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Improving Hotel Room Demand Forecasts for Vienna across Hotel Classes and Forecast Horizons: Single Models and Combination Techniques Based on Encompassing Tests

Department of Tourism and Service Management, Modul University Vienna, 1190 Vienna, Austria
Academic Editor: Konstantinos Nikolopoulos
Forecasting 2021, 3(4), 884-919; https://doi.org/10.3390/forecast3040054
Received: 1 November 2021 / Revised: 23 November 2021 / Accepted: 24 November 2021 / Published: 27 November 2021
(This article belongs to the Special Issue Tourism Forecasting: Time-Series Analysis of World and Regional Data)
The present study employs daily data made available by the STR SHARE Center covering the period from 1 January 2010 to 31 January 2020 for six Viennese hotel classes and their total. The forecast variable of interest is hotel room demand. As forecast models, (1) Seasonal Naïve, (2) Error Trend Seasonal (ETS), (3) Seasonal Autoregressive Integrated Moving Average (SARIMA), (4) Trigonometric Seasonality, Box–Cox Transformation, ARMA Errors, Trend and Seasonal Components (TBATS), (5) Seasonal Neural Network Autoregression (Seasonal NNAR), and (6) Seasonal NNAR with an external regressor (seasonal naïve forecast of the inflation-adjusted ADR) are employed. Forecast evaluation is carried out for forecast horizons h = 1, 7, 30, and 90 days ahead based on rolling windows. After conducting forecast encompassing tests, (a) mean, (b) median, (c) regression-based weights, (d) Bates–Granger weights, and (e) Bates–Granger ranks are used as forecast combination techniques. In the relative majority of cases (i.e., in 13 of 28), combined forecasts based on Bates–Granger weights and on Bates–Granger ranks provide the highest level of forecast accuracy in terms of typical measures. Finally, the employed methodology represents a fully replicable toolkit for practitioners in terms of both forecast models and forecast combination techniques. View Full-Text
Keywords: forecast combination; forecast encompassing tests; hotel room demand forecasting; hotel classes; neural network autoregression; multiple seasonal patterns forecast combination; forecast encompassing tests; hotel room demand forecasting; hotel classes; neural network autoregression; multiple seasonal patterns
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MDPI and ACS Style

Gunter, U. Improving Hotel Room Demand Forecasts for Vienna across Hotel Classes and Forecast Horizons: Single Models and Combination Techniques Based on Encompassing Tests. Forecasting 2021, 3, 884-919. https://doi.org/10.3390/forecast3040054

AMA Style

Gunter U. Improving Hotel Room Demand Forecasts for Vienna across Hotel Classes and Forecast Horizons: Single Models and Combination Techniques Based on Encompassing Tests. Forecasting. 2021; 3(4):884-919. https://doi.org/10.3390/forecast3040054

Chicago/Turabian Style

Gunter, Ulrich. 2021. "Improving Hotel Room Demand Forecasts for Vienna across Hotel Classes and Forecast Horizons: Single Models and Combination Techniques Based on Encompassing Tests" Forecasting 3, no. 4: 884-919. https://doi.org/10.3390/forecast3040054

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