IDEAS home Printed from https://ideas.repec.org/a/pal/jorapm/v19y2020i1d10.1057_s41272-019-00221-6.html
   My bibliography  Save this article

Hotel daily demand forecasting for high-frequency and complex seasonality data: a case study in Thailand

Author

Listed:
  • Naragain Phumchusri

    (Chulalongkorn University)

  • Phoom Ungtrakul

    (Chulalongkorn University)

Abstract

Accurate hotel daily demand forecasting is an important input for hotel revenue management. This research presents forecasting models, both time series and causal methods, for a case study 4-star hotel in Phuket, Thailand. Holt–Winters, Box–Jenkins, Box–Cox transformation, ARMA errors, trend and multiple seasonal patterns (BATS), trigonometric BATS (TBATS), artificial neural network (ANN), and support vector regression are explored. For causal method, independent variables used as regressor inputs are transformed data observed in the past periods, the number of tourist arrivals from main countries to Phuket, Oil prices, exchange rate, etc. Model accuracy is measured using mean absolute percentage error (MAPE) and mean absolute error. Findings suggested that ANN outperforms other models with the lowest MAPE of 8.96%. It shows that Machine Learning techniques studied in this research outperform the advanced time series methods designed for complex seasonality data like BATS and TBATS. Unlike previous works, this research is a pioneer to introduce data transformation as inputs for machine learning models and to compare time series method and machine learning method for hotel daily demand forecasting. The results obtained can be applied to the case study hotel’s future planning about the forecasted number of left-over rooms so that they effectively allocate to their discounted online travel agent more effectively.

Suggested Citation

  • Naragain Phumchusri & Phoom Ungtrakul, 2020. "Hotel daily demand forecasting for high-frequency and complex seasonality data: a case study in Thailand," Journal of Revenue and Pricing Management, Palgrave Macmillan, vol. 19(1), pages 8-25, February.
    • Handle: RePEc:pal:jorapm:v:19:y:2020:i:1:d:10.1057_s41272-019-00221-6
    DOI: 10.1057/s41272-019-00221-6
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1057/s41272-019-00221-6
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1057/s41272-019-00221-6?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
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Peter R. Winters, 1960. "Forecasting Sales by Exponentially Weighted Moving Averages," Management Science, INFORMS, vol. 6(3), pages 324-342, April.
    2. Alysha M De Livera, 2010. "Automatic forecasting with a modified exponential smoothing state space framework," Monash Econometrics and Business Statistics Working Papers 10/10, Monash University, Department of Econometrics and Business Statistics.
    3. Weatherford, Larry R. & Kimes, Sheryl E., 2003. "A comparison of forecasting methods for hotel revenue management," International Journal of Forecasting, Elsevier, vol. 19(3), pages 401-415.
    4. Tay, Francis E. H. & Cao, Lijuan, 2001. "Application of support vector machines in financial time series forecasting," Omega, Elsevier, vol. 29(4), pages 309-317, August.
    5. E. Martinez-De-Pison & J. Fernandez-Ceniceros & A. V. Pernia-Espinoza & F. J. Martinez-De-Pison & Andres Sanz-Garcia, 2016. "Hotel Reservation Forecasting Using Flexible Soft Computing Techniques: A Case of Study in a Spanish Hotel," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 15(05), pages 1211-1234, September.
    6. J W Taylor, 2003. "Short-term electricity demand forecasting using double seasonal exponential smoothing," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 54(8), pages 799-805, August.
    7. Haensel, Alwin & Koole, Ger, 2011. "Booking horizon forecasting with dynamic updating: A case study of hotel reservation data," International Journal of Forecasting, Elsevier, vol. 27(3), pages 942-960, July.
    8. Haensel, Alwin & Koole, Ger, 2011. "Booking horizon forecasting with dynamic updating: A case study of hotel reservation data," International Journal of Forecasting, Elsevier, vol. 27(3), pages 942-960.
    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. Nantachai Kantanantha & Jiaranai Awichanirost, 2022. "Analyzing and forecasting online tour bookings using Google Analytics metrics," Journal of Revenue and Pricing Management, Palgrave Macmillan, vol. 21(3), pages 354-365, June.
    2. Dong Zhang & Chong Wu, 2023. "What online review features really matter? An explainable deep learning approach for hotel demand forecasting," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 74(9), pages 1100-1117, September.

    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. 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.
    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. Zvi Schwartz & Timothy Webb & Jean-Pierre I van der Rest & Larissa Koupriouchina, 2021. "Enhancing the accuracy of revenue management system forecasts: The impact of machine and human learning on the effectiveness of hotel occupancy forecast combinations across multiple forecasting horizo," Tourism Economics, , vol. 27(2), pages 273-291, March.
    4. E. Martinez-De-Pison & J. Fernandez-Ceniceros & A. V. Pernia-Espinoza & F. J. Martinez-De-Pison & Andres Sanz-Garcia, 2016. "Hotel Reservation Forecasting Using Flexible Soft Computing Techniques: A Case of Study in a Spanish Hotel," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 15(05), pages 1211-1234, September.
    5. Larry Weatherford, 2016. "The history of forecasting models in revenue management," Journal of Revenue and Pricing Management, Palgrave Macmillan, vol. 15(3), pages 212-221, July.
    6. Timothy Webb, 2022. "Forecasting at capacity: the bias of unconstrained forecasts in model evaluation," Journal of Revenue and Pricing Management, Palgrave Macmillan, vol. 21(6), pages 645-656, December.
    7. Apostolos Ampountolas, 2019. "Forecasting hotel demand uncertainty using time series Bayesian VAR models," Tourism Economics, , vol. 25(5), pages 734-756, August.
    8. Gardner, Everette Jr., 2006. "Exponential smoothing: The state of the art--Part II," International Journal of Forecasting, Elsevier, vol. 22(4), pages 637-666.
    9. Larissa Koupriouchina & Jean-Pierre van der Rest & Zvi Schwartz, 2023. "Judgmental Adjustments of Algorithmic Hotel Occupancy Forecasts: Does User Override Frequency Impact Accuracy at Different Time Horizons?," Tourism Economics, , vol. 29(8), pages 2143-2164, December.
    10. Corberán-Vallet, Ana & Bermúdez, José D. & Vercher, Enriqueta, 2011. "Forecasting correlated time series with exponential smoothing models," International Journal of Forecasting, Elsevier, vol. 27(2), pages 252-265, April.
    11. Trull, Oscar & García-Díaz, J. Carlos & Troncoso, Alicia, 2021. "One-day-ahead electricity demand forecasting in holidays using discrete-interval moving seasonalities," Energy, Elsevier, vol. 231(C).
    12. Haensel, Alwin & Koole, Ger, 2011. "Booking horizon forecasting with dynamic updating: A case study of hotel reservation data," International Journal of Forecasting, Elsevier, vol. 27(3), pages 942-960, July.
    13. Theresa Maria Rausch & Tobias Albrecht & Daniel Baier, 2022. "Beyond the beaten paths of forecasting call center arrivals: on the use of dynamic harmonic regression with predictor variables," Journal of Business Economics, Springer, vol. 92(4), pages 675-706, May.
    14. Karamaziotis, Panagiotis I. & Raptis, Achilleas & Nikolopoulos, Konstantinos & Litsiou, Konstantia & Assimakopoulos, Vassilis, 2020. "An empirical investigation of water consumption forecasting methods," International Journal of Forecasting, Elsevier, vol. 36(2), pages 588-606.
    15. Hill, Arthur V. & Zhang, Weiyong & Burch, Gerald F., 2015. "Forecasting the forecastability quotient for inventory management," International Journal of Forecasting, Elsevier, vol. 31(3), pages 651-663.
    16. J D Bermúdez & J V Segura & E Vercher, 2006. "Improving demand forecasting accuracy using nonlinear programming software," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 57(1), pages 94-100, January.
    17. Yelena Vardanyan & Henrik Madsen, 2019. "Stochastic Bilevel Program for Optimal Coordinated Energy Trading of an EV Aggregator," Energies, MDPI, vol. 12(20), pages 1-18, October.
    18. Noelia Oses & Jon Kepa Gerrikagoitia & Aurkene Alzua, 2016. "Modelling and prediction of a destination’s monthly average daily rate and occupancy rate based on hotel room prices offered online," Tourism Economics, , vol. 22(6), pages 1380-1403, December.
    19. Óscar Trull & J. Carlos García-Díaz & Alicia Troncoso, 2019. "Application of Discrete-Interval Moving Seasonalities to Spanish Electricity Demand Forecasting during Easter," Energies, MDPI, vol. 12(6), pages 1-16, March.
    20. Rice, William L. & Park, So Young & Pan, Bing & Newman, Peter, 2019. "Forecasting campground demand in US national parks," Annals of Tourism Research, Elsevier, vol. 75(C), pages 424-438.

    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:pal:jorapm:v:19:y:2020:i:1:d:10.1057_s41272-019-00221-6. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.palgrave.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.