2000
DOI: 10.1016/s0925-2312(99)00142-3
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Use of neural networks for predictions using time series: Illustration with the El Niño Southern oscillation phenomenon

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Cited by 10 publications
(5 citation statements)
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“…Instead, ANN can learn the complex transport processes of a system from given observed data, serving as an instrument for universal data approximation. Accordingly, a considerable number of ANN applications for prediction, classification, signal processing and optimization can be found in various fields such as fmance and economics, meteorology, chemistry and chemical engineering, computer science, water resources engineering, and so forth (Maas et al 2000;Sahoo et al 2005;Valverde Ramirez et al 2005), exemplifying its great predictive potential of transport processes.…”
Section: List Of Tablesmentioning
confidence: 99%
“…Instead, ANN can learn the complex transport processes of a system from given observed data, serving as an instrument for universal data approximation. Accordingly, a considerable number of ANN applications for prediction, classification, signal processing and optimization can be found in various fields such as fmance and economics, meteorology, chemistry and chemical engineering, computer science, water resources engineering, and so forth (Maas et al 2000;Sahoo et al 2005;Valverde Ramirez et al 2005), exemplifying its great predictive potential of transport processes.…”
Section: List Of Tablesmentioning
confidence: 99%
“…Also, averaging forecasts from an ensemble of ANNs with different random weights assigned to the neurons at the start of the learning phase improved results with respect to using the results of a single ANN. Maas et al [53] further analyzed this fact and suggested using it to estimate prediction reliability. Tangang et al [54] simplified the ANN architecture by using extended EOFs (EEOFs), which project the observed fields (wind stress or SLP) onto spatio-temporal patterns, instead of on spatial ones (using EOFs).…”
Section: Early ML Approachesmentioning
confidence: 99%
“…-Maas et al [11] predict environmental parameters based on temporal series that correspond to the El Niño phenomenon.…”
Section: Neural Networkmentioning
confidence: 99%