Neural Networks
The nature of statistical learning theory
The nature of statistical learning theory
Self-organizing maps
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Predicting Time Series with Support Vector Machines
ICANN '97 Proceedings of the 7th International Conference on Artificial Neural Networks
Recursive self-organizing maps
Neural Networks - New developments in self-organizing maps
Regression neural network for error correction in foreign exchange forecasting and trading
Computers and Operations Research
Recurrent self-organising maps and local support vector machine models for exchange rate prediction
ISNN'06 Proceedings of the Third international conference on Advances in Neural Networks - Volume Part III
Multi-agent modeling of multiple FX-markets by neural networks
IEEE Transactions on Neural Networks
Modeling exchange rates: smooth transitions, neural networks, and linear models
IEEE Transactions on Neural Networks
Multiresolution forecasting for futures trading using wavelet decompositions
IEEE Transactions on Neural Networks
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Nowadays a great deal of effort has been made in order to gain advantages in foreign exchange (FX) rates predictions. However, most existing techniques seldom excel the simple random walk model in practical applications. This paper describes a self-organising network formed on the basis of a mixture of adaptive autoregressive models. The proposed network, termed self-organising mixture autoregressive (SOMAR) model, can be used to describe and model nonstationary, nonlinear time series by means of a number of underlying local regressive models. An autocorrelation coefficient-based measure is proposed as the similarity measure for assigning input samples to the underlying local models. Experiments on both benchmark time series and several FX rates have been conducted. The results show that the proposed method consistently outperforms other local time series modelling techniques on a range of performance measures including the mean-square-error, correct trend predication percentage, accumulated profit and model variance.