Adaptive Ensemble Models of Extreme Learning Machines for Time Series Prediction
Mark van Heeswijk, Yoan Miche, Tiina Lindh-Knuutila, Peter Hilbers, Timo Honkela, Erkki Oja and Amaury Lendasse
Artificial Neural Networks – ICANN 2009
Lecture Notes in Computer Science
Springer Berlin / Heidelberg
In this paper, we investigate the application of adaptive ensemble models of Extreme Learning Machines (ELMs) to the problem of one-step ahead prediction in (non)stationary time series. We verify that the method works on stationary time series and test the adaptivity of the ensemble model on a nonstationary time series. In the experiments, we show that the adaptive ensemble model achieves a test error comparable to the best methods, while keeping adaptivity. Moreover, it has low computational cost.