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...

Πλήρης περιγραφή

Λεπτομέρειες βιβλιογραφικής εγγραφής
Κύριος συγγραφέας: Alexandridis, Antonis K.
Άλλοι συγγραφείς: Zapranis, Achilleas, 1965-
Μορφή: Ηλ. βιβλίο
Γλώσσα:English
Έκδοση: Hoboken, New Jersey : John Wiley & Sons, Inc., [2014]
Θέματα:
Διαθέσιμο Online:Full Text via HEAL-Link
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100 1 |a Alexandridis, Antonis K. 
245 1 0 |a Wavelet neural networks :  |b with applications in financial engineering, chaos, and classification /  |c Antonis K. Alexandridis, School of Mathematics, Statistics and Actuarial Science, University of Kent, Canterbury, UK, Achilleas D. Zapranis, Department of Accounting and Finance, University of Macedonia, Thessaloniki, Greece. 
264 1 |a Hoboken, New Jersey :  |b John Wiley & Sons, Inc.,  |c [2014] 
300 |a 1 online resource. 
336 |a text  |2 rdacontent 
337 |a computer  |2 rdamedia 
338 |a online resource  |2 rdacarrier 
504 |a Includes bibliographical references and index. 
505 0 |a Machine learning and financial engineering -- Neural networks -- Wavelet neural networks -- Model selection : selecting the architecture of the network -- Variable selection : determining the explanatory variables -- Model adequacy testing : determining the networks future performance -- Modeling the uncertainty: from point estimates to prediction intervals -- Modeling financial temperature derivatives -- Modeling financial wind derivatives -- Predicting chaotic time series -- Classification of breast cancer cases. 
588 |a Description based on print version record and CIP data provided by publisher. 
520 |a 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. 
650 0 |a Wavelets (Mathematics) 
650 0 |a Neural networks (Computer science) 
650 0 |a Financial engineering. 
650 7 |a COMPUTERS  |x General.  |2 bisacsh 
650 7 |a Financial engineering.  |2 fast  |0 (OCoLC)fst00924623 
650 7 |a Neural networks (Computer science)  |2 fast  |0 (OCoLC)fst01036260 
650 7 |a Wavelets (Mathematics)  |2 fast  |0 (OCoLC)fst01172896 
655 4 |a Electronic books. 
655 0 |a Electronic books. 
700 1 |a Zapranis, Achilleas,  |d 1965- 
776 0 8 |i Print version:  |a Alexandridis, Antonis K.  |t Wavelet neural networks  |d Hoboken, New Jersey : John Wiley & Sons, Inc., [2014]  |z 9781118592526  |w (DLC) 2013047838 
856 4 0 |u https://doi.org/10.1002/9781118596272  |z Full Text via HEAL-Link 
994 |a 92  |b DG1