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
Link To Document