Resumen de How do Less Advanced Forecasting Methods Perform on Weekly RevPAR in Different Forecasting Horizons Following the Recession?
Tianshu Zheng, Barry A.N Bloom, Xiaofan Wang
The purpose of this study is to examine the performance of three smoothing methods on forecasting weekly revenue per Available room (revPAr) following the recent recession in comparison to more sophisticated time series forecasting methods. The results of this study show that simpler methods perform better. Simple moving Average and Single Exponential Smoothing outperformed Autoregressive Integrated moving Average and Artificial Neural Networks in all 10 of 5-week forecasting horizons, which suggests accurate weekly revPAr forecasting in both short and long term can be achieved with simple, easy-to-learn, yet effective methods. The findings of the study are expected to not only contribute to the limited literature of revPAr forecasting, but also provide practitioners with empirical evidence for selecting appropriate time series forecasting methods for weekly revPAr forecasting in different time horizons, particularly following an economic downturn.
