Time series mining / Ahmed Abd Elzaher Toony Fares ; Supervised Osman Hegazy, Omar S. Soliman

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Ahmed Abdelzaher Toony Fares

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Material type: Text

TextLanguage: English Publication details: Cairo : Ahmed Abdelzaher Toony Fares , 2015Description: 93 Leaves : charts ; 30cmOther title:

التنقيب فى السلاسل الزمنية [Added title page title]

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Dissertation note: Thesis (Ph.D.) - Cairo University - Faculty of Computers and Information - Department of Information Systems Summary: The research shows the use of a hybrid stock-picking technique that integrates neural network, fuzzy logic and various quantum techniques to perform on the fluctuations and the movement of the correlations in stock price changes, to increase the efficiency of stock trading while using a suitable model that added advantage for a fund manager with many clients, each with a different portfolio and with variable probability for risk. Many classical soft computing approaches have successfully applied in the prediction of stock price and showed a good performance. This research investigates the power of Quantum Genetic Algorithm in a neuro-fuzzy system composed of an Adaptive Neuro Fuzzy Inference System (ANFIS) controller used in prediction of stock market, identified using an optimization technique based on a double chains quantum genetic algorithm

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Item type	Current library	Home library	Call number	Copy number	Status	Date due	Barcode
Thesis	قاعة الرسائل الجامعية - الدور الاول	المكتبة المركزبة الجديدة - جامعة القاهرة	Cai01.20.04.Ph.D.2015.Ah.T (Browse shelf(Opens below))		Not for loan		01010110067770000
CD - Rom	مخـــزن الرســائل الجـــامعية - البدروم	المكتبة المركزبة الجديدة - جامعة القاهرة	Cai01.20.04.Ph.D.2015.Ah.T (Browse shelf(Opens below))	67770.CD	Not for loan		01020110067770000

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Thesis (Ph.D.) - Cairo University - Faculty of Computers and Information - Department of Information Systems

The research shows the use of a hybrid stock-picking technique that integrates neural network, fuzzy logic and various quantum techniques to perform on the fluctuations and the movement of the correlations in stock price changes, to increase the efficiency of stock trading while using a suitable model that added advantage for a fund manager with many clients, each with a different portfolio and with variable probability for risk. Many classical soft computing approaches have successfully applied in the prediction of stock price and showed a good performance. This research investigates the power of Quantum Genetic Algorithm in a neuro-fuzzy system composed of an Adaptive Neuro Fuzzy Inference System (ANFIS) controller used in prediction of stock market, identified using an optimization technique based on a double chains quantum genetic algorithm

Issued also as CD

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