Neural Classification Approach for Short Term Forecast of Exchange Rate Movement with Point and Figure Charts

Link:
Autor/in:
Verlag/Körperschaft:
IEEE
Erscheinungsjahr:
2008
Medientyp:
Text
Beschreibung:
  • In the domain of classification and forecasting tasks, artificial neural networks (ANNs) are prominent data mining methods. Neural network paradigms like learning vector quantization (LVQ) are suitable for solving classification problems. In this paper, we combine LVQ with the popular Point & Figure (P&F) chart analysis applied to a one day forecast of the exchange rate between Euro (EUR) and US Dollar (USD). We present two different P&F encoding schemes and analyze the classification accuracy and results of a trading system fed with our results from the LVQ.
  • In the domain of classification and forecasting tasks, artificial neural networks (ANNs) are prominent data mining methods. Neural network paradigms like learning vector quantization (LVQ) are suitable for solving classification problems. In this paper, we combine LVQ with the popular Point & Figure (P&F) chart analysis applied to a one day forecast of the exchange rate between Euro (EUR) and US Dollar (USD). We present two different P&F encoding schemes and analyze the classification accuracy and results of a trading system fed with our results from the LVQ.
Lizenz:
  • info:eu-repo/semantics/closedAccess
Quellsystem:
Forschungsinformationssystem der UHH

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