Improving daily occupancy forecasting accuracy for hotels based on EEMD-ARIMA model

Abstract

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.

Weekly Hotel Occupancy Forecasting of a Tourism Destination

“…Zhang et al adopted EEMD-ARIMA for daily hotel occupancy forecasting for an individual hotel. This research validated that the EEMD-ARIMA model had better forecasting ability than the ARIMA model, especially in the short term [7].…”

“…After being modified and tested by relatively stable weekly data, a modified EEMD-ARIMA can achieve more accurate medium-long term forecasting. In addition, the previous study [7] had not tested other data samples, while the modified EEMD-ARIMA model are considered more reliable, since it was tested on four other data series. Last but not least, since the hotel occupancy is a symbol of tourism demand, the empirical results can not only support hotel managers for decision making, they can also provide support for tourism destinations' management, resource allocation, and sustainable development.…”

“…As shown in Figure 5, the predicted series does not predict the actual data series accurately. In the previous EEMD-ARIMA forecasting [7], the original signals were nonstationary. When they were decomposed into relatively stationary signals, the additive effect of all of the IMFs enabled them to overcome the drawback of the complexity in the original signals and improved the forecasting accuracy.…”

“…Ensemble empirical mode decomposition (EEMD) was an improvement to the empirical mode decomposition (EMD) that emerged in 2005 [6]. It is believed that combining the EEMD method and ARIMA model helps remove interference signals from the original tourism time series, resulting in a more accurate trend for better forecasting [7,8]. The present study introduced a modified EEMD-ARIMA model, and used it to generate predictions of hotel occupancy.…”

A decomposition-ensemble approach for tourism forecasting

“…A model comparison shows that the proposed model outperforms the single BPNN and ARIMA models. Moreover, Zhang, Wu et al (2017) proposed a decomposition-ensemble approach based on EEMD and ARIMA for predicting daily occupancy of an individual hotel. Result shows that the proposed method can improve forecasting accuracy compared to the ARIMA method, especially for short-term forecasting.…”