Hybridizing exponential smoothing and neural network for financial time series predication

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

doi:10.1007/11758549_69

Hybridizing exponential smoothing and neural network for financial time series predication

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

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

62 Citations (Scopus)

Abstract

In this study, a hybrid synergy model integrating exponential smoothing and neural network is proposed for financial time series prediction. The proposed model attempts to incorporate the linear characteristics of an exponential smoothing model and nonlinear patterns of neural network to create a "synergetic" model via the linear programming technique. For verification, two real-world financial time series are used for testing purpose. © Springer-Verlag Berlin Heidelberg 2006.

Original language	English
Title of host publication	Computational Science - ICCS 2006
Subtitle of host publication	6th International Conference, Proceedings
Publisher	Springer Verlag
Pages	493-500
Volume	3994 LNCS - IV
ISBN (Print)	3540343857, 9783540343851
DOIs	https://doi.org/10.1007/11758549_69
Publication status	Published - 2006
Event	ICCS 2006: 6th International Conference on Computational Science - Reading, United Kingdom Duration: 28 May 2006 → 31 May 2006

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	3994 LNCS - IV
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	ICCS 2006: 6th International Conference on Computational Science
Place	United Kingdom
City	Reading
Period	28/05/06 → 31/05/06

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Lai, K. K., Yu, L., Wang, S., & Huang, W. (2006). Hybridizing exponential smoothing and neural network for financial time series predication. In Computational Science - ICCS 2006: 6th International Conference, Proceedings (Vol. 3994 LNCS - IV, pp. 493-500). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 3994 LNCS - IV). Springer Verlag. https://doi.org/10.1007/11758549_69

Lai, Kin Keung ; Yu, Lean ; Wang, Shouyang et al. / Hybridizing exponential smoothing and neural network for financial time series predication. Computational Science - ICCS 2006: 6th International Conference, Proceedings. Vol. 3994 LNCS - IV Springer Verlag, 2006. pp. 493-500 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

@inproceedings{06e4dbe58d264382ba3d72d696f62001,

title = "Hybridizing exponential smoothing and neural network for financial time series predication",

abstract = "In this study, a hybrid synergy model integrating exponential smoothing and neural network is proposed for financial time series prediction. The proposed model attempts to incorporate the linear characteristics of an exponential smoothing model and nonlinear patterns of neural network to create a {"}synergetic{"} model via the linear programming technique. For verification, two real-world financial time series are used for testing purpose. {\textcopyright} Springer-Verlag Berlin Heidelberg 2006.",

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

year = "2006",

doi = "10.1007/11758549_69",

language = "English",

isbn = "3540343857",

volume = "3994 LNCS - IV",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

publisher = "Springer Verlag",

pages = "493--500",

booktitle = "Computational Science - ICCS 2006",

address = "Germany",

note = "ICCS 2006: 6th International Conference on Computational Science ; Conference date: 28-05-2006 Through 31-05-2006",

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Lai, KK, Yu, L, Wang, S & Huang, W 2006, Hybridizing exponential smoothing and neural network for financial time series predication. in Computational Science - ICCS 2006: 6th International Conference, Proceedings. vol. 3994 LNCS - IV, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 3994 LNCS - IV, Springer Verlag, pp. 493-500, ICCS 2006: 6th International Conference on Computational Science, Reading, United Kingdom, 28/05/06. https://doi.org/10.1007/11758549_69

Hybridizing exponential smoothing and neural network for financial time series predication. / Lai, Kin Keung; Yu, Lean; Wang, Shouyang et al.
Computational Science - ICCS 2006: 6th International Conference, Proceedings. Vol. 3994 LNCS - IV Springer Verlag, 2006. p. 493-500 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 3994 LNCS - IV).

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

TY - GEN

T1 - Hybridizing exponential smoothing and neural network for financial time series predication

AU - Lai, Kin Keung

AU - Yu, Lean

AU - Wang, Shouyang

AU - Huang, Wei

PY - 2006

Y1 - 2006

N2 - In this study, a hybrid synergy model integrating exponential smoothing and neural network is proposed for financial time series prediction. The proposed model attempts to incorporate the linear characteristics of an exponential smoothing model and nonlinear patterns of neural network to create a "synergetic" model via the linear programming technique. For verification, two real-world financial time series are used for testing purpose. © Springer-Verlag Berlin Heidelberg 2006.

AB - In this study, a hybrid synergy model integrating exponential smoothing and neural network is proposed for financial time series prediction. The proposed model attempts to incorporate the linear characteristics of an exponential smoothing model and nonlinear patterns of neural network to create a "synergetic" model via the linear programming technique. For verification, two real-world financial time series are used for testing purpose. © Springer-Verlag Berlin Heidelberg 2006.

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U2 - 10.1007/11758549_69

DO - 10.1007/11758549_69

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

SN - 3540343857

SN - 9783540343851

VL - 3994 LNCS - IV

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 493

EP - 500

BT - Computational Science - ICCS 2006

PB - Springer Verlag

T2 - ICCS 2006: 6th International Conference on Computational Science

Y2 - 28 May 2006 through 31 May 2006

ER -

Lai KK, Yu L, Wang S, Huang W. Hybridizing exponential smoothing and neural network for financial time series predication. In Computational Science - ICCS 2006: 6th International Conference, Proceedings. Vol. 3994 LNCS - IV. Springer Verlag. 2006. p. 493-500. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/11758549_69

Hybridizing exponential smoothing and neural network for financial time series predication

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