• DocumentCode
    1783953
  • Title

    A hybrid method for time series prediction using EMD and SVR

  • Author

    Bican, Bahadir ; Yaslan, Yusuf

  • Author_Institution
    Dept. of Comput. Eng., Istanbul Tech. Univ., Istanbul, Turkey
  • fYear
    2014
  • fDate
    21-23 May 2014
  • Firstpage
    566
  • Lastpage
    569
  • Abstract
    Forecasting in several areas such as stock price, electricity power consumption, tourist arrival rates or capacity planning allows us to give decisions for future events. The rising up or falling down of the values can support researchers, economists or investors while giving their important decisions. This study aims to forecast the directional movements of electricity load demands and evaluates the performance on 3 load datasets. In experimental results, the proposed Empirical Mode Decomposition (EMD) and Support Vector Regression (SVR) based hybrid method is compared with single SVR. It is observed that the proposed EMD-SVR method outperforms the single SVR performance on direction measurements including Direction Accuracy, Correct Up and Correct Down trends.
  • Keywords
    load forecasting; power engineering computing; regression analysis; support vector machines; time series; EMD; SVR; correct down trends; correct up; direction accuracy; direction measurements; electricity load demand forecasting; empirical mode decomposition; support vector regression; time series prediction; Electricity; Feature extraction; Forecasting; Prediction algorithms; Support vector machines; Time series analysis; Training; Time series analysis; empirical mode decomposition; forecasting; regression analysis; support vector machines;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Communications, Control and Signal Processing (ISCCSP), 2014 6th International Symposium on
  • Conference_Location
    Athens
  • Type

    conf

  • DOI
    10.1109/ISCCSP.2014.6877938
  • Filename
    6877938