Forecasting Tourism Demand with an Improved Mixed Data Sampling Model
نویسندگان
چکیده
منابع مشابه
Tourism Demand Forecasting by Improved SVR Model
The inboard tourism demand forecasting is very important to the development of tourism industry. In this paper, the SVR model is adopted to forecast monthly inbound tourism demand of China. And the elitist Non-dominated Sorting Genetic Algorithm (NSGAII) is used to parameter optimization. The NSGAII algorithm can reduce complexity of the algorithm, keeps the diversity of population and increasi...
متن کاملTourism Demand Forecasting Model Using Neural Network
Travel agencies should be able to judge the market demand for tourism to develop sales plans accordingly. However, many travel agencies lack the ability to judge the market demand for tourism, and thus make risky business decisions. Based on the above, this study applied the Artificial Neural Network combined with the Genetic Algorithm (GA) to establish a prediction model of air ticket sales re...
متن کاملForecasting Tourism Demand with Composite Search Index
Researchers have adopted online data such as search query volumes to forecast tourism demand for a destination, including tourist volumes and hotel occupancy. However, the massive yet highly correlated query data pose challenges when researchers attempt to include them in the forecasting model. We propose a framework and procedure for creating a composite search index adopted in a generalized d...
متن کاملTourism Demand Forecasting Based on a Neuro-Fuzzy Model
Tourism in Greece plays a major role in the country’s economy and an accurate forecasting model for tourism demand is a useful tool, which could affect decision making and planning for the future. This paper answers some questions such as: how did the forecasting techniques evolve over the years, how precise can they be, and in what way can they be used in assessing the demand for tourism? An A...
متن کاملForecasting International Tourism Demand- an Empirical Case in Taiwan
Tourism, one of the biggest industries in many countries, has been considered a complexly integrated and self-contained economic activity.As key determinants of thetourism demand are not fully identified to some extent different forecasting models vary in thelevel of accuracy. By comparing the performance of diverse forecasting models,including the linear regression, autoregressive integrated m...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Journal of Travel Research
سال: 2020
ISSN: 0047-2875,1552-6763
DOI: 10.1177/0047287520906220