Artificial Neural Networks Numerical Forecasting of Economic Time Series

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F62156489%3A43110%2F11%3A00170460" target="_blank" >RIV/62156489:43110/11:00170460 - 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

Artificial Neural Networks Numerical Forecasting of Economic Time Series

Popis výsledku v původním jazyce

Current global market is driven by many factors such as the information age, the time and amount of information distributed by many data channels. It is practically impossible to analyze all kinds of incoming information flows and transform them to datawith classical methods. New requirements call for using other methods. Artificial neural networks once trained on patterns can be used for forecasting and they are able to work with extremely big data sets in reasonable time. Traditionally this task is solved by using statistical analysis - first a time-series model is constructed and then statistical prediction algorithms are applied to it in order to obtain future values. The common point for both methods is the learning process from samples of past data or learning from the past. From many of the uncommon points the input conditions for the model creation and length of the time series pattern set could be pointed. On one hand very sophisticated statistical methods exist that have str

Název v anglickém jazyce

Artificial Neural Networks Numerical Forecasting of Economic Time Series

Popis výsledku anglicky

Current global market is driven by many factors such as the information age, the time and amount of information distributed by many data channels. It is practically impossible to analyze all kinds of incoming information flows and transform them to datawith classical methods. New requirements call for using other methods. Artificial neural networks once trained on patterns can be used for forecasting and they are able to work with extremely big data sets in reasonable time. Traditionally this task is solved by using statistical analysis - first a time-series model is constructed and then statistical prediction algorithms are applied to it in order to obtain future values. The common point for both methods is the learning process from samples of past data or learning from the past. From many of the uncommon points the input conditions for the model creation and length of the time series pattern set could be pointed. On one hand very sophisticated statistical methods exist that have str

Druh

C - Kapitola v odborné knize

CEP obor

IN - Informatika

OECD FORD obor

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Projekt

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Návaznosti

Z - Vyzkumny zamer (s odkazem do CEZ)

Rok uplatnění

2011

Kód důvěrnosti údajů

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

Název knihy nebo sborníku

Artificial Neural Networks - Application

ISBN

978-953-307-188-6

Počet stran výsledku

16

Strana od-do

13-28

Počet stran knihy

586

Název nakladatele

InTech

Místo vydání

Riejka, Croatia

Kód UT WoS kapitoly

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