Open AccessModelling Prediction of Consumer Demand in the Tourism and Hospitality Based on Time Series
Author(s) -
Bohdan Danylyshyn,
Lidiia Shynkaruk,
Olha Volodymyrivna Prokopenko,
Svіtlana Bondarenko,
Kateryna Veres,
Liliia Kovalenko
Publication year - 2019
Publication title -
international journal of recent technology and engineering
Language(s) - English
Resource type - Journals
ISSN - 2277-3878
DOI - 10.35940/ijrte.d4338.118419
Subject(s) - tourism , demand forecasting , hospitality , time series , quantitative analysis (chemistry) , operations research , econometrics , business , marketing , computer science , economics , geography , engineering , chemistry , archaeology , chromatography , machine learning
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.