A Wavelet Support Vector Machine Combination Model for Singapore Tourist Arrival to Malaysia

International Research and Innovation Summit 2017

Rafidah, A.^1 ; Shabri, Ani^2 ; Nurulhuda, A.^1 ; Suhaila, Y.^1

Technical Foundation Department, Universiti Kuala Lumpur (UniKL), Malaysian Institute of Industrial Technology, Persiaran Sinaran Ilmu, Bandar Seri Alam, Johor

81750, Malaysia^1

Departments of Mathematics, Science Faculty, University of Technology Malaysia, Skudai, Johor, Malaysia^2

关键词: Combination models;    Developed model;    Kernel function;    Malaysia;    Singapore;    Single models;    Time series prediction;    Wavelet support vector machines;   

Others  :  https://iopscience.iop.org/article/10.1088/1757-899X/226/1/012077/pdf
DOI  :  10.1088/1757-899X/226/1/012077

来源: IOP

【 摘 要 】

In this study, wavelet support vector machine model (WSVM) is proposed and applied for monthly data Singapore tourist time series prediction. The WSVM model is combination between wavelet analysis and support vector machine (SVM). In this study, we have two parts, first part we compare between the kernel function and second part we compare between the developed models with single model, SVM. The result showed that kernel function linear better than RBF while WSVM outperform with single model SVM to forecast monthly Singapore tourist arrival to Malaysia.