A reliability-based RBF network ensemble model for foreign exchange rates predication

Lean Yu; Wei Huang; Kin Keung Lai; Shouyang Wang

doi:10.1007/11893295_43

A reliability-based RBF network ensemble model for foreign exchange rates predication

Lean Yu
, Wei Huang
, Kin Keung Lai
, Shouyang Wang

Department of Decision Analytics and Operations

Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review

15 Citations (Scopus)

Abstract

In this study, a reliability-based RBF neural network ensemble forecasting model is proposed to overcome the shortcomings of the existing neural ensemble methods and ameliorate forecasting performance. In this model, the ensemble weights are determined by the reliability measure of RBF network output. For testing purposes, we compare the new ensemble model's performance with some existing network ensemble approaches in terms of three exchange rates series. Experimental results reveal that the prediction using the proposed approach is consistently better than those obtained using the other methods presented in this study in terms of the same measurements. © Springer-Verlag Berlin Heidelberg 2006.

Original language	English
Title of host publication	Neural Information Processing
Subtitle of host publication	13th International Conference, ICONIP 2006, Hong Kong, China, October 3-6, 2006, Proceedings, Part III
Editors	Irwin King, Jun Wang, Lai-Wan Chan
Place of Publication	Berlin, Heidelberg
Publisher	Springer
Pages	380-389
ISBN (Electronic)	978-3-540-46485-3
ISBN (Print)	9783540464846
DOIs	https://doi.org/10.1007/11893295_43
Publication status	Published - 2006
Event	13th International Conference on Neural Information Processing (ICONIP 2006) - Hong Kong, China Duration: 3 Oct 2006 → 6 Oct 2006

Publication series

Name	Lecture Notes in Computer Science
Volume	4234
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	13th International Conference on Neural Information Processing (ICONIP 2006)
Place	China
City	Hong Kong
Period	3/10/06 → 6/10/06

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10.1007/11893295_43

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Yu, L., Huang, W., Lai, K. K., & Wang, S. (2006). A reliability-based RBF network ensemble model for foreign exchange rates predication. In I. King, J. Wang, & L.-W. Chan (Eds.), Neural Information Processing: 13th International Conference, ICONIP 2006, Hong Kong, China, October 3-6, 2006, Proceedings, Part III (pp. 380-389). (Lecture Notes in Computer Science; Vol. 4234). Springer . https://doi.org/10.1007/11893295_43

Yu, Lean ; Huang, Wei ; Lai, Kin Keung et al. / A reliability-based RBF network ensemble model for foreign exchange rates predication. Neural Information Processing: 13th International Conference, ICONIP 2006, Hong Kong, China, October 3-6, 2006, Proceedings, Part III. editor / Irwin King ; Jun Wang ; Lai-Wan Chan. Berlin, Heidelberg : Springer , 2006. pp. 380-389 (Lecture Notes in Computer Science).

@inproceedings{1548d1d70ce74ddaab173aac4cc81bb2,

title = "A reliability-based RBF network ensemble model for foreign exchange rates predication",

abstract = "In this study, a reliability-based RBF neural network ensemble forecasting model is proposed to overcome the shortcomings of the existing neural ensemble methods and ameliorate forecasting performance. In this model, the ensemble weights are determined by the reliability measure of RBF network output. For testing purposes, we compare the new ensemble model's performance with some existing network ensemble approaches in terms of three exchange rates series. Experimental results reveal that the prediction using the proposed approach is consistently better than those obtained using the other methods presented in this study in terms of the same measurements. {\textcopyright} Springer-Verlag Berlin Heidelberg 2006.",

author = "Lean Yu and Wei Huang and Lai, \{Kin Keung\} and Shouyang Wang",

year = "2006",

doi = "10.1007/11893295\_43",

language = "English",

isbn = "9783540464846",

series = "Lecture Notes in Computer Science",

publisher = "Springer ",

pages = "380--389",

editor = "King, \{Irwin \} and Wang, \{Jun \} and Chan, \{Lai-Wan \}",

booktitle = "Neural Information Processing",

note = "13th International Conference on Neural Information Processing (ICONIP 2006) ; Conference date: 03-10-2006 Through 06-10-2006",

}

Yu, L, Huang, W, Lai, KK & Wang, S 2006, A reliability-based RBF network ensemble model for foreign exchange rates predication. in I King, J Wang & L-W Chan (eds), Neural Information Processing: 13th International Conference, ICONIP 2006, Hong Kong, China, October 3-6, 2006, Proceedings, Part III. Lecture Notes in Computer Science, vol. 4234, Springer , Berlin, Heidelberg, pp. 380-389, 13th International Conference on Neural Information Processing (ICONIP 2006), Hong Kong, China, 3/10/06. https://doi.org/10.1007/11893295_43

A reliability-based RBF network ensemble model for foreign exchange rates predication. / Yu, Lean; Huang, Wei; Lai, Kin Keung et al.
Neural Information Processing: 13th International Conference, ICONIP 2006, Hong Kong, China, October 3-6, 2006, Proceedings, Part III. ed. / Irwin King; Jun Wang; Lai-Wan Chan. Berlin, Heidelberg: Springer , 2006. p. 380-389 (Lecture Notes in Computer Science; Vol. 4234).

Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review

TY - GEN

T1 - A reliability-based RBF network ensemble model for foreign exchange rates predication

AU - Yu, Lean

AU - Huang, Wei

AU - Lai, Kin Keung

AU - Wang, Shouyang

PY - 2006

Y1 - 2006

N2 - In this study, a reliability-based RBF neural network ensemble forecasting model is proposed to overcome the shortcomings of the existing neural ensemble methods and ameliorate forecasting performance. In this model, the ensemble weights are determined by the reliability measure of RBF network output. For testing purposes, we compare the new ensemble model's performance with some existing network ensemble approaches in terms of three exchange rates series. Experimental results reveal that the prediction using the proposed approach is consistently better than those obtained using the other methods presented in this study in terms of the same measurements. © Springer-Verlag Berlin Heidelberg 2006.

AB - In this study, a reliability-based RBF neural network ensemble forecasting model is proposed to overcome the shortcomings of the existing neural ensemble methods and ameliorate forecasting performance. In this model, the ensemble weights are determined by the reliability measure of RBF network output. For testing purposes, we compare the new ensemble model's performance with some existing network ensemble approaches in terms of three exchange rates series. Experimental results reveal that the prediction using the proposed approach is consistently better than those obtained using the other methods presented in this study in terms of the same measurements. © Springer-Verlag Berlin Heidelberg 2006.

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UR - https://www.scopus.com/record/pubmetrics.uri?eid=2-s2.0-33750701425&origin=recordpage

U2 - 10.1007/11893295_43

DO - 10.1007/11893295_43

M3 - RGC 32 - Refereed conference paper (with host publication)

SN - 9783540464846

T3 - Lecture Notes in Computer Science

SP - 380

EP - 389

BT - Neural Information Processing

A2 - King, Irwin

A2 - Wang, Jun

A2 - Chan, Lai-Wan

PB - Springer

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T2 - 13th International Conference on Neural Information Processing (ICONIP 2006)

Y2 - 3 October 2006 through 6 October 2006

ER -

Yu L, Huang W, Lai KK, Wang S. A reliability-based RBF network ensemble model for foreign exchange rates predication. In King I, Wang J, Chan LW, editors, Neural Information Processing: 13th International Conference, ICONIP 2006, Hong Kong, China, October 3-6, 2006, Proceedings, Part III. Berlin, Heidelberg: Springer . 2006. p. 380-389. (Lecture Notes in Computer Science). Epub 2006 Oct 3. doi: 10.1007/11893295_43

A reliability-based RBF network ensemble model for foreign exchange rates predication

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