Optimized Multivariate Lag Structure Selection
Computational Economics - Special issue on computational studies at Cambridge
Combining Artificial Neural Nets: Ensemble and Modular Multi-Net Systems
Combining Artificial Neural Nets: Ensemble and Modular Multi-Net Systems
Computational Intelligence: Principles, Techniques and Applications
Computational Intelligence: Principles, Techniques and Applications
Advances in Fuzzy Clustering and its Applications
Advances in Fuzzy Clustering and its Applications
Surveying stock market forecasting techniques - Part II: Soft computing methods
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Handbook of Parametric and Nonparametric Statistical Procedures
Handbook of Parametric and Nonparametric Statistical Procedures
A fuzzy GARCH model applied to stock market scenario using a genetic algorithm
Expert Systems with Applications: An International Journal
An efficient hybrid approach based on PSO, ACO and k-means for cluster analysis
Applied Soft Computing
Expert Systems with Applications: An International Journal
Integration of genetic fuzzy systems and artificial neural networks for stock price forecasting
Knowledge-Based Systems
Understanding consumer heterogeneity: A business intelligence application of neural networks
Knowledge-Based Systems
Expert Systems with Applications: An International Journal
SVR with hybrid chaotic genetic algorithms for tourism demand forecasting
Applied Soft Computing
A class of hybrid morphological perceptrons with application in time series forecasting
Knowledge-Based Systems
Expert Systems with Applications: An International Journal
Forecasting tourism demand based on empirical mode decomposition and neural network
Knowledge-Based Systems
Expert Systems with Applications: An International Journal
Parameter control in evolutionary algorithms
IEEE Transactions on Evolutionary Computation
Genetic reinforcement learning through symbiotic evolution forfuzzy controller design
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
A hybrid fuzzy intelligent agent-based system for stock price prediction
International Journal of Intelligent Systems
A two-stage approach for formulating fuzzy regression models
Knowledge-Based Systems
Tourism demand forecasting using novel hybrid system
Expert Systems with Applications: An International Journal
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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.