Please use this identifier to cite or link to this item:
Title: Hybrid Approach of an Empirical Mode Decomposition and Wavelet Support Vector Machine for Forecasting Singapore Tourist Arrivals to Malaysia
Authors: A. Rafidah
A. Shabri
Keywords: empirical mode decomposition (EMD)
support vector machine (SVM) and Wavelet support vector machine (WSVM)
Issue Date: 22-Nov-2018
Abstract: Time series modelling and forecasting has fundamental importance to various practical domains. Thus, a lot of active research works is going on in this subject during several years. Many important models have been proposed in literature for improving the accuracy and efficiency of time series modelling and forecasting.This study presents a hybrid model empirical mode decomposition (EMD), wavelet and support vector machine (SVM) for Singapore tourist arrival to Malaysia. The EMD method is employed to decompose the monthly data tourist arrival to several intrinsic mode functions (IMFs) and residual. Wavelet support vector machine (WSVM) combines the advantages of wavelet analysis and SVM and improves the learning efficiency and forecasting accuracy. The weight of combination model is decided by forecasting precision of EMD model and WSVM model. At last, the EMD_WSVM model is used to forecast monthly data of tourist arrivals from Singapore to Malaysia and the results show that the proposed combination model has better performance on forecasting accuracy compared with the other models.
Appears in Collections:Conference Papers

Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.