Open AccessApplication of Empirical mode Decomposition with Wavelets Support Vector Machine in time Series Data
Author(s) -
UniKL Mitec,
A. Rafidah,
Ani Shabri,
Ernie Mazuin
Publication year - 2020
Publication title -
international journal of innovative technology and exploring engineering
Language(s) - English
Resource type - Journals
ISSN - 2278-3075
DOI - 10.35940/ijitee.c9225.019320
Subject(s) - hilbert–huang transform , support vector machine , artificial intelligence , series (stratigraphy) , computer science , mode (computer interface) , decomposition , wavelet , pattern recognition (psychology) , time series , tourism , data mining , empirical research , machine learning , mathematics , statistics , geography , telecommunications , geology , paleontology , archaeology , white noise , operating system , ecology , biology
This paper mainly discussed on the forecast of Thailand tourist visiting Malaysia. This paper proposed a three-stage technique in which the empirical mode decomposition (EMD) is combined with wavelet methods and support vector machine model. We used the proposed technique, EMD_WSVM to forecast two ASEAN country tourism timeseries. Detail experiments are conducted for the proposed method, in which there is a comparison between the EMD_WSVM, WSVM and SVM methods. The proposed EMD_WSVM model is determined to be dominant to the other methods in predicting the number of tourist arrivals