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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Wu Chong Chen Pu |
| Copyright Year | 2006 |
| Description | Author affiliation: Sch. of Manage., Harbin Inst. of Technol. (Wu Chong; Chen Pu) |
| 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 |
| Sponsorship | Nat. Natural Sci. Found. of China Harbin Inst. of Technol. P.R. China |
| Starting Page | 1494 |
| Ending Page | 1497 |
| File Size | 3785759 |
| Page Count | 4 |
| File Format | |
| ISBN | 7560323553 |
| DOI | 10.1109/ICMSE.2006.314265 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2006-10-05 |
| Publisher Place | France |
| Access Restriction | Subscribed |
| Rights Holder | HARBIN INSTITUTE OF TECHNOLOGY |
| Subject Keyword | Backpropagation Predictive models Financial management Feedforward neural networks Support vector machine Forecasting Financial time series Support vector machines Technology management BP neural network Neural networks Technology forecasting Economic forecasting Feedforward systems |
| Content Type | Text |
| Resource Type | Article |
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