A neural sequential associator using feedback multilayer neural networks is proposed to predict long-term time series data. The neural network analyzes the inherent structure in the sequence and predicts the future sequence based on these structures. Feedback multilayer neural networks are used in duplicate and the inputs to such models are functions of time to represent time correlations of temporal data in the synaptic weights during learning. It is shown that the method gives better performance than neural networks without feedback when applied to the prediction of long-term stock prices.
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