Tourism demand forecasting using stacking ensemble model with adaptive fuzzy combiner


Cankurt S., Subasi A.

SOFT COMPUTING, vol.26, pp.3455-3467, 2022 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 26
  • Publication Date: 2022
  • Doi Number: 10.1007/s00500-021-06695-0
  • Journal Name: SOFT COMPUTING
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, Applied Science & Technology Source, Compendex, Computer & Applied Sciences, INSPEC, zbMATH
  • Page Numbers: pp.3455-3467
  • Keywords: Artificial neural network (ANN), ANFIS, Multivariate time series forecasting, Stacking ensemble, Tourism demand forecasting, ARTIFICIAL NEURAL-NETWORKS, TIME-SERIES, COMBINATION, PREDICTION, ANFIS, SYSTEM, ARIMA
  • Süleyman Demirel University Affiliated: No

Abstract

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.