IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v11y2019i3p793-d203139.html
   My bibliography  Save this article

Development of Fuzzy Time Series Model for Hotel Occupancy Forecasting

Author

Listed:
  • Rashad Aliyev

    (Department of Mathematics, Faculty of Arts and Sciences, Eastern Mediterranean University, Famagusta 99628, North Cyprus, via Mersin 10, Turkey)

  • Sara Salehi

    (Department of Mathematics, Faculty of Arts and Sciences, Eastern Mediterranean University, Famagusta 99628, North Cyprus, via Mersin 10, Turkey)

  • Rafig Aliyev

    (Warwick Business School, University of Warwick, Coventry CV4 7AL, UK)

Abstract

Receiving appropriate forecast accuracy is important in many countries’ economic activities, and developing effective and precise time series model is critical issue in tourism demand forecasting. In this paper, fuzzy rule-based system model for hotel occupancy forecasting is developed by analyzing 40 months’ time series data and applying fuzzy c-means clustering algorithm. Based on the values of root mean square error and mean absolute percentage error which are metrics for measuring forecast accuracy, it is defined that the model with 7 clusters and 4 inputs is the optimal forecasting model for hotel occupancy.

Suggested Citation

  • Rashad Aliyev & Sara Salehi & Rafig Aliyev, 2019. "Development of Fuzzy Time Series Model for Hotel Occupancy Forecasting," Sustainability, MDPI, vol. 11(3), pages 1-13, February.
    • Handle: RePEc:gam:jsusta:v:11:y:2019:i:3:p:793-:d:203139
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/11/3/793/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/11/3/793/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. 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.
    2. Chang-Jui Lin & Hsueh-Fang Chen & Tian-Shyug Lee, 2011. "Forecasting Tourism Demand Using Time Series, Artificial Neural Networks and Multivariate Adaptive Regression Splines:Evidence from Taiwan," International Journal of Business Administration, International Journal of Business Administration, Sciedu Press, vol. 2(2), pages 14-24, May.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Binru Zhang & Yulian Pu & Yuanyuan Wang & Jueyou Li, 2019. "Forecasting Hotel Accommodation Demand Based on LSTM Model Incorporating Internet Search Index," Sustainability, MDPI, vol. 11(17), pages 1-14, August.
    2. Tianxiang Zheng & Shaopeng Liu & Zini Chen & Yuhan Qiao & Rob Law, 2020. "Forecasting Daily Room Rates on the Basis of an LSTM Model in Difficult Times of Hong Kong: Evidence from Online Distribution Channels on the Hotel Industry," Sustainability, MDPI, vol. 12(18), pages 1-17, September.
    3. Mohamed Hamitouche & Jose-Luis Molina, 2022. "A Review of AI Methods for the Prediction of High-Flow Extremal Hydrology," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(10), pages 3859-3876, August.
    4. Feng, Qianqian & Sun, Xiaolei & Hao, Jun & Li, Jianping, 2021. "Predictability dynamics of multifactor-influenced installed capacity: A perspective of country clustering," Energy, Elsevier, vol. 214(C).

    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. Oscar Claveria & Enric Monte & Salvador Torra, 2016. "Modelling cross-dependencies between Spain’s regional tourism markets with an extension of the Gaussian process regression model," SERIEs: Journal of the Spanish Economic Association, Springer;Spanish Economic Association, vol. 7(3), pages 341-357, August.
    2. Oscar Claveria & Enric Monte & Salvador Torra, 2017. "“Regional tourism demand forecasting with machine learning models: Gaussian process regression vs. neural network models in a multiple-input multiple-output setting”," AQR Working Papers 201701, University of Barcelona, Regional Quantitative Analysis Group, revised Jan 2017.
    3. Oscar Claveria & Enric Monte & Salvador Torra, 2018. "“A regional perspective on the accuracy of machine learning forecasts of tourism demand based on data characteristics”," AQR Working Papers 201802, University of Barcelona, Regional Quantitative Analysis Group, revised Apr 2018.
    4. Beria, Paolo & Laurino, Antonio, 2016. "Determinants of daily fluctuations in air passenger volumes. The effect of events and holidays on Milan Malpensa airport," Journal of Air Transport Management, Elsevier, vol. 53(C), pages 73-84.
    5. Saayman, Andrea & Viljoen, Armand & Saayman, Melville, 2018. "Africa’s outbound tourism: An Almost Ideal Demand System perspective," Annals of Tourism Research, Elsevier, vol. 73(C), pages 141-158.
    6. Hongjuan Song & Yushi Jiang, 2019. "Dynamic pricing decisions by potential tourists under uncertainty: The effects of tourism advertising," Tourism Economics, , vol. 25(2), pages 213-234, March.
    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. Min Gon Chung & Tao Pan & Xintong Zou & Jianguo Liu, 2018. "Complex Interrelationships between Ecosystem Services Supply and Tourism Demand: General Framework and Evidence from the Origin of Three Asian Rivers," Sustainability, MDPI, vol. 10(12), pages 1-21, December.
    9. Gozgor, Giray & Lau, Chi Keung Marco & Zeng, Yan & Lin, Zhibin, 2019. "The effectiveness of the legal system and inbound tourism," Annals of Tourism Research, Elsevier, vol. 76(C), pages 24-35.
    10. 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.
    11. Helena Nemec Rudež, 2020. "Is International Tourism Growth Supported by Increased Tourism Receipts?," Academica Turistica - Tourism and Innovation Journal, University of Primorska Press, vol. 13(2), pages 153-156.
    12. Oscar Claveria & Enric Monte & Salvador Torra, 2015. "“Multiple-input multiple-output vs. single-input single-output neural network forecasting”," AQR Working Papers 201502, University of Barcelona, Regional Quantitative Analysis Group, revised Jan 2015.
    13. Balli, Hatice Ozer & Tsui, Wai Hong Kan & Balli, Faruk, 2019. "Modelling the volatility of international visitor arrivals to New Zealand," Journal of Air Transport Management, Elsevier, vol. 75(C), pages 204-214.
    14. Song, Haiyan & Wen, Long & Liu, Chang, 2019. "Density tourism demand forecasting revisited," Annals of Tourism Research, Elsevier, vol. 75(C), pages 379-392.
    15. Hulya Bakirtas & Vildan Gulpinar Demirci, 2022. "Can Google Trends data provide information on consumer’s perception regarding hotel brands?," Information Technology & Tourism, Springer, vol. 24(1), pages 57-83, March.
    16. Llano, Carlos & Pardo, Juan & Pérez-Balsalobre, Santiago & Pérez, Julián, 2023. "Estimating multicountry tourism flows by transport mode," Annals of Tourism Research, Elsevier, vol. 103(C).
    17. Dr. Murat çuhadar & Iclal Cogurcu & Ceyda Kukrer, 2014. "Modelling and Forecasting Cruise Tourism Demand to Izmir by Different Artificial Neural Network Architectures," International Journal of Business and Social Research, LAR Center Press, vol. 4(3), pages 12-28, March.
    18. Marisol Valencia Cárdenas & Juan Gabriel Vanegas López & Juan Carlos Correa Morales & Jorge Aníbal Restrepo Morales, 2017. "Comparing forecasts for tourism dynamics in Medellín, Colombia," Lecturas de Economía, Universidad de Antioquia, Departamento de Economía, issue 86, pages 199-230, Enero - J.
    19. Chengyuan Zhang & Fuxin Jiang & Shouyang Wang & Shaolong Sun, 2020. "A New Decomposition Ensemble Approach for Tourism Demand Forecasting: Evidence from Major Source Countries," Papers 2002.09201, arXiv.org.
    20. Valencia Cárdenas, Marisol & Vanegas López, Juan Gabriel & Correa Morales, Juan Carlos & Restrepo Morales, Jorge Aníbal, 2016. "Comparación de pronósticos para la dinámica del turismo en Medellín, Colombia," Revista Lecturas de Economía, Universidad de Antioquia, CIE, issue 86, pages 199-230, December.

    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:gam:jsusta:v:11:y:2019:i:3:p:793-:d:203139. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    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.