Abstract
This article focuses on a new proposed artificial neural network (ANN) model for tourism demand forecasting using time-series which, unlike previous models, uses different seasons of arrivals and values of months with similar behavior as input variables and achieves a forecast up to a year in advance. We demonstrate the validity and greater precision of the proposed model in forecasting a nonconsolidated destination with marked seasonality, which has been scarcely dealt with in other research. We achieve a comparatively greater quality of results and a longer period in advance than previously used auto-regressive integrated moving average (ARIMA) and ANN models. Highly accurate results were also obtained in destinations such as Portugal, which also proves its validity for mature destinations.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
