Modeling and Forecasting of Tourism Time Series Data using ANN-Fourier Series Model and Monte Carlo Simulation

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dc.authoridVarol, Asaf/0000-0003-1606-4079en_US
dc.authoridDanbatta, Salim Jibrin/0000-0002-8913-5766en_US
dc.contributor.authorDanbatta, Salim Jibrin
dc.contributor.authorVarol, Asaf
dc.date.accessioned2024-07-12T21:40:37Z
dc.date.available2024-07-12T21:40:37Z
dc.date.issued2021en_US
dc.department[Belirlenecek]en_US
dc.description9th International Symposium on Digital Forensics and Security (ISDFS) -- JUN 28-29, 2021 -- Fırat Univ, Elazig, TURKEYen_US
dc.description.abstractTourism is counted as one of the most sensitive sectors to crises such as the COVID-19 pandemic. By the first quarter of 2020, it brought the foreign visitors' travels to a sudden and unexpected halt. This has negatively affected the tourism sector. Due to the perishable nature of the tourism industry products, many researchers are calling for urgent development and implementation of a rescue plan that will help in predicting the future number of foreign visitors. In this paper, we proposed an approach to modeling and forecasting a tourism time-series data that have both trend and seasonality. This approach is a combination of the Fourier series and artificial neural network methods to capture the seasonality and trend components in data. We applied this method to the monthly foreign visitors to Turkey dataset. We studied the data for the periods before, and during the COVID-19 pandemic. To account for uncertainties in the model prediction during the COVID-19 pandemic, we employed the Monte Carlo simulation method. We run 100 Monte Carlo simulations within +/- 2 sigma from the model curve. The mean of these 100 Monte Carlo simulation paths is computed and used for presenting the Monte Carlo forecast result values of the data. To test the feasibility of this approach, we compared the model predictions with some other existing models in the literature. In each case, the model has demonstrated a decent prediction and outperformed the benchmarked models. The proposed model produces a statistically good fit and acceptable result that can be used to forecast other tourism-related attributes.en_US
dc.description.sponsorshipIEEE Turkey Sect,Maltepe Univ,Sam Houston State Univ,Gazi Univ,San Diego State Univ,Arab Open Univ,Hacettepe Univ,Polytechnic Inst Cavado & Ave,Balikesir Univ,Ondokuz Mayis Univ,Assoc Software & Cyber Secur Turkey,Informat Assoc Turkey,Recep Tayyip Erdoğan Univ,Singidunum Univ,TELUQ Univ,Yıldız Teknik Univen_US
dc.identifier.doi10.1109/ISDFS52919.2021.9486325
dc.identifier.isbn978-1-6654-4481-1
dc.identifier.scopus2-s2.0-85114687376en_US
dc.identifier.scopusqualityN/Aen_US
dc.identifier.urihttps://doi.org/10.1109/ISDFS52919.2021.9486325
dc.identifier.urihttps://hdl.handle.net/20.500.12415/7392
dc.identifier.wosWOS:000844418700008en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoenen_US
dc.publisherIeeeen_US
dc.relation.ispartof9th International Symposium on Digital Forensics And Security (Isdfs'21)en_US
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.snmzKY08733
dc.subjectFourier Seriesen_US
dc.subjectArtificial Neural Networken_US
dc.subjectMonte Carlo Simulationen_US
dc.titleModeling and Forecasting of Tourism Time Series Data using ANN-Fourier Series Model and Monte Carlo Simulationen_US
dc.typeConference Object
dspace.entity.typePublication

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