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dc.contributor.authorCankurt, Selcuk
dc.contributor.authorSubasi, Abdulhamit
dc.date.accessioned2023-03-13T07:26:26Z
dc.date.available2023-03-13T07:26:26Z
dc.date.issued2022-04-01
dc.identifier.citationCankurt, S., Subasi, A. Tourism demand forecasting using stacking ensemble model with adaptive fuzzy combiner. Soft Comput 26, 3455–3467 (2022). https://doi.org/10.1007/s00500-021-06695-0en_US
dc.identifier.doihttps://doi.org/10.1007/s00500-021-06695-0en_US
dc.identifier.urihttp://hdl.handle.net/20.500.14131/604
dc.description.abstractOver 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.en_US
dc.publisherSpringer Berlin Heidelbergen_US
dc.subjectArtificial Neural Network (ANN)en_US
dc.subjectANFISen_US
dc.subjectMultivariate Time Series Forecastingen_US
dc.subjectStacking Ensembleen_US
dc.subjectTourism Demand Forecastingen_US
dc.subjectResearch Subject Categories::INTERDISCIPLINARY RESEARCH AREASen_US
dc.titleTourism demand forecasting using stacking ensemble model with adaptive fuzzy combineren_US
dc.source.journalSoft Computingen_US
dc.source.volume26en_US
dc.source.issue7en_US
dc.contributor.researcherExternal Collaborationen_US
dc.contributor.labArtificial Intelligence & Cyber Security Laben_US
dc.subject.KSALeadEconen_US
dc.source.indexScopus/ISIen_US
dc.contributor.departmentComputer Scienceen_US
dc.contributor.firstauthorCankurt, Selcuk


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