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

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.