Developing a hybrid intelligent model for forecasting problems: Case study of tourism demand time series

Abstract

Forecasting tourism demand is a crucial issue in the tourism industry and is generally seen to be one of the most complex functions of tourism management. With the accurate forecasted trends and patterns that indicate the sizes, directions and characteristics of future international tourist flows, the government and private sectors can have a well-organized tourism strategy and provide a better infrastructure to serve the visitors and develop a suitable marketing strategy to gain benefit from the growing tourism. With the aim of developing accurate forecasting tools in the tourism industry, this study presents a new hybrid intelligent model that is called Modular Genetic-Fuzzy Forecasting System (MGFFS) by a combination of genetic fuzzy expert systems and data preprocessing. MGFFS is developed in three stage architecture. The first stage is data preprocessing. Some statistical tests are used to choose the key lags that are to be considered in the time series model. Then data transformation and K-means clustering have been applied to develop a modular model for reducing the complexity of the whole data space to become something more homogeneous. In the second stage, extraction of the TSK type fuzzy rule-based system for each cluster will be carried out by means of an efficient genetic learning algorithm that uses symbiotic evolution for fitness assignment. In the last stage, the testing data are first clustered and tourism demand forecasting is done by means of each cluster's fuzzy system. Results show that forecasting accuracy of MGFFS is relatively better than other approaches in literature such as Classical Time Series models, Neuro-Fuzzy systems, and neural network, according to MAPE and RMSE evaluations. Powerful non-parametric statistical tests such as Friedman, Bonferroni, Holm and Hochberg are also used for comparing the performance of MGFFS with others. Based on the statistical tests, MGFFS is better than other models in accuracy and can be used as a suitable forecasting tool in tourism demand forecasting problems. © 2013 Elsevier B.V. All rights reserved.