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    Tourism demand forecasting using stacking ensemble model with adaptive fuzzy combiner

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    Author
    Cankurt, Selcuk
    Subasi, Abdulhamit cc
    Subject
    Artificial Neural Network (ANN)
    ANFIS
    Multivariate Time Series Forecasting
    Stacking Ensemble
    Tourism Demand Forecasting
    Research Subject Categories::INTERDISCIPLINARY RESEARCH AREAS
    Date
    2022-04-01
    
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    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.
    Department
    Computer Science
    Publisher
    Springer Berlin Heidelberg
    Journal title
    Soft Computing
    DOI
    https://doi.org/10.1007/s00500-021-06695-0
    ae974a485f413a2113503eed53cd6c53
    https://doi.org/10.1007/s00500-021-06695-0
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