Ensembles of echo state networks for time series prediction

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.