Hybrid Modeling for Forecasting of Domestic Business Tourism Demand in Tehran

One of the most important events in the tourism industry of each country is the demand for a product or destination of tourism. There will always be distances and deviations between actual and predicted values, but the use of scientific and modern methods of forecasting will cause the results to reach far more than an objective estimate to the truth. In recent years, with the changing pattern of holidays and the formation of short-term holidays, cities have found the opportunity for tourism development. One of the most important types of domestic tourism in Tehran, based on the statistics of the National Center of Statistics and the views of the experts in this area, is business tourism. For this purpose, the present study seeks to propose models for forecasting effective variables on forecasting domestic business tourism demand in Tehran. To do this, information was used between the years 2001 to 2015. Independent variable of this study is the number of domestic Business tourists in Tehran, and dependent variables were selected based on Delphi and Fuzzy DEMATEL techniques. The model framework is a combination of regression, fuzzy neural network, and SVR algorithm, which combines these methods to measure forecast errors and compare the methods. The results of this research show that the proposed hybrid approach of regression and Adaptive Neuro-Fuzzy Inference System (ANFIS) can have better prediction than other methods for forecasting domestic Business tourism.