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|Title:||Chaotic time series prediction with a global model: Artificial neural network||Authors:||Karunasinghe, D.S.K.
|Keywords:||Artificial neural network
|Issue Date:||30-May-2006||Citation:||Karunasinghe, D.S.K., Liong, S.-Y. (2006-05-30). Chaotic time series prediction with a global model: Artificial neural network. Journal of Hydrology 323 (1-4) : 92-105. ScholarBank@NUS Repository. https://doi.org/10.1016/j.jhydrol.2005.07.048||Abstract:||An investigation on the performance of artificial neural network (ANN) as a global model over the widely used local models (local averaging technique and local polynomials technique) in chaotic time series prediction is conducted. A theoretical noise-free chaotic time series, a noise added theoretical chaotic time series and two chaotic river flow time series are analyzed in this study. Three prediction horizons (1, 3 and 5 lead times) are considered. A limited number of parameter combinations were considered to select the best ANN models (MLPs) for prediction. This procedure was shown to be effective at least for the time series considered in this study. A remarkable prediction performance was gained with Global ANN models on noise-free chaotic Lorenz series. The overall results showed the superiority of global ANN models over the widely used local prediction models. © 2005 Elsevier B.V. All rights reserved.||Source Title:||Journal of Hydrology||URI:||http://scholarbank.nus.edu.sg/handle/10635/110846||ISSN:||00221694||DOI:||10.1016/j.jhydrol.2005.07.048|
|Appears in Collections:||Staff Publications|
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