A hybrid method for time series prediction using EMD and SVR

Bahadir Bican, Yusuf Yaslan

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

15 Citations (Scopus)

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.

Original languageEnglish
Title of host publicationISCCSP 2014 - 2014 6th International Symposium on Communications, Control and Signal Processing, Proceedings
PublisherIEEE Computer Society
Pages566-569
Number of pages4
ISBN (Print)9781479928903
DOIs
Publication statusPublished - 2014
Event6th International Symposium on Communications, Control and Signal Processing, ISCCSP 2014 - Athens, Greece
Duration: 21 May 201423 May 2014

Publication series

NameISCCSP 2014 - 2014 6th International Symposium on Communications, Control and Signal Processing, Proceedings

Conference

Conference6th International Symposium on Communications, Control and Signal Processing, ISCCSP 2014
Country/TerritoryGreece
CityAthens
Period21/05/1423/05/14

Keywords

  • empirical mode decomposition
  • forecasting
  • regression analysis
  • support vector machines
  • Time series analysis

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