@inproceedings{1ed08219d9334e2d821b4d325214df77,
title = "Application of support vector machines in paying rate forecasting",
abstract = "This paper deals with the application of a novel neural network technique, support vector machine (SVM), in financial time series forecasting. This study applies SVM to predict the paying rate index. The objective of this paper is to examine the feasibility of SVM in paying rate forecasting by comparing it with a feed-forward backpropagation (BP) neural network. We choose Gaussian function as its Kernel function.The experiment shows that SVM outperforms the feed-forward BP neural network based on the criteria of mean absolute error(MAE), mean absolute percent error(MAPE), mean squared error(MSE)and root mean square error(RMSE). Analysis of the experimental results proved that it is advantageous to apply SVMs to forecast paying rate.",
keywords = "BP neural network, Financial time series, Forecasting, Support vector machine",
author = "Chong Wu and Pu Chen",
year = "2006",
doi = "10.1109/ICMSE.2006.314265",
language = "英语",
isbn = "7560323553",
series = "Proceedings of 2006 International Conference on Management Science and Engineering, ICMSE'06 (13th)",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "1494--1497",
booktitle = "Proceedings of 2006 International Conference on Management Science and Engineering, ICMSE'06 (13th)",
address = "美国",
note = "2006 International Conference on Management Science and Engineering, ICMSE'06 ; Conference date: 05-10-2006 Through 07-10-2006",
}