The nature of statistical learning theory
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Machine Learning
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
Feedforward Neural Network Methodology
Feedforward Neural Network Methodology
Machine Learning
Artificial Neural Networks: Learning Algorithms, Performance Evaluation, and Applications
Artificial Neural Networks: Learning Algorithms, Performance Evaluation, and Applications
SVM Based Models for Predicting Foreign Currency Exchange Rates
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
A novel nonlinear ensemble forecasting model incorporating GLAR and ANN for foreign exchange rates
Computers and Operations Research
Random Forests for land cover classification
Pattern Recognition Letters - Special issue: Pattern recognition in remote sensing (PRRS 2004)
A hybrid model for exchange rate prediction
Decision Support Systems
Statistical fuzzy interval neural networks for currency exchange rate time series prediction
Applied Soft Computing
Testing the random walk hypothesis through robust estimation of correlation
Computational Statistics & Data Analysis
Exchange Rates Forecasting with Least Squares Support Vector Machine
CSSE '08 Proceedings of the 2008 International Conference on Computer Science and Software Engineering - Volume 05
Optimal artificial neural network topology for foreign exchange forecasting
Proceedings of the 46th Annual Southeast Regional Conference on XX
Age regression from faces using random forests
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
Ensemble Methods in Data Mining: Improving Accuracy Through Combining Predictions
Ensemble Methods in Data Mining: Improving Accuracy Through Combining Predictions
Chaos-based support vector regressions for exchange rate forecasting
Expert Systems with Applications: An International Journal
Modern Applied Statistics with S
Modern Applied Statistics with S
Neural fuzzy forecasting of the china yuan to US dollar exchange rate: a swarm intelligence approach
ICSI'11 Proceedings of the Second international conference on Advances in swarm intelligence - Volume Part I
Forecasting time series with genetic fuzzy predictor ensemble
IEEE Transactions on Fuzzy Systems
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In the recent era, computational intelligence techniques have found an increased popularity in addressing varied financial issues, including foreign exchange rate prediction. This article, through an intelligent system research framework, relates the Australian dollar (AUD)/US dollar (USD) exchange rate to the Australian and the US stock market indices. Information for exchange rate, All Ordinaries Index (AOI) and Dow Jones Industrial Average (DJI) for the trading days over the period January 1991–May 2011 is considered in this research. Utilizing a set of statistical and computational intelligence techniques, the research establishes that the AUD/USD exchange rate is best estimated by a linear forecast model compared with the nonlinear and ensemble-based intelligent system models. This research further highlights that, among the competing linear models, the model with both the stock market indices and historical exchange rate values as the predictors is the best forecaster. Parameters of the linear model are deduced through a Monte Carlo stochastic approach. Relative importance of the predictors is also studied, and the influence of historical exchange rates, the immediate impact of AOI and the lagged effect of DJI are noted. Copyright © 2012 John Wiley & Sons, Ltd.