Tourism demand forecasting using stacking ensemble model with adaptive fuzzy combiner

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.

Analyzing intelligent tourism development and public services based on a fuzzy genetic hybrid system to promote environmental and cultural values

“…The method outperformed the alternative algorithms. Cankurt et al [ 30 ] developed a model for predicting tourism demand with a stacking combination model and an adaptive fuzzy combiner. The method aims to create an ensemble model for multivariate forecasting.…”

An Economic Approach to Smart Tourism (Tourism 4.0) Technologies: The Case of Türkiye

“…Dolayısıyla hizmet sektörü arasında yer alan "turizm" bir ülke için ekonomik, sosyal, kültürel ve doğal çevre ile sürekli etkileşimde olan çok yönlü bir faaliyet alanı oluşturmaktadır (Tüleykan, 2017). Özellikle de gelişmekte olan ülkeler ve destinasyonları için turizm faaliyetleri gelir, istihdam ve döviz sağlayıcı bir etki bırakmaktadır (Cankurt & Subaşı, 2022).…”

Forecasting the Tourist Arrival Volumes and Tourism Income with Combined ANN Architecture in the Post COVID-19 Period: The Case of Turkey

“…Furthermore, some studies incorporate neural networks with adaptive neuro-fuzzy inference systems to overcome a large number of input variables for multivariate forecasting [35], while others prefer using ensemble deep-learning approaches to address challenges such as the curse of dimensionality and high model complexity [36]. He et al [37] propose a new multiscale mode learning-based model for forecasting tourist arrivals by introducing mode decomposition models and the convolutional neural network model.…”

“…The methods chosen are TRAMO-SEATS [62][63][64], ETS [28,65,66], ARIMA [67][68][69], X-13 ARIMA SEATS [70], X11, STL decomposition, the grey model [54,55,71], ANN [42,72], and MLP [73,74], which are widely used forecasting methods in the literature. Each of the aforementioned techniques is initially applied simply to the study of tourist data, and then it is integrated with the ANN technique to incorporate the exogenous factors listed below: the global financial crisis, Turkey-Russia warplane crash crisis, the COVID-19 pandemic, and USD/TRY exchange rates.…”