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A Forecasting Model for Short Term Tourist Arrival Based on the Empirical Mode Decomposition and Support Vector Regression

In: Proceedings of 2013 4th International Asia Conference on Industrial Engineering and Management Innovation (IEMI2013)

Author

Listed:
  • Jun Wang

    (Sichuan University)

  • Ming-ming Hu

    (Sichuan University)

  • Peng Ge

    (Sichuan University)

  • Pei-yu Ren

    (Sichuan University)

  • Rong Zhao

    (Sichuan University)

Abstract

In this study, a hybrid forecasting model based on Empirical Mode Decomposition (EMD) and Least Squares Support Vector Machines (LSSVMs) is proposed to predict tourism demand (i.e. the maximal number of arrivals in a short time interval). The proposed approach first uses EMD decompose the complicated data into a finite set of Intrinsic Mode Functions (IMFs) and a residue, then the IMF components and residue are modeled and forecasted using Least Squares Support Vector Machines, next, the forecasting values are obtained by the sum of these prediction results. In order to evaluate the performance of the proposed approach, the maximal values of tourist arrive in 1 min time interval is used as an illustrative example. Experimental results show that the proposed model outperforms the single LSSVM model without EMD preprocessing.

Suggested Citation

  • Jun Wang & Ming-ming Hu & Peng Ge & Pei-yu Ren & Rong Zhao, 2014. "A Forecasting Model for Short Term Tourist Arrival Based on the Empirical Mode Decomposition and Support Vector Regression," Springer Books, in: Ershi Qi & Jiang Shen & Runliang Dou (ed.), Proceedings of 2013 4th International Asia Conference on Industrial Engineering and Management Innovation (IEMI2013), edition 127, pages 1009-1021, Springer.
    • Handle: RePEc:spr:sprchp:978-3-642-40060-5_97
    DOI: 10.1007/978-3-642-40060-5_97
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