A NOVEL FUZZY TIME-SERIES BASED FORECASTING OF STOCK PRICE

Forecasting has an important utility with respect to stock prices. It is the case with all listed companies that facilitates buyers to stocks to take probably well informed decisions. The predication of stock prices is an important research area which has many benefits. There were many techniques in the literature to solve the problem of prediction. Fuzzy time series is one of them that are utilized frequently for making accurate predictions of stock prices. In this paper we improve the prediction performance of fuzzy time-series models a proposing a novel approach which provides better performance. The algorithm we implemented is known as fast Fourier transform algorithm. We built a prototype application to demonstrate the efficiency of our algorithm. The empirical results revealed that our approach is useful in real world applications where forecasting stock prices is essential in order to make investment decisions.

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