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

    Volume 41, Issue 3 (May 2013)

    ISSN: 0090-3973

    CODEN: JTEOAD

    Published Online: 27 March 2013

    Page Count: 8


    Lai, Ming-Cheng
    Graduate Institute of Business Administration, National Taipei College of Business, Taipei City,

    Yeh, Ching-Chiang
    Dept. of Business Administration, National Taipei College of Business, Zhongzheng District, Taipei City,

    Shieh, Lon-Fon
    Dept. of Business Management, National United Univ., Miaoli,

    (Received 24 April 2012; accepted 27 November 2012)

    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.


    Paper ID: JTE20120120

    DOI: 10.1520/JTE20120120

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    Title A Hybrid Model by Empirical Mode Decomposition and Support Vector Regression for Tourist Arrivals Forecasting
    Symposium , 0000-00-00
    Committee F32