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.
['Mark van Heeswijk', 'Yoan Miche', 'Tiina Lindh-Knuutila', 'Peter A. J. Hilbers', 'Timo Honkela', 'Erkki Oja', 'Amaury Lendasse']
Adaptive Ensemble Models of Extreme Learning Machines for Time Series Prediction