Exchange rates forecasting using a hybrid fuzzy and neural network model

An-Pin Chen; Hsio Yi Lin

doi:10.1109/CIDM.2007.368952

Exchange rates forecasting using a hybrid fuzzy and neural network model

An-Pin Chen^*, Hsio Yi Lin

^*Corresponding author for this work

Institute of Information Management

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

1 Scopus citations

Abstract

Artificial neural networks (ANNs) are promising approaches for financial time series prediction and have been widely applied to handle finance problems because of its nonlinear structures. However, ANNs have some limitations in evaluating the output nodes as a result of single-point values. This study proposed a hybrid model, called Fuzzy BPN, consisting of backpropagation neural network (BPN) and fuzzy membership function for taking advantage of nonlinear features and interval values instead of the shortcoming of single-point estimation. In addition, the experimental processing can demonstrate the feasibility of applying the hybrid model-Fuzzy BPN and the empirical results show that Fuzzy BPN provides a useful alternative to exchange rate forecasting.

Original language	English
Title of host publication	Proceedings of the 2007 IEEE Symposium on Computational Intelligence and Data Mining, CIDM 2007
Pages	758-763
Number of pages	6
DOIs	https://doi.org/10.1109/CIDM.2007.368952
State	Published - 25 Sep 2007
Event	1st IEEE Symposium on Computational Intelligence and Data Mining, CIDM 2007 - Honolulu, HI, United States Duration: 1 Apr 2007 → 5 Apr 2007

Publication series

Name	Proceedings of the 2007 IEEE Symposium on Computational Intelligence and Data Mining, CIDM 2007

Conference

Conference	1st IEEE Symposium on Computational Intelligence and Data Mining, CIDM 2007
Country	United States
City	Honolulu, HI
Period	1/04/07 → 5/04/07

Keywords

Backpropagation neural network
Exchange rate
Fuzzy membership function

Access to Document

10.1109/CIDM.2007.368952

Fingerprint Dive into the research topics of 'Exchange rates forecasting using a hybrid fuzzy and neural network model'. Together they form a unique fingerprint.

Cite this

Chen, A-P., & Lin, H. Y. (2007). Exchange rates forecasting using a hybrid fuzzy and neural network model. In Proceedings of the 2007 IEEE Symposium on Computational Intelligence and Data Mining, CIDM 2007 (pp. 758-763). [4221376] (Proceedings of the 2007 IEEE Symposium on Computational Intelligence and Data Mining, CIDM 2007). https://doi.org/10.1109/CIDM.2007.368952

@inproceedings{8a2fc85b79614ef4b5aced7559e6f585,

title = "Exchange rates forecasting using a hybrid fuzzy and neural network model",

abstract = "Artificial neural networks (ANNs) are promising approaches for financial time series prediction and have been widely applied to handle finance problems because of its nonlinear structures. However, ANNs have some limitations in evaluating the output nodes as a result of single-point values. This study proposed a hybrid model, called Fuzzy BPN, consisting of backpropagation neural network (BPN) and fuzzy membership function for taking advantage of nonlinear features and interval values instead of the shortcoming of single-point estimation. In addition, the experimental processing can demonstrate the feasibility of applying the hybrid model-Fuzzy BPN and the empirical results show that Fuzzy BPN provides a useful alternative to exchange rate forecasting.",

keywords = "Backpropagation neural network, Exchange rate, Fuzzy membership function",

author = "An-Pin Chen and Lin, {Hsio Yi}",

year = "2007",

month = sep,

day = "25",

doi = "10.1109/CIDM.2007.368952",

language = "English",

isbn = "1424407052",

series = "Proceedings of the 2007 IEEE Symposium on Computational Intelligence and Data Mining, CIDM 2007",

pages = "758--763",

booktitle = "Proceedings of the 2007 IEEE Symposium on Computational Intelligence and Data Mining, CIDM 2007",

note = "null ; Conference date: 01-04-2007 Through 05-04-2007",

}

Chen, A-P & Lin, HY 2007, Exchange rates forecasting using a hybrid fuzzy and neural network model. in Proceedings of the 2007 IEEE Symposium on Computational Intelligence and Data Mining, CIDM 2007., 4221376, Proceedings of the 2007 IEEE Symposium on Computational Intelligence and Data Mining, CIDM 2007, pp. 758-763, 1st IEEE Symposium on Computational Intelligence and Data Mining, CIDM 2007, Honolulu, HI, United States, 1/04/07. https://doi.org/10.1109/CIDM.2007.368952

Exchange rates forecasting using a hybrid fuzzy and neural network model. / Chen, An-Pin; Lin, Hsio Yi.

Proceedings of the 2007 IEEE Symposium on Computational Intelligence and Data Mining, CIDM 2007. 2007. p. 758-763 4221376 (Proceedings of the 2007 IEEE Symposium on Computational Intelligence and Data Mining, CIDM 2007).

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

TY - GEN

T1 - Exchange rates forecasting using a hybrid fuzzy and neural network model

AU - Chen, An-Pin

AU - Lin, Hsio Yi

PY - 2007/9/25

Y1 - 2007/9/25

N2 - Artificial neural networks (ANNs) are promising approaches for financial time series prediction and have been widely applied to handle finance problems because of its nonlinear structures. However, ANNs have some limitations in evaluating the output nodes as a result of single-point values. This study proposed a hybrid model, called Fuzzy BPN, consisting of backpropagation neural network (BPN) and fuzzy membership function for taking advantage of nonlinear features and interval values instead of the shortcoming of single-point estimation. In addition, the experimental processing can demonstrate the feasibility of applying the hybrid model-Fuzzy BPN and the empirical results show that Fuzzy BPN provides a useful alternative to exchange rate forecasting.

AB - Artificial neural networks (ANNs) are promising approaches for financial time series prediction and have been widely applied to handle finance problems because of its nonlinear structures. However, ANNs have some limitations in evaluating the output nodes as a result of single-point values. This study proposed a hybrid model, called Fuzzy BPN, consisting of backpropagation neural network (BPN) and fuzzy membership function for taking advantage of nonlinear features and interval values instead of the shortcoming of single-point estimation. In addition, the experimental processing can demonstrate the feasibility of applying the hybrid model-Fuzzy BPN and the empirical results show that Fuzzy BPN provides a useful alternative to exchange rate forecasting.

KW - Backpropagation neural network

KW - Exchange rate

KW - Fuzzy membership function

UR - http://www.scopus.com/inward/record.url?scp=34548808208&partnerID=8YFLogxK

U2 - 10.1109/CIDM.2007.368952

DO - 10.1109/CIDM.2007.368952

M3 - Conference contribution

AN - SCOPUS:34548808208

SN - 1424407052

SN - 9781424407057

T3 - Proceedings of the 2007 IEEE Symposium on Computational Intelligence and Data Mining, CIDM 2007

SP - 758

EP - 763

BT - Proceedings of the 2007 IEEE Symposium on Computational Intelligence and Data Mining, CIDM 2007

Y2 - 1 April 2007 through 5 April 2007

ER -