This study applied random forest (RF) and empirical mode decomposition (EMD)techniques to tourism demand forecasting. The aim of this study is to examine the feasibility of the proposed EMD-RF model in tourism demand forecasting. For this purpose, the original tourism demand series were first decomposed into a finite, and often small, number of intrinsic mode functions (IMFs). Then a random forest model was used to model each of the extracted IMFs, so that the tendencies of these IMFs could be accurately predicted. The monthly tourist arrivals to Taiwan from Japanese
during 2002–2010 were employed as the data set. The proposed model was verified by comparing it with back-propagation neural network (BPNN) and multiple regression (MR) models. The experimental results demonstrate that EMD-RF outperforms the EMD-BPNN and EMD-MR models based on mean absolute percentage error (MAPE).