DocumentCode :
684306
Title :
Ensembles of echo state networks for time series prediction
Author :
Wei Yao ; Zhigang Zeng ; Cheng Lian ; Huiming Tang
Author_Institution :
Sch. of Comput. Sci., South-Central Univ. for Nat., Wuhan, China
fYear :
2013
fDate :
19-21 Oct. 2013
Firstpage :
299
Lastpage :
304
Abstract :
In time series prediction tasks, dynamic models are less popular than static models, while they are more suitable for modeling the underlying dynamics of time series. In this paper, a novel architecture and supervised learning principle for recurrent neural networks, namely echo state networks, are adopted to build dynamic time series predictors. Ensemble techniques are employed to overcome the randomness and instability of echo state predictors, and a dynamic ensemble predictor is therefore established. The proposed predictor is tested in numerical experiments and different strategies for training the predictor are also comparatively studied. A case study is then conducted to test the predictor´s performance in realistic prediction tasks.
Keywords :
learning (artificial intelligence); recurrent neural nets; time series; dynamic ensemble predictor; dynamic model; echo state network; echo state predictor; ensemble technique; recurrent neural network; static model; supervised learning; time series prediction; Artificial neural networks; Training;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Advanced Computational Intelligence (ICACI), 2013 Sixth International Conference on
Conference_Location :
Hangzhou
Print_ISBN :
978-1-4673-6341-9
Type :
conf
DOI :
10.1109/ICACI.2013.6748520
Filename :
6748520
Link To Document :
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