A Hybrid Model by Empirical Mode Decomposition and Support Vector Regression for Tourist Arrivals Forecasting

Abstract

Abstract This study develops a new hybrid model by integrating empirical mode decomposition (EMD) and support vector regression (SVR) for tourist arrivals forecasting. The proposed approach first uses EMD, which can adaptively decompose the complicated raw data into a finite set of intrinsic mode functions (IMFs) and a residue. After identifying the IMF components and residue, they are then modeled and forecasted using SVR. The final forecasting value can be obtained by the sum of these prediction results. Real data sets of international tourist arrivals to Taiwan were used. Experimental results show the effectiveness of the hybrid model when comparing it with other approaches.