Knowledge Discovery in Dynamic Data Using Neural Networks

Kód výsledku v IS VaVaI

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Výsledek na webu

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DOI - Digital Object Identifier

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Jazyk výsledku

angličtina

Název v původním jazyce

Knowledge Discovery in Dynamic Data Using Neural Networks

Popis výsledku v původním jazyce

This article aims at knowledge discovery in dynamic data via classification based on neural networks. In our experimental study we have used three different types of neural networks based on Hebb, Adaline and backpropagation training rules. Our goal wasto discover important market (Forex) patterns which repeatedly appear in the market history. Developed classifiers based upon neural networks should effectively look for the key characteristics of the patterns in dynamic data. We focus on reliability ofrecognition made by the described algorithms with optimized training patterns based on the reduction of the calculation costs. To interpret the data from the analysis we created a basic trading system and trade all recommendations provided by the neuralnetwork.

Název v anglickém jazyce

Knowledge Discovery in Dynamic Data Using Neural Networks

Popis výsledku anglicky

This article aims at knowledge discovery in dynamic data via classification based on neural networks. In our experimental study we have used three different types of neural networks based on Hebb, Adaline and backpropagation training rules. Our goal wasto discover important market (Forex) patterns which repeatedly appear in the market history. Developed classifiers based upon neural networks should effectively look for the key characteristics of the patterns in dynamic data. We focus on reliability ofrecognition made by the described algorithms with optimized training patterns based on the reduction of the calculation costs. To interpret the data from the analysis we created a basic trading system and trade all recommendations provided by the neuralnetwork.

Druh

D - Stať ve sborníku

CEP obor

IN - Informatika

OECD FORD obor

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Projekt

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Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2015

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

Information Science and Applications, Lecture Notes in Electrical Engineering

ISBN

978-3-662-46577-6

ISSN

1876-1100

e-ISSN

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Počet stran výsledku

8

Strana od-do

575-582

Název nakladatele

Springer Verlag

Místo vydání

Berlin Heidelberg

Místo konání akce

Pattaya, Thailand

Datum konání akce

24. 2. 2015

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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