A fractal structure of time series and prediction (a comparative study to neural network)

Kód výsledku v IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61988987%3A17310%2F10%3AA1100YDC" target="_blank" >RIV/61988987:17310/10:A1100YDC - isvavai.cz</a>
Výsledek na webu
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Jazyk výsledku
angličtina
Název v původním jazyce
A fractal structure of time series and prediction (a comparative study to neural network)
Popis výsledku v původním jazyce
We develop two models for analysis and forecasting of financial time series. The first model is based on Elliott waves, which disposes of fractal structure. The second one uses an artificial neural network that is adapted by backpropagation. The Elliottwave principle is a detailed description of how financial markets behave. Artificial neural networks are suitable for predicting time series because are able to generalize and are resistant to noise. This paper also includes experimental results of timeseries prediction carried out with both mentioned models.
Název v anglickém jazyce
A fractal structure of time series and prediction (a comparative study to neural network)
Popis výsledku anglicky
We develop two models for analysis and forecasting of financial time series. The first model is based on Elliott waves, which disposes of fractal structure. The second one uses an artificial neural network that is adapted by backpropagation. The Elliottwave principle is a detailed description of how financial markets behave. Artificial neural networks are suitable for predicting time series because are able to generalize and are resistant to noise. This paper also includes experimental results of timeseries prediction carried out with both mentioned models.

Rok uplatnění
2010
Kód důvěrnosti údajů
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

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