A comparison between neural networks and chaotic models for exchange rate prediction.

DOI10.1016/S0167-9473(98)00067-XzbMath1042.91526MaRDI QIDQ1285487

Rosa A. Schiavo, Francesco Lisi

Publication date: 28 April 1999

Published in: Computational Statistics and Data Analysis (Search for Journal in Brave)

zbMATH Keywords

Exchange ratesArtificial neural networksBack-propagationChaotic modelsTime series prediction

Mathematics Subject Classification ID

Related Items (6)

Combining seasonal ARIMA models with computational intelligence techniques for time series forecasting ⋮ Testing forecast accuracy of foreign exchange rates: Predictions from feed forward and various recurrent neural network architectures ⋮ Comparison of the performance of multi-layer perceptron and linear regression for epidemiological data ⋮ Modeling exchange rates using wavelet decomposed genetic neural networks ⋮ Recurrent support vector regression for a non-linear ARMA model with applications to forecasting financial returns ⋮ EXPLORING THE IMPACT OF CALENDAR EFFECTS ON THE DYNAMIC STRUCTURE AND FORECASTS OF FINANCIAL TIME SERIES

Cites Work

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• Nonlinear prediction of chaotic time series
• Embedology
• Neural networks: A review from a statistical perspective. With comments and a rejoinder by the authors
• Multilayer feedforward networks are universal approximators
• NONLINEAR TIME SEQUENCE ANALYSIS
• Neural Networks and Related 'Massively Parallel' Methods for Statistics: A Short Overview
• Portfolio Analysis in a Stable Paretian Market
• Approximation by superpositions of a sigmoidal function

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