales," Annals of Tourism Research, Elsevier, vol. 95(C).
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Keywords
Regional forecasting; Daily forecasting; Leading indicator; Advance booking; Dynamic pricing; Hotelier’s expectations about tourism demand;All these keywords.
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