Modelling Prediction of Consumer Demand in the Tourism and Hospitality Based on Time Series
Bohdan Danylyshyn1, Lidiia Shynkaruk2, Olha Prokopenko3, Svitlana Bondarenko4, Kateryna Veres5, LiliiaKovalenko6
1Bohdan Danylyshyn, Department of Regional Studies and Tourism, Kyiv National Economic University named after Vadym Hetman, Kyiv, Ukraine.
2Lidiia Shynkaruk, Producation and Investment Management Department, National University of Life and Environmental Sciences of Ukraine, Kyiv, Ukraine.
3Olha Prokopenko, Department of Business Administration Tallinn University of Technology, Tallinn, Estonia.
4Svitlana Bondarenko*, Department of Economic Management of Natural Resources, Institute of Market Problems and Economic-Ecological Research of the NAS of Ukraine, Odessa, Ukraine.
5Kateryna Veres, Department of Tourism and Hotel Business, National University of Food Technologies, Kyiv, Ukraine.
6Liliia Kovalenko, Department of Hotel and Restaurant Business, Odessa National Academy of Food Technologies, Odessa, Ukraine.
Manuscript received on November 12, 2019. | Revised Manuscript received on November 23, 2019. | Manuscript published on 30 November, 2019. | PP: 8551-8558 | Volume-8 Issue-4, November 2019. | Retrieval Number: D4338118419/2019©BEIESP | DOI: 10.35940/ijrte.D4338.118419
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Travel services, unlike other services, cannot be stored or stockpiled for the future. Unsold hotel rooms, excursions or unfilled seats on the aeroplane cannot be sold over time. When real demand provides planned load factors, the business grows. This indicates the importance of demand forecasting for all tourism enterprises.In forecasting tourism demand, quantitative and qualitative approaches are used. A quantitative approach is based on statistical information for the previous period, and a qualitative one is based on people’s opinions and opinions. Multivariate regression analysis is the most popular model for forecasting tourist demand. It takes into account many factors on which the tourist flow depends. In conditions of limited data, a time series model is used, which gives a high forecast, especially in pronounced seasonality. For a more accurate forecast of tourism demand, it is necessary to combine quantitative and qualitative approaches.
Keywords: Consumer Demand, Hospitality, Modelling Prediction, Time Series, Tourism.
Scope of the Article: Regression and Prediction.