Abstract
In this paper, we propose the combination of wavelet transform, neural networks and fuzzy logic for achieving a short-term prediction and decision support on financial time-series data. Typical example of this data are the daily rates of price change in equities of the Stock Exchange Market, in which our system may work as a useful advising tool for trade and investment decisions. The basic concept behind this procedure is the attempt to use these short-term rates of price change for constructing a short-term policy, using an approach that simulates human behavior. To enhance the learning capabilities of the neural network we reduce the noise of this signal using wavelet thresholding. Then we use the neural network prediction output in order to feed a fuzzy system. However, selecting arbirtrarily the exact shapes and ranges of the antecedent sets can be critical for the efficiency of such system. Therefore, the fuzzy system is trained using genetic and neural procedures. The goal of this training is to achieve a linguistic and a defuzzificated output, which would be similar to the output of a fuzzy system, if it had been applied to real-world forward rates of change, using a common sense rule base.
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Tsakonas, A., Dounias, G., Tselentis, G. (2002). Forecast of Short Term Trends in Stock Exchange using Fuzzy Rules and Neural Networks on Multiresolution Processed Signals. In: Zimmermann, HJ., Tselentis, G., van Someren, M., Dounias, G. (eds) Advances in Computational Intelligence and Learning. International Series in Intelligent Technologies, vol 18. Springer, Dordrecht. https://doi.org/10.1007/978-94-010-0324-7_18
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DOI: https://doi.org/10.1007/978-94-010-0324-7_18
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