A novel stock trading prediction and recommendation system

The prices of the stock are influence by many factors and emerge extremely nonlinear structure. Therefore, the stock trading prediction and recommendation is an extremely challenging task. In this paper, a novel stock trading prediction and recommendation system is proposed in user-friendly form. The recommendation system can inform the user whether is to buy or sell the stocks in the next step. Information granulation is applied to transform raw time series into meaningful and interpretable granules, and the more effective non-uniform partitioning method for prediction is presented. The system first determines the intervals based on information granules, and then define the fuzzy sets and fuzzify the historical data. Third, construct fuzzy relationships and assign weights to each period. Finally, the prediction and recommendation is implemented. The experimental results show the proposed system yields better prediction performance, and increases profit-making opportunities.