Application of support vector machines in debt to GDP ratio forecasting

Wu Chong; Chen Pu

doi:10.1109/ICMLC.2006.258504

Application of support vector machines in debt to GDP ratio forecasting

Wu Chong^*
, Chen Pu

^*Corresponding author for this work

School of Management

School of Management, Harbin Institute of Technology

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

5 Scopus citations

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 debt to GDP ratio index. The objective of this paper is to examine the feasibility of SVM in foreign debt risk forecasting by comparing it with a back-propagation (BP) neural network. We choose Gaussian function as its Kernel function. The experiment shows that SVM outperforms the 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 debt to GDP ratio.

Original language	English
Title of host publication	Proceedings of the 2006 International Conference on Machine Learning and Cybernetics
Pages	3412-3415
Number of pages	4
DOIs	https://doi.org/10.1109/ICMLC.2006.258504
State	Published - 2006
Event	2006 International Conference on Machine Learning and Cybernetics - Dalian, China Duration: 13 Aug 2006 → 16 Aug 2006

Publication series

Name	Proceedings of the 2006 International Conference on Machine Learning and Cybernetics
Volume	2006

Conference

Conference	2006 International Conference on Machine Learning and Cybernetics
Country/Territory	China
City	Dalian
Period	13/08/06 → 16/08/06

Keywords

BP neural network
Financial time series
Forecasting
Support vector machine

Access to Document

10.1109/ICMLC.2006.258504

Cite this

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title = "Application of support vector machines in debt to GDP ratio 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 debt to GDP ratio index. The objective of this paper is to examine the feasibility of SVM in foreign debt risk forecasting by comparing it with a back-propagation (BP) neural network. We choose Gaussian function as its Kernel function. The experiment shows that SVM outperforms the 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 debt to GDP ratio.",

keywords = "BP neural network, Financial time series, Forecasting, Support vector machine",

author = "Wu Chong and Chen Pu",

year = "2006",

doi = "10.1109/ICMLC.2006.258504",

language = "英语",

isbn = "1424400619",

series = "Proceedings of the 2006 International Conference on Machine Learning and Cybernetics",

pages = "3412--3415",

booktitle = "Proceedings of the 2006 International Conference on Machine Learning and Cybernetics",

note = "2006 International Conference on Machine Learning and Cybernetics ; Conference date: 13-08-2006 Through 16-08-2006",

}

Chong, W & Pu, C 2006, Application of support vector machines in debt to GDP ratio forecasting. in Proceedings of the 2006 International Conference on Machine Learning and Cybernetics., 4028658, Proceedings of the 2006 International Conference on Machine Learning and Cybernetics, vol. 2006, pp. 3412-3415, 2006 International Conference on Machine Learning and Cybernetics, Dalian, China, 13/08/06. https://doi.org/10.1109/ICMLC.2006.258504

Application of support vector machines in debt to GDP ratio forecasting. / Chong, Wu; Pu, Chen.
Proceedings of the 2006 International Conference on Machine Learning and Cybernetics. 2006. p. 3412-3415 4028658 (Proceedings of the 2006 International Conference on Machine Learning and Cybernetics; Vol. 2006).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Application of support vector machines in debt to GDP ratio forecasting

AU - Chong, Wu

AU - Pu, Chen

PY - 2006

Y1 - 2006

N2 - 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 debt to GDP ratio index. The objective of this paper is to examine the feasibility of SVM in foreign debt risk forecasting by comparing it with a back-propagation (BP) neural network. We choose Gaussian function as its Kernel function. The experiment shows that SVM outperforms the 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 debt to GDP ratio.

AB - 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 debt to GDP ratio index. The objective of this paper is to examine the feasibility of SVM in foreign debt risk forecasting by comparing it with a back-propagation (BP) neural network. We choose Gaussian function as its Kernel function. The experiment shows that SVM outperforms the 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 debt to GDP ratio.

KW - BP neural network

KW - Financial time series

KW - Forecasting

KW - Support vector machine

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SN - 9781424400614

T3 - Proceedings of the 2006 International Conference on Machine Learning and Cybernetics

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BT - Proceedings of the 2006 International Conference on Machine Learning and Cybernetics

T2 - 2006 International Conference on Machine Learning and Cybernetics

Y2 - 13 August 2006 through 16 August 2006

ER -

Application of support vector machines in debt to GDP ratio forecasting

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Keywords

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