Ensemble Deep Learning for Regression and Time Series Forecasting

被引：0

作者：

Qiu, Xueheng ^{[1
]}

Zhang, Le ^{[1
]}

Ren, Ye ^{[1
]}

Suganthan, P. N. ^{[1
]}

Amaratunga, Gehan ^{[2
]}

机构：

[1] Nanyang Technol Univ, Sch Elect & Elect Engn, Singapore, Singapore

[2] Univ Cambridge, Dept Engn, Cambridge CB2 1TN, England

来源：

2014 IEEE SYMPOSIUM ON COMPUTATIONAL INTELLIGENCE IN ENSEMBLE LEARNING (CIEL) | 2014年

关键词：

Deep learning; Ensemble method; Time series forecasting; Regression; Load demand forecasting; Neural Networks; Support Vector Regression; SUPPORT VECTOR REGRESSION; ALGORITHM;

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

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.

引用

页码：21 / 26

页数：6

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