A RPCL-CLP architecture for financial time series forecasting

Yiu-ming Cheung; Wai Man Leung; Lei Xu

doi:10.1109/ICNN.1995.487525

A RPCL-CLP architecture for financial time series forecasting

Yiu-ming Cheung^*, Wai Man Leung, Lei Xu

^*Corresponding author for this work

Department of Computer Science

Research output: Chapter in book/report/conference proceeding › Conference proceeding › peer-review

5 Citations (Scopus)

Abstract

In this paper, we propose a new architecture based on the rival penalized competitive learning algorithm (RPCL) of Xu, Krzyzak and Oja (1993) and combined linear prediction method (CLP). The performance of RPCL-CLP is insensitive to the initial number of cluster nodes selected. Experimental results show that it is robust in long-term prediction for financial time series forecasting.

Original language	English
Title of host publication	Proceedings of ICNN'95 - International Conference on Neural Networks
Publisher	IEEE
Pages	829-832
Number of pages	4
Volume	2
ISBN (Print)	0780327683
DOIs	https://doi.org/10.1109/ICNN.1995.487525
Publication status	Published - 27 Nov 1995
Event	1995 IEEE International Conference on Neural Networks - Perth, WA, Australia Duration: 27 Nov 1995 → 1 Dec 1995 https://ieeexplore.ieee.org/xpl/conhome/3505/proceeding

Publication series

Name	IEEE International Conference on Neural Networks - Conference Proceedings

Conference

Conference	1995 IEEE International Conference on Neural Networks
Country/Territory	Australia
City	Perth, WA
Period	27/11/95 → 1/12/95
Internet address	https://ieeexplore.ieee.org/xpl/conhome/3505/proceeding

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10.1109/ICNN.1995.487525

Cite this

@inproceedings{85da3638a39242a6b86d3d77572430f6,

title = "A RPCL-CLP architecture for financial time series forecasting",

abstract = "In this paper, we propose a new architecture based on the rival penalized competitive learning algorithm (RPCL) of Xu, Krzyzak and Oja (1993) and combined linear prediction method (CLP). The performance of RPCL-CLP is insensitive to the initial number of cluster nodes selected. Experimental results show that it is robust in long-term prediction for financial time series forecasting.",

author = "Yiu-ming Cheung and Leung, {Wai Man} and Lei Xu",

note = "This work was supported by Ho Sin-Hang Education Endowment Fund for Project HSH 95/02.; 1995 IEEE International Conference on Neural Networks ; Conference date: 27-11-1995 Through 01-12-1995",

year = "1995",

month = nov,

day = "27",

doi = "10.1109/ICNN.1995.487525",

language = "English",

isbn = "0780327683",

volume = "2",

series = "IEEE International Conference on Neural Networks - Conference Proceedings",

publisher = "IEEE",

pages = "829--832",

booktitle = "Proceedings of ICNN'95 - International Conference on Neural Networks",

address = "United States",

url = "https://ieeexplore.ieee.org/xpl/conhome/3505/proceeding",

}

Cheung, Y, Leung, WM & Xu, L 1995, A RPCL-CLP architecture for financial time series forecasting. in Proceedings of ICNN'95 - International Conference on Neural Networks. vol. 2, IEEE International Conference on Neural Networks - Conference Proceedings, IEEE, pp. 829-832, 1995 IEEE International Conference on Neural Networks, Perth, WA, Australia, 27/11/95. https://doi.org/10.1109/ICNN.1995.487525

A RPCL-CLP architecture for financial time series forecasting. / Cheung, Yiu-ming; Leung, Wai Man; Xu, Lei.
Proceedings of ICNN'95 - International Conference on Neural Networks. Vol. 2 IEEE, 1995. p. 829-832 (IEEE International Conference on Neural Networks - Conference Proceedings).

Research output: Chapter in book/report/conference proceeding › Conference proceeding › peer-review

TY - GEN

T1 - A RPCL-CLP architecture for financial time series forecasting

AU - Cheung, Yiu-ming

AU - Leung, Wai Man

AU - Xu, Lei

N1 - This work was supported by Ho Sin-Hang Education Endowment Fund for Project HSH 95/02.

PY - 1995/11/27

Y1 - 1995/11/27

N2 - In this paper, we propose a new architecture based on the rival penalized competitive learning algorithm (RPCL) of Xu, Krzyzak and Oja (1993) and combined linear prediction method (CLP). The performance of RPCL-CLP is insensitive to the initial number of cluster nodes selected. Experimental results show that it is robust in long-term prediction for financial time series forecasting.

AB - In this paper, we propose a new architecture based on the rival penalized competitive learning algorithm (RPCL) of Xu, Krzyzak and Oja (1993) and combined linear prediction method (CLP). The performance of RPCL-CLP is insensitive to the initial number of cluster nodes selected. Experimental results show that it is robust in long-term prediction for financial time series forecasting.

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U2 - 10.1109/ICNN.1995.487525

DO - 10.1109/ICNN.1995.487525

M3 - Conference proceeding

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SN - 0780327683

VL - 2

T3 - IEEE International Conference on Neural Networks - Conference Proceedings

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BT - Proceedings of ICNN'95 - International Conference on Neural Networks

PB - IEEE

T2 - 1995 IEEE International Conference on Neural Networks

Y2 - 27 November 1995 through 1 December 1995

ER -

A RPCL-CLP architecture for financial time series forecasting

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