Resumen de Improving daily occupancy forecasting accuracy for hotels based on EEMD-ARIMA model
Gaojun Zhang, Jinfeng Wu, Bing Pan, Junyi Li, Minjie Ma, Muzi Zhang, Jian Wang
Predicting daily occupancy is extremely important for the revenue management of individual hotels. However, daily occupancy can fluctuate widely and is difficult to forecast accurately based on existing forecasting methods. In this article, ensemble empirical mode decomposition (EEMD)—a novel method—is introduced, and an individual hotel is chosen to test the effectiveness of EEMD in combination with an autoregressive integrated moving average (ARIMA). Result shows that this novel method, EEMD-ARIMA, can improve forecasting accuracy compared to the popular ARIMA method, especially for short-term forecasting
