Intelligent Data Prediction System Using Data Mining and Neural Networks

Abstract

Stock market prediction is a significant area of financial forecasting; this is of great interest to stock investors, stock dealers, and applied researchers. Important issues in the development of a fully automated stock market prediction system are: attribute extraction from the metadata, attribute selection for highest forecast accuracy, and minimize the dimensionality of selected attribute set and the precision, and robustness of the prediction system. In this paper, we are using two methodologies; data mining and neural networks for achieving an intelligent data prediction system. Data mining is a specific step in this method, which involves the application of new learning algorithms for extracting the patterns (models) from the existing data. In the proposed system, accurate results were predicted by adjusting the advanced neural network parameters. Neural network extracts valuable information from an enormous dataset and data mining helps to presume future trends and behaviors. Therefore, a combination of both the methods could make the calculation much reliable.