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Wavelet neural networks: with applications in financial engineering, chaos, and classification

Through extensive examples and case studies, Wavelet Neural Networks provides a step-by-step introduction to modeling, training, and forecasting using wavelet networks. The acclaimed authors present a statistical model identification framework to successfully apply wavelet networks in various applic...

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Detalles Bibliográficos
Autores principales: Alexandridis, Antonios K, Zapranis, Achilleas D
Lenguaje:eng
Publicado: Wiley 2014
Materias:
Acceso en línea:http://cds.cern.ch/record/1701573
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author Alexandridis, Antonios K
Zapranis, Achilleas D
author_facet Alexandridis, Antonios K
Zapranis, Achilleas D
author_sort Alexandridis, Antonios K
collection CERN
description Through extensive examples and case studies, Wavelet Neural Networks provides a step-by-step introduction to modeling, training, and forecasting using wavelet networks. The acclaimed authors present a statistical model identification framework to successfully apply wavelet networks in various applications, specifically, providing the mathematical and statistical framework needed for model selection, variable selection, wavelet network construction, initialization, training, forecasting and prediction, confidence intervals, prediction intervals, and model adequacy testing. The text is ideal for
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institution Organización Europea para la Investigación Nuclear
language eng
publishDate 2014
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spelling cern-17015732021-04-21T21:02:12Zhttp://cds.cern.ch/record/1701573engAlexandridis, Antonios KZapranis, Achilleas DWavelet neural networks: with applications in financial engineering, chaos, and classificationComputing and ComputersThrough extensive examples and case studies, Wavelet Neural Networks provides a step-by-step introduction to modeling, training, and forecasting using wavelet networks. The acclaimed authors present a statistical model identification framework to successfully apply wavelet networks in various applications, specifically, providing the mathematical and statistical framework needed for model selection, variable selection, wavelet network construction, initialization, training, forecasting and prediction, confidence intervals, prediction intervals, and model adequacy testing. The text is ideal forWileyoai:cds.cern.ch:17015732014
spellingShingle Computing and Computers
Alexandridis, Antonios K
Zapranis, Achilleas D
Wavelet neural networks: with applications in financial engineering, chaos, and classification
title Wavelet neural networks: with applications in financial engineering, chaos, and classification
title_full Wavelet neural networks: with applications in financial engineering, chaos, and classification
title_fullStr Wavelet neural networks: with applications in financial engineering, chaos, and classification
title_full_unstemmed Wavelet neural networks: with applications in financial engineering, chaos, and classification
title_short Wavelet neural networks: with applications in financial engineering, chaos, and classification
title_sort wavelet neural networks: with applications in financial engineering, chaos, and classification
topic Computing and Computers
url http://cds.cern.ch/record/1701573
work_keys_str_mv AT alexandridisantoniosk waveletneuralnetworkswithapplicationsinfinancialengineeringchaosandclassification
AT zapranisachilleasd waveletneuralnetworkswithapplicationsinfinancialengineeringchaosandclassification