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 …