A New Intelligent System Methodology for Time Series Forecasting with Artificial Neural Networks
Neural Processing Letters
A hybrid method for tuning neural network for financial time series forecasting
MS '08 Proceedings of the 19th IASTED International Conference on Modelling and Simulation
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A wide range of applications can be identified for time series prediction, including energy systems planning, currency forecasting, or traffic prediction. Specifically, stock exchange operations can greatly benefit from efficient forecast techniques. Therefore, a number of different prediction approaches have been proposed such as linear models, Feedforward Neural network models, Recurrent Neural networks or Fuzzy Neural Models. In this paper one presents a prediction model based on fuzzy rules that relate past data values with the next unknown value to be estimated. A Fuzzy Boolean Neural Network has been used for this purpose, which has been applied to the Nasdaq index prediction. The results turned to be encouraging, namely on the percentage of correct up/down trend prediction.