ANN-Based Mid-Term Stock Forecasting
Jun Zhang · Computer Engineering and Science · 2006
This paper presents a method based on the BP neural network for stock market modeling, forecasting and decision-making. In NN- based stock forecasting, the complexity of input data has a negative effect on network training efficiency and forecasting precision. Focusing on solving this problem, a fuzzy curve method is developed to filter variables. This method is developed to eliminate those inputs, which are dependent on other important inputs. The process is fast and can be effectively used on the systems with a large number of inputs and data points. The results show that after data processing, the training time is reduced and the forecasting precision is enhanced.