Recurrent self-organising maps and local support vector machine models for exchange rate prediction

He Ni; Hujun Yin

doi:10.1007/11760191_74

Recurrent self-organising maps and local support vector machine models for exchange rate prediction

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

Abstract

This paper considers the problem of predicting non-linear, non-stationary financial time sequence data, which is often difficult for traditional regressive models. The Self-Organising Map (SOM) is a vector quantisation method that represents statistical data sets in a topology preserving fashion. The method, which uses the Recurrent Self-Organising Map(RSOM) to partition the original data space into several disjointed regions and then uses Support Vector Machines (SVMs) to make the prediction as a regression method. It is model free and does not require a prior knowledge of the data. Experiments show that the method can make certain degree of profits and outperforms the GARCH method. © Springer-Verlag Berlin Heidelberg 2006.

Original language	English
Title of host publication	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)\|Lect. Notes Comput. Sci.
Place of Publication	Berlin
Publisher	Springer Nature
Pages	504-511
Number of pages	7
Volume	3973
ISBN (Print)	3540344829, 9783540344827
DOIs	https://doi.org/10.1007/11760191_74
Publication status	Published - 2006
Event	3rd International Symposium on Neural Networks, ISNN 2006 - Advances in Neural Networks - Chengdu Duration: 1 Jul 2006 → …

Conference

Conference	3rd International Symposium on Neural Networks, ISNN 2006 - Advances in Neural Networks
City	Chengdu
Period	1/07/06 → …

Access to Document

10.1007/11760191_74

Cite this

@inproceedings{93796239168d4d2da80ac6e2ef38b2f5,

title = "Recurrent self-organising maps and local support vector machine models for exchange rate prediction",

abstract = "This paper considers the problem of predicting non-linear, non-stationary financial time sequence data, which is often difficult for traditional regressive models. The Self-Organising Map (SOM) is a vector quantisation method that represents statistical data sets in a topology preserving fashion. The method, which uses the Recurrent Self-Organising Map(RSOM) to partition the original data space into several disjointed regions and then uses Support Vector Machines (SVMs) to make the prediction as a regression method. It is model free and does not require a prior knowledge of the data. Experiments show that the method can make certain degree of profits and outperforms the GARCH method. {\textcopyright} Springer-Verlag Berlin Heidelberg 2006.",

author = "He Ni and Hujun Yin",

year = "2006",

doi = "10.1007/11760191_74",

language = "English",

isbn = "3540344829",

volume = "3973",

pages = "504--511",

booktitle = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)|Lect. Notes Comput. Sci.",

publisher = "Springer Nature",

address = "United States",

note = "3rd International Symposium on Neural Networks, ISNN 2006 - Advances in Neural Networks ; Conference date: 01-07-2006",

}

Ni, H & Yin, H 2006, Recurrent self-organising maps and local support vector machine models for exchange rate prediction. in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)|Lect. Notes Comput. Sci.. vol. 3973, Springer Nature, Berlin, pp. 504-511, 3rd International Symposium on Neural Networks, ISNN 2006 - Advances in Neural Networks, Chengdu, 1/07/06. https://doi.org/10.1007/11760191_74

Recurrent self-organising maps and local support vector machine models for exchange rate prediction. / Ni, He; Yin, Hujun.
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)|Lect. Notes Comput. Sci.. Vol. 3973 Berlin: Springer Nature, 2006. p. 504-511.

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

TY - GEN

T1 - Recurrent self-organising maps and local support vector machine models for exchange rate prediction

AU - Ni, He

AU - Yin, Hujun

PY - 2006

Y1 - 2006

N2 - This paper considers the problem of predicting non-linear, non-stationary financial time sequence data, which is often difficult for traditional regressive models. The Self-Organising Map (SOM) is a vector quantisation method that represents statistical data sets in a topology preserving fashion. The method, which uses the Recurrent Self-Organising Map(RSOM) to partition the original data space into several disjointed regions and then uses Support Vector Machines (SVMs) to make the prediction as a regression method. It is model free and does not require a prior knowledge of the data. Experiments show that the method can make certain degree of profits and outperforms the GARCH method. © Springer-Verlag Berlin Heidelberg 2006.

AB - This paper considers the problem of predicting non-linear, non-stationary financial time sequence data, which is often difficult for traditional regressive models. The Self-Organising Map (SOM) is a vector quantisation method that represents statistical data sets in a topology preserving fashion. The method, which uses the Recurrent Self-Organising Map(RSOM) to partition the original data space into several disjointed regions and then uses Support Vector Machines (SVMs) to make the prediction as a regression method. It is model free and does not require a prior knowledge of the data. Experiments show that the method can make certain degree of profits and outperforms the GARCH method. © Springer-Verlag Berlin Heidelberg 2006.

U2 - 10.1007/11760191_74

DO - 10.1007/11760191_74

M3 - Conference contribution

SN - 3540344829

SN - 9783540344827

VL - 3973

SP - 504

EP - 511

BT - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)|Lect. Notes Comput. Sci.

PB - Springer Nature

CY - Berlin

T2 - 3rd International Symposium on Neural Networks, ISNN 2006 - Advances in Neural Networks

Y2 - 1 July 2006

ER -

Recurrent self-organising maps and local support vector machine models for exchange rate prediction

Abstract

Conference

Access to Document

Fingerprint

Cite this