Tourism demand forecasting using stacking ensemble model with adaptive fuzzy combiner
Cankurt S. Subasi A.
April 2022Springer Science and Business Media Deutschland GmbH
Soft Computing
2022#26Issue 73455 - 3467 pp.
Over the last decades, several soft computing techniques have been applied to tourism demand forecasting. Among these techniques, a neuro-fuzzy model of ANFIS (adaptive neuro-fuzzy inference system) has started to emerge. A conventional ANFIS model cannot deal with the large dimension of a dataset, and cannot work with our dataset, which is composed of a 62 time-series, as well. This study attempts to develop an ensemble model by incorporating neural networks with ANFIS to deal with a large number of input variables for multivariate forecasting. Our proposed approach is a collaboration of two base learners, which are types of the neural network models and a meta-learner of ANFIS in the framework of the stacking ensemble. The results show that the stacking ensemble of ANFIS (meta-learner) and ANN models (base learners) outperforms its stand-alone counterparts of base learners. Numerical results indicate that the proposed ensemble model achieved a MAPE of 7.26% compared to its single-instance ANN models with MAPEs of 8.50 and 9.18%, respectively. Finally, this study which is a novel application of the ensemble systems in the context of tourism demand forecasting has shown better results compared to those of the single expert systems based on the artificial neural networks.
ANFIS , Artificial neural network (ANN) , Multivariate time series forecasting , Stacking ensemble , Tourism demand forecasting
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Faculty of Engineering and Natural Sciences, Suleyman Demirel University, Almaty, Kazakhstan
Institute of Biomedicine, Faculty of Medicine, University of Turku, Turku, 20520, Finland
Department of Computer Science, College of Engineering, Effat University, Jeddah, 21478, Saudi Arabia
Faculty of Engineering and Natural Sciences
Institute of Biomedicine
Department of Computer Science
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