Abstract
There are different methods which can be used for the support of forecasting. Nowadays the new theories of soft computing are used for these purposes. There could be mentioned fuzzy logic, neural networks and some other methods. The aim of the paper is focused on the use of fuzzy logic for forecasting purposes. The advantage of the use of fuzzy logic is in processing imprecision, uncertainty, vagueness, semi-truth, or approximated and nonlinear data. The applications on the stock market have specific features in comparison with others. The processes are focused on private corporate attempts at money making; therefore, the details of applications, successful or not, are not published very often. The fuzzy logic helps in decentralization of decision-making processes to be standardized, reproduced, and documented, that is an important factor in the business field. It was proved by the tests in the practice that the presented case studies had their justness to be used as a support for a decision making on the stock market.
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Dostál, P. (2013). Forecasting of Time Series with Fuzzy Logic. In: Zelinka, I., Chen, G., Rössler, O., Snasel, V., Abraham, A. (eds) Nostradamus 2013: Prediction, Modeling and Analysis of Complex Systems. Advances in Intelligent Systems and Computing, vol 210. Springer, Heidelberg. https://doi.org/10.1007/978-3-319-00542-3_16
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DOI: https://doi.org/10.1007/978-3-319-00542-3_16
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