Abstract
This paper introduces the performance of different forecasting methods for tourism demand, which can be employed as one of the statistical tools for time series forecasting. The Holt-Winters (HW), Seasonal Autoregressive Integrated Moving Average (SARIMA) and Grey model (GM (1, 1)) are three important statistical models in time-series forecasting. This paper analyzes and compare the performance of forecasting models using rough set methods, Total Roughness (TR), Min-Min Roughness (MMR) and Maximum Dependency of attributes (MDA). Current research identifies the best time series forecasting model among the three studied time series forecasting models. Comparative study shows that HW and SARIMA are superior models than GM (1, 1) for forecasting seasonal time series under TR, MMR and MDA criteria. In addition, the authors of this study showed that GM (1, 1) grey model is unqualified for seasonal time series data.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
More From: Decision Making: Applications in Management and Engineering
Disclaimer: 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.