Title :
Support vector machine for regression and applications to financial forecasting
Author :
Trafalis, Theodore B. ; Ince, Huseyin
Author_Institution :
Sch. of Ind. Eng., Oklahoma Univ., Norman, OK, USA
Abstract :
The main purpose of the paper is to compare the support vector machine (SVM) developed by Cortes and Vapnik (1995) with other techniques such as backpropagation and radial basis function (RBF) networks for financial forecasting applications. The theory of the SVM algorithm is based on statistical learning theory. Training of SVMs leads to a quadratic programming (QP) problem. Preliminary computational results for stock price prediction are also presented
Keywords :
backpropagation; finance; forecasting theory; function approximation; quadratic programming; radial basis function networks; financial forecasting; regression; statistical learning theory; stock price prediction; support vector machine; Backpropagation algorithms; Industrial engineering; Machine learning; Machine learning algorithms; Pattern classification; Quadratic programming; Statistical learning; Support vector machine classification; Support vector machines; Virtual colonoscopy;
Conference_Titel :
Neural Networks, 2000. IJCNN 2000, Proceedings of the IEEE-INNS-ENNS International Joint Conference on
Conference_Location :
Como
Print_ISBN :
0-7695-0619-4
DOI :
10.1109/IJCNN.2000.859420
