Predicting Stock Trend Using Fourier Transform and Support Vector Regression

Predicting stock price is an important task as well as difficult problem. Stock price prediction depends on various factors and their complex relationships, which is the act of trying to determine the future value of a company stock. The successful prediction of a stock future price could yield significant profit. This paper demonstrates the applicability of a framework that combines support vector regression and Fourier transform, for predicting the stock price by learning the historic data. Fourier transform is used for noise filtering, and the support vector regression is for model training. Our results suggest that the proposed framework is a powerful predictive tool for stock predictions in the financial market.