Hydrological time series forecasting remains a difficult task due to its complicated nonlinear, non-stationary and multi-scale characteristics. To solve this difficulty and improve the prediction accuracy, a novel four-stage hybrid model is proposed for hydrological time series forecasting based on the principle of 'denoising, decomposition and ensemble'. The proposed model has four stages, i.e...

Abstract: In this paper a novel Ensemble Empirical Mode decomposition (EEMD) and adaptive filtering is proposed to filter out Gaussian noise and contact noise contained in raw biomedical signals. Real Biomedical signals from the MIT-BIH database are used to validate the performance of the proposed method. It has been observed that original signals can be significantly enhanced by using the prop...