Tourism Demand Forecasting by Improved SVR Model
نویسنده
چکیده
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 increasing the forecasting accuracy. At last, the proposed NSGAII-SVR model is used to forecast monthly inbound tourism demand of China from July 2011 to December 2011. And the experimental results show that the NSGAII-SVR has the best performance on forecasting compared with other models.
منابع مشابه
مدل سازی ترکیبی پیش بینی تقاضای گردشگری پزشکی داخلی شهر تهران
Introduction: One of the most important events in the tourism industry of each country is the demand for a product or destination and its true prediction of tourism. It should be noted that there are distances and deviations between actual values and predictions. The use of modern scientific and forecasting methods will make the results far more than an objective estimate and closer to the trut...
متن کاملTourism Demand Forecasting: Econometric Model based on Multivariate Adaptive Regression Splines, Artificial Neural Network and Support Vector Regression
This paper develops tourism demand econometric models based on the monthly data of tourists to Taiwan and adopts Multivariate Adaptive Regression Splines (MARS), Artificial Neural Network (ANN) and Support Vector Regression (SVR), MARS, ANN and SVR to develop forecast models and compare the forecast results. The results showed that SVR model is the optimal model, with a mean error rate of 3.61%...
متن کاملCyclic electric load forecasting by seasonal SVR with chaotic genetic algorithm
Application of support vector regression (SVR) with chaotic sequence and evolutionary algorithms not only could improve forecasting accuracy performance, but also could effectively avoid converging prematurely (i.e., trapping into a local optimum). However, the tendency of electric load sometimes reveals cyclic changes (such as hourly peak in a working day, weekly peak in a business week, and m...
متن کاملForecasting the Demand for Health Tourism in Asian Countries Using a Gm(1,1)-alpha Model
The purpose – Accurately forecasting the demand for international health tourism is important to newly-emerging markets in the world. The aim of this study was presents a more suitable and accurate model for forecasting the demand for health tourism that should be more theoretically useful. Design – Applying GM(1,1) with adaptive levels of α (hereafter GM(1,1)-α model) to provide a concise pred...
متن کاملTourism Demand Modelling and Forecasting—A Review of Recent Research
This paper reviews the published studies on tourism demand modelling and forecasting since 2000. One of the key findings of this review is that the methods used in analysing and forecasting the demand for tourism have been more diverse than those identified by other review articles. In addition to the most popular time series and econometric models, a number of new techniques have emerged in th...
متن کامل