A big data framework for stock price forecasting using fuzzy time series

Stock price forecasting is the most difficult field owing to irregularities. Therefore, the stock price forecasting and recommendation is an extremely challenging task. In this paper, a big data framework for stock price forecasting using fuzzy time series is proposed in user-friendly form. The method fully capitalizes on the two key technologies, fuzzy set theory and classical time series forecasting methods, to deal with the stock price forecasting. First, using the fuzzy time series the method predicts the fuzzy trend of the forecasted data based on historical stock big data. Then, an autoregressive model is utilized to determine the fluctuation quantity of the forecasted data. Finally, the forecasted stock price is obtained by integrating trend prediction with fluctuation quantity. TAIEX are employed to illustrate the proposed forecasting framework and to compare the forecasting accuracy between the proposed forecasting framework and the existing methods. The experimental results indicate that the proposed forecasting framework produces better forecasting performance.