Financial data forecasting using optimized echo state network

Junxiu Liu; Tiening Sun; Yuling Luo; Qiang Fu; Yi Cao; Jia Zhai; Xuemei Ding

doi:10.1007/978-3-030-04221-9_13

Financial data forecasting using optimized echo state network

Junxiu Liu
, Tiening Sun
, Yuling Luo^*
, Qiang Fu
, Yi Cao
, Jia Zhai
, Xuemei Ding

^*Corresponding author for this work

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

Abstract

The echo state network (ESN) is a dynamic neural network, which simplifies the training process in the conventional neural network. Due to its powerful non-linear computing ability, it has been applied to predict the time series. However, the parameters of the ESN need to be set experimentally, which can lead to instable performance and there is space to further improve its performance. In order to address this challenge, an improved fruit fly optimization algorithm (IFOA) is proposed in this work to optimize four key parameters of the ESN. Compared to the original fruit fly optimization algorithm (FOA), the proposed IFOA improves the optimization efficiency, where two novel particles are proposed in the fruit flies swarm, and the search process of the swarm is transformed from two-dimensional to three-dimensional space. The proposed approach is applied to financial data sets. Experimental results show that the proposed FOA-ESN and IFOA-ESN models are more effective (~50% improvement) than others, and the IFOA-ESN can obtain the best prediction accuracy.

Original language	English
Title of host publication	Neural Information Processing - 25th International Conference, ICONIP 2018, Proceedings
Editors	Andrew Chi Sing Leung, Long Cheng, Seiichi Ozawa
Publisher	Springer
Pages	138-149
Number of pages	12
ISBN (Print)	9783030042202
DOIs	https://doi.org/10.1007/978-3-030-04221-9_13
Publication status	Published - 2018
Event	25th International Conference on Neural Information Processing, ICONIP 2018 - Siem Reap, Cambodia Duration: 13 Dec 2018 → 16 Dec 2018

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	11305 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	25th International Conference on Neural Information Processing, ICONIP 2018
Country/Territory	Cambodia
City	Siem Reap
Period	13/12/18 → 16/12/18

Keywords / Materials (for Non-textual outputs)

algorithm optimization
echo state network
fruit fly algorithm
time series

Access to Document

10.1007/978-3-030-04221-9_13

Cite this

Liu, J., Sun, T., Luo, Y., Fu, Q., Cao, Y., Zhai, J., & Ding, X. (2018). Financial data forecasting using optimized echo state network. In A. C. S. Leung, L. Cheng, & S. Ozawa (Eds.), Neural Information Processing - 25th International Conference, ICONIP 2018, Proceedings (pp. 138-149). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11305 LNCS). Springer. https://doi.org/10.1007/978-3-030-04221-9_13

Liu, Junxiu ; Sun, Tiening ; Luo, Yuling et al. / Financial data forecasting using optimized echo state network. Neural Information Processing - 25th International Conference, ICONIP 2018, Proceedings. editor / Andrew Chi Sing Leung ; Long Cheng ; Seiichi Ozawa. Springer, 2018. pp. 138-149 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

@inproceedings{cd0ae08368784d458413e0ebd251de22,

title = "Financial data forecasting using optimized echo state network",

abstract = "The echo state network (ESN) is a dynamic neural network, which simplifies the training process in the conventional neural network. Due to its powerful non-linear computing ability, it has been applied to predict the time series. However, the parameters of the ESN need to be set experimentally, which can lead to instable performance and there is space to further improve its performance. In order to address this challenge, an improved fruit fly optimization algorithm (IFOA) is proposed in this work to optimize four key parameters of the ESN. Compared to the original fruit fly optimization algorithm (FOA), the proposed IFOA improves the optimization efficiency, where two novel particles are proposed in the fruit flies swarm, and the search process of the swarm is transformed from two-dimensional to three-dimensional space. The proposed approach is applied to financial data sets. Experimental results show that the proposed FOA-ESN and IFOA-ESN models are more effective (\textasciitilde{}50\% improvement) than others, and the IFOA-ESN can obtain the best prediction accuracy.",

keywords = "algorithm optimization, echo state network, fruit fly algorithm, time series",

author = "Junxiu Liu and Tiening Sun and Yuling Luo and Qiang Fu and Yi Cao and Jia Zhai and Xuemei Ding",

year = "2018",

doi = "10.1007/978-3-030-04221-9\_13",

language = "English",

isbn = "9783030042202",

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

publisher = "Springer",

pages = "138--149",

editor = "Leung, \{Andrew Chi Sing\} and Long Cheng and Seiichi Ozawa",

booktitle = "Neural Information Processing - 25th International Conference, ICONIP 2018, Proceedings",

address = "United Kingdom",

note = "25th International Conference on Neural Information Processing, ICONIP 2018 ; Conference date: 13-12-2018 Through 16-12-2018",

}

Liu, J, Sun, T, Luo, Y, Fu, Q, Cao, Y, Zhai, J & Ding, X 2018, Financial data forecasting using optimized echo state network. in ACS Leung, L Cheng & S Ozawa (eds), Neural Information Processing - 25th International Conference, ICONIP 2018, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 11305 LNCS, Springer, pp. 138-149, 25th International Conference on Neural Information Processing, ICONIP 2018, Siem Reap, Cambodia, 13/12/18. https://doi.org/10.1007/978-3-030-04221-9_13

Financial data forecasting using optimized echo state network. / Liu, Junxiu; Sun, Tiening; Luo, Yuling et al.
Neural Information Processing - 25th International Conference, ICONIP 2018, Proceedings. ed. / Andrew Chi Sing Leung; Long Cheng; Seiichi Ozawa. Springer, 2018. p. 138-149 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11305 LNCS).

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

TY - GEN

T1 - Financial data forecasting using optimized echo state network

AU - Liu, Junxiu

AU - Sun, Tiening

AU - Luo, Yuling

AU - Fu, Qiang

AU - Cao, Yi

AU - Zhai, Jia

AU - Ding, Xuemei

PY - 2018

Y1 - 2018

N2 - The echo state network (ESN) is a dynamic neural network, which simplifies the training process in the conventional neural network. Due to its powerful non-linear computing ability, it has been applied to predict the time series. However, the parameters of the ESN need to be set experimentally, which can lead to instable performance and there is space to further improve its performance. In order to address this challenge, an improved fruit fly optimization algorithm (IFOA) is proposed in this work to optimize four key parameters of the ESN. Compared to the original fruit fly optimization algorithm (FOA), the proposed IFOA improves the optimization efficiency, where two novel particles are proposed in the fruit flies swarm, and the search process of the swarm is transformed from two-dimensional to three-dimensional space. The proposed approach is applied to financial data sets. Experimental results show that the proposed FOA-ESN and IFOA-ESN models are more effective (~50% improvement) than others, and the IFOA-ESN can obtain the best prediction accuracy.

AB - The echo state network (ESN) is a dynamic neural network, which simplifies the training process in the conventional neural network. Due to its powerful non-linear computing ability, it has been applied to predict the time series. However, the parameters of the ESN need to be set experimentally, which can lead to instable performance and there is space to further improve its performance. In order to address this challenge, an improved fruit fly optimization algorithm (IFOA) is proposed in this work to optimize four key parameters of the ESN. Compared to the original fruit fly optimization algorithm (FOA), the proposed IFOA improves the optimization efficiency, where two novel particles are proposed in the fruit flies swarm, and the search process of the swarm is transformed from two-dimensional to three-dimensional space. The proposed approach is applied to financial data sets. Experimental results show that the proposed FOA-ESN and IFOA-ESN models are more effective (~50% improvement) than others, and the IFOA-ESN can obtain the best prediction accuracy.

KW - algorithm optimization

KW - echo state network

KW - fruit fly algorithm

KW - time series

UR - https://www.scopus.com/pages/publications/85059059319

U2 - 10.1007/978-3-030-04221-9_13

DO - 10.1007/978-3-030-04221-9_13

M3 - Conference contribution

AN - SCOPUS:85059059319

SN - 9783030042202

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

SP - 138

EP - 149

BT - Neural Information Processing - 25th International Conference, ICONIP 2018, Proceedings

A2 - Leung, Andrew Chi Sing

A2 - Cheng, Long

A2 - Ozawa, Seiichi

PB - Springer

T2 - 25th International Conference on Neural Information Processing, ICONIP 2018

Y2 - 13 December 2018 through 16 December 2018

ER -

Liu J, Sun T, Luo Y, Fu Q, Cao Y, Zhai J et al. Financial data forecasting using optimized echo state network. In Leung ACS, Cheng L, Ozawa S, editors, Neural Information Processing - 25th International Conference, ICONIP 2018, Proceedings. Springer. 2018. p. 138-149. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). Epub 2018 Nov 17. doi: 10.1007/978-3-030-04221-9_13

Financial data forecasting using optimized echo state network

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Keywords / Materials (for Non-textual outputs)

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