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
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;
Conference_Titel :
Communications, Control and Signal Processing (ISCCSP), 2014 6th International Symposium on
Conference_Location :
Athens
DOI :
10.1109/ISCCSP.2014.6877938