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Cited by 368

The DOI matches its Crossref record; 7 of the 7 references it cites were found in a registry or an index.

Ensemble deep learning for regression and time series forecasting

Qiu, Xueheng, Zhang, Le, Ren, Ye, Suganthan, P., Amaratunga, Gehan·2014·2014 IEEE Symposium on Computational Intelligence in Ensemble Learning (CIEL)

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.

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Metadata

Authors
Qiu, X., Zhang, L., Ren, Y., Suganthan, P., & Amaratunga, G.
Venue
2014 IEEE Symposium on Computational Intelligence in Ensemble Learning (CIEL)
Publisher
IEEE
Published
2014-12
Pages
1-6
OpenAlex
W2007272376
Cited by
368
References
7
FWCI
17.47
Type
conference paper
Language
en
Open access
closed

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CoChat. (2026). Citation check: Ensemble deep learning for regression and time series forecasting. https://app.cochat.ai/papers/10.1109/ciel.2014.7015739
The paper
Qiu, X., Zhang, L., Ren, Y., Suganthan, P., & Amaratunga, G. (2014). Ensemble deep learning for regression and time series forecasting. 2014 IEEE Symposium on Computational Intelligence in Ensemble Learning (CIEL). https://doi.org/10.1109/ciel.2014.7015739

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.