Forecasting High Frequency Financial Data: Statistical and Fuzzy Logic RBF ANN Approach

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F47813059%3A19240%2F12%3A%230004393" target="_blank" >RIV/47813059:19240/12:#0004393 - isvavai.cz</a>

Výsledek na webu

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DOI - Digital Object Identifier

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Jazyk výsledku

angličtina

Název v původním jazyce

Forecasting High Frequency Financial Data: Statistical and Fuzzy Logic RBF ANN Approach

Popis výsledku v původním jazyce

Forecast accuracy of economic and financial processes is a popular measure for quantifying the risk in multi-criteria decision making. In this paper, we develop forecasting models based on the statistical (stochastic) methods, sometimes called as hard computing, and on the soft methods using granular computing. To illustrate the forecasting performance of these approaches the learning aspects of RBF networks are presented and an application is included. We show a new approach of function estimation fornonlinear time series model by means of a granular fuzzy logic neural network based on Gaussian activation function with cloud concept. In a comparative study is shown that the presented approach is able to model and predict the high frequency data withreasonable accuracy and more efficient than the statistical methods.

Název v anglickém jazyce

Forecasting High Frequency Financial Data: Statistical and Fuzzy Logic RBF ANN Approach

Popis výsledku anglicky

Forecast accuracy of economic and financial processes is a popular measure for quantifying the risk in multi-criteria decision making. In this paper, we develop forecasting models based on the statistical (stochastic) methods, sometimes called as hard computing, and on the soft methods using granular computing. To illustrate the forecasting performance of these approaches the learning aspects of RBF networks are presented and an application is included. We show a new approach of function estimation fornonlinear time series model by means of a granular fuzzy logic neural network based on Gaussian activation function with cloud concept. In a comparative study is shown that the presented approach is able to model and predict the high frequency data withreasonable accuracy and more efficient than the statistical methods.

Druh

D - Stať ve sborníku

CEP obor

IN - Informatika

OECD FORD obor

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Projekt

<a href="/cs/project/ED1.1.00%2F02.0070" target="_blank" >ED1.1.00/02.0070: Centrum excelence IT4Innovations</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2012

Kód důvěrnosti údajů

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

Název statě ve sborníku

Proceedings of the 10th International FLINS Conference

ISBN

978-981-4417-73-0

ISSN

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e-ISSN

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Počet stran výsledku

6

Strana od-do

827-832

Název nakladatele

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Místo vydání

Paříž

Místo konání akce

Istambul

Datum konání akce

1. 1. 2012

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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