Adaptive Ensemble Models of Extreme Learning Machines for Time Series Prediction

Abstract. 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.

• Type: Conference
• Year: 2009
• Where: ICANN
• Authors: Mark van Heeswijk, Yoan Miche, Tiina Lindh-Knuutila, Peter A. J. Hilbers, Timo Honkela, Erkki Oja, Amaury Lendasse
• Keywords: Adaptive Ensemble Model, Artificial Intelligence, ICANN 2009, One-step Ahead Prediction, Time Series