Time Series Analysis and Data Prediction: An ECM Neuronal Approach Applied to EUR/USD Currency

Abstract:

Several approaches to dynamic modeling in economic such as ARIMA, GARCH, neural nets and error corrected models have become popular in recent years. We evaluate statistical and neuronal methods for daily EUR/USD currency prediction using daily EUR/USD time series data. Both techniques are reviewed and contrasted from the accuracy of forecasting models point of view. We show that an RBF neural network can achieve better prediction results than the latest statistical methodologies. Following fruitful applications of neural networks to predict financial data this work goes ahead by using neural networks for modeling any non-linearities within the estimated statistical models.

• Periodical: Advanced Materials Research (Volume 918)
• Pages: 301-306
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.918.301
• Online since: April 2014
• Authors: Dusan Marcek*
• Keywords: ARIMA Model, Error Correction Models, Forecast Accuracy, Machine Learning, RBF Neural Network, Time Series Analysis

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