Abstract
The aim of this contribution is to show that the combination of F-transform, fuzzy relations, neural networks and genetic algorithms can be successfully used in analysis and forecast of short time series encountered in financial analysis of a small enterprize. We propose to represent a time series trend by the direct F-transform components and to model it by one of three different models that are based on a linear autoregressive equation, neural network or fuzzy relation autoregressive equation. An optimal model of the trend will be chosen by a genetic algorithm. In comparison with other time series techniques the proposed one is simple and effective in computation and forecast.
In the application part, we present a description of a new software system that has been elaborated on the basis of the proposed theory. It includes analysis of time series and their tendencies in linguistic terms.
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Yarushkina, N., Perfilieva, I., Afanasieva, T., Igonin, A., Romanov, A., Shishkina, V. (2011). Time Series Processing and Forecasting Using Soft Computing Tools. In: Kuznetsov, S.O., Ślęzak, D., Hepting, D.H., Mirkin, B.G. (eds) Rough Sets, Fuzzy Sets, Data Mining and Granular Computing. RSFDGrC 2011. Lecture Notes in Computer Science(), vol 6743. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-21881-1_25
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DOI: https://doi.org/10.1007/978-3-642-21881-1_25
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