Ensemble deep learning for regression and time series forecasting

Author

Xueheng Qiu ; Le Zhang ; Ye Ren ; Suganthan, P. ; Amaratunga, Gehan

Author_Institution

Sch. of Electr. & Electron. Eng., Nanyang Technol. Univ., Singapore, Singapore

fYear

2014

fDate

9-12 Dec. 2014

Firstpage

1

Lastpage

6

Abstract

In this paper, for the first time, an ensemble of deep learning belief networks (DBN) is proposed for regression and time series forecasting. Another novel contribution is to aggregate the outputs from various DBNs by a support vector regression (SVR) model. We show the advantage of the proposed method on three electricity load demand datasets, one artificial time series dataset and three regression datasets over other benchmark methods.

Keywords

belief networks; learning (artificial intelligence); load forecasting; neural nets; power engineering computing; regression analysis; support vector machines; time series; DBN; SVR model; artificial time series dataset; deep learning belief network; electricity load demand dataset; ensemble deep learning; regression dataset; regression forecasting; support vector regression model; time series forecasting; Forecasting; Learning systems; Load modeling; Neural networks; Support vector machines; Time series analysis; Training; Deep learning; Ensemble method; Load demand forecasting; Neural Networks; Regression; Support Vector Regression; Time series forecasting;

fLanguage

English

Publisher

ieee

Conference_Titel

Computational Intelligence in Ensemble Learning (CIEL), 2014 IEEE Symposium on

Conference_Location

Orlando, FL

Type

conf

DOI

10.1109/CIEL.2014.7015739

Filename

7015739