IDEAS home Printed from https://ideas.repec.org/a/wly/jforec/v41y2022i5p920-932.html
   My bibliography  Save this article

ANN–polynomial–Fourier series modeling and Monte Carlo forecasting of tourism data

Author

Listed:
  • Salim Jibrin Danbatta
  • Asaf Varol

Abstract

Modeling and forecasting of tourism data have received attention in the past decades. Turkey is one of the countries that benefit significantly from the tourism industry. Several time‐series models have been recommended to best describe tourist arrivals to Turkey. However, in the 21st century, the world experiences great uncertainty in most possible event outcomes. These uncertainties are very difficult to account for. We proposed a hybrid artificial neural network (ANN)–polynomial–Fourier method to model the number of foreign visitors to Turkey from January 2004 to December 2020. The proposed model performance before and during the COVID‐19 pandemic is evaluated separately. We evaluate the model performance by comparing with results from Danbatta and Varol (2021, https://doi.org/10.1142/S179396232141004X), Fourier series, and ARIMA models. To account for prediction uncertainties, we ran 300 Monte Carlo simulations within ±2σ from the model regression curve. According to the result outcomes, the proposed ANN–polynomial–Fourier has proven worthy to be considered a candidate model for the Turkish tourism data. The multistep ahead forecast suggests a 10.22% increase in the monthly foreign visitors' arrivals to Turkey in the year 2021.

Suggested Citation

  • Salim Jibrin Danbatta & Asaf Varol, 2022. "ANN–polynomial–Fourier series modeling and Monte Carlo forecasting of tourism data," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 41(5), pages 920-932, August.
    • Handle: RePEc:wly:jforec:v:41:y:2022:i:5:p:920-932
    DOI: 10.1002/for.2845
    as

    Download full text from publisher

    File URL: https://doi.org/10.1002/for.2845
    Download Restriction: no
    File URL: https://libkey.io/10.1002/for.2845?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Xie, Gang & Qian, Yatong & Wang, Shouyang, 2020. "A decomposition-ensemble approach for tourism forecasting," Annals of Tourism Research, Elsevier, vol. 81(C).
    2. Athanasopoulos, George & Hyndman, Rob J. & Song, Haiyan & Wu, Doris C., 2011. "The tourism forecasting competition," International Journal of Forecasting, Elsevier, vol. 27(3), pages 822-844, July.
    3. Witt, Stephen F. & Witt, Christine A., 1995. "Forecasting tourism demand: A review of empirical research," International Journal of Forecasting, Elsevier, vol. 11(3), pages 447-475, September.
    4. Zhang, Yishuo & Li, Gang & Muskat, Birgit & Law, Rob & Yang, Yating, 2020. "Group pooling for deep tourism demand forecasting," Annals of Tourism Research, Elsevier, vol. 82(C).
    5. Tussyadiah, Iis, 2020. "A review of research into automation in tourism: Launching the Annals of Tourism Research Curated Collection on Artificial Intelligence and Robotics in Tourism," Annals of Tourism Research, Elsevier, vol. 81(C).
    6. Ritchie, Brent W. & Jiang, Yawei, 2019. "A review of research on tourism risk, crisis and disaster management: Launching the annals of tourism research curated collection on tourism risk, crisis and disaster management," Annals of Tourism Research, Elsevier, vol. 79(C).
    7. Yılmaz, Engin, 2015. "Forecasting tourist arrivals to Turkey," MPRA Paper 68616, University Library of Munich, Germany.
    8. Emil Kraft & Dogan Keles & Wolf Fichtner, 2020. "Modeling of frequency containment reserve prices with econometrics and artificial intelligence," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 39(8), pages 1179-1197, December.
    9. Zhang, Hanyuan & Song, Haiyan & Wen, Long & Liu, Chang, 2021. "Forecasting tourism recovery amid COVID-19," Annals of Tourism Research, Elsevier, vol. 87(C).
    10. Zhenwei Li & Jing Han & Yuping Song, 2020. "On the forecasting of high‐frequency financial time series based on ARIMA model improved by deep learning," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 39(7), pages 1081-1097, November.
    11. Qiu, Richard T.R. & Park, Jinah & Li, ShiNa & Song, Haiyan, 2020. "Social costs of tourism during the COVID-19 pandemic," Annals of Tourism Research, Elsevier, vol. 84(C).
    12. Ahmad Muhammad & Fatih Külahcı & Pishtiwan Akram, 2020. "Modeling radon time series on the North Anatolian Fault Zone, Turkiye: Fourier transforms and Monte Carlo simulations," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 104(1), pages 979-996, October.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Davide Provenzano & Serena Volo, 2022. "Tourism recovery amid COVID-19: The case of Lombardy, Italy," Tourism Economics, , vol. 28(1), pages 110-130, February.
    2. Yang, Yang & Zhang, Carol X. & Rickly, Jillian M., 2021. "A review of early COVID-19 research in tourism: Launching the Annals of Tourism Research's Curated Collection on coronavirus and tourism1," Annals of Tourism Research, Elsevier, vol. 91(C).
    3. Jian-Wu Bi & Tian-Yu Han & Yanbo Yao, 2024. "Collaborative forecasting of tourism demand for multiple tourist attractions with spatial dependence: A combined deep learning model," Tourism Economics, , vol. 30(2), pages 361-388, March.
    4. Celeste Vong & Paulo Rita & Nuno António, 2021. "Health-Related Crises in Tourism Destination Management: A Systematic Review," Sustainability, MDPI, vol. 13(24), pages 1-28, December.
    5. Liu, Xing (Stella) & Wan, Lisa C. & Yi, Xiao (Shannon), 2022. "Humanoid versus non-humanoid robots: How mortality salience shapes preference for robot services under the COVID-19 pandemic?," Annals of Tourism Research, Elsevier, vol. 94(C).
    6. Leon John Mach, 2021. "Surf Tourism in Uncertain Times: Resident Perspectives on the Sustainability Implications of COVID-19," Societies, MDPI, vol. 11(3), pages 1-15, July.
    7. Eden Xiaoying Jiao & Jason Li Chen, 2019. "Tourism forecasting: A review of methodological developments over the last decade," Tourism Economics, , vol. 25(3), pages 469-492, May.
    8. Erica Mingotto & Michele Tamma, 2021. "Covid-19 and recovery strategies. Some insights from an ongoing exploratory study in the Italian hospitality industry: the case of the historic city centre of Venice," Working Papers 02, Department of Management, Università Ca' Foscari Venezia.
    9. Marcos Álvarez-Díaz & Manuel González-Gómez & María Soledad Otero-Giráldez, 2018. "Forecasting International Tourism Demand Using a Non-Linear Autoregressive Neural Network and Genetic Programming," Forecasting, MDPI, vol. 1(1), pages 1-17, September.
    10. Long Wen & Chang Liu & Haiyan Song, 2019. "Forecasting tourism demand using search query data: A hybrid modelling approach," Tourism Economics, , vol. 25(3), pages 309-329, May.
    11. Çalişkan, Uğur & Gursoy, Dogan & Özer, Özgür & Chi, Oscar Hengxuan, 2022. "Effects of Tourism on Local Residents’ Quality of Life, Happiness and Life Satisfaction: Moderating Role of the COVID-19 Risk Perceptions," Journal of Tourism, Sustainability and Well-being, Cinturs - Research Centre for Tourism, Sustainability and Well-being, University of Algarve, vol. 10(4), pages 274-291.
    12. Kourentzes, Nikolaos & Saayman, Andrea & Jean-Pierre, Philippe & Provenzano, Davide & Sahli, Mondher & Seetaram, Neelu & Volo, Serena, 2021. "Visitor arrivals forecasts amid COVID-19: A perspective from the Africa team," Annals of Tourism Research, Elsevier, vol. 88(C).
    13. Xi Wu & Adam Blake, 2023. "The Impact of the COVID-19 Crisis on Air Travel Demand: Some Evidence From China," SAGE Open, , vol. 13(1), pages 21582440231, January.
    14. Yılmaz, Engin, 2015. "Forecasting tourist arrivals to Turkey," MPRA Paper 68616, University Library of Munich, Germany.
    15. Ulrich Gunter, 2021. "Improving Hotel Room Demand Forecasts for Vienna across Hotel Classes and Forecast Horizons: Single Models and Combination Techniques Based on Encompassing Tests," Forecasting, MDPI, vol. 3(4), pages 1-36, November.
    16. Anna Jęczmyk & Jarosław Uglis & Jan Zawadka & Joanna Pietrzak-Zawadka & Monika Małgorzata Wojcieszak-Zbierska & Magdalena Kozera-Kowalska, 2023. "Impact of COVID-19 Pandemic on Tourist Travel Risk Perception and Travel Behaviour: A Case Study of Poland," IJERPH, MDPI, vol. 20(8), pages 1-20, April.
    17. Peng, Bo & Song, Haiyan & Crouch, Geoffrey I., 2014. "A meta-analysis of international tourism demand forecasting and implications for practice," Tourism Management, Elsevier, vol. 45(C), pages 181-193.
    18. José Antonio Donaire & Núria Galí & Raquel Camprubi, 2021. "Empty Summer: International Tourist Behavior in Spain during COVID-19," Sustainability, MDPI, vol. 13(8), pages 1-14, April.
    19. Choi, Sungwoo & Yeon, Jihwan & Song, Hyoung Ju & Hu, Jihao, 2023. "Information technology as a buffer against COVID-19," Annals of Tourism Research, Elsevier, vol. 98(C).
    20. Liu, Yuan-Yuan & Tseng, Fang-Mei & Tseng, Yi-Heng, 2018. "Big Data analytics for forecasting tourism destination arrivals with the applied Vector Autoregression model," Technological Forecasting and Social Change, Elsevier, vol. 130(C), pages 123-134.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:wly:jforec:v:41:y:2022:i:5:p:920-932. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Wiley Content Delivery (email available below). General contact details of provider: http://www3.interscience.wiley.com/cgi-bin/jhome/2966 .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.