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Foreign Currency Exchange Rate Prediction using Neuro-Fuzzy Systems

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Foreign Currency Exchange Rate Prediction using Neuro-Fuzzy Systems. / Yong, Yoke Leng; Lee, Yunli; Gu, Xiaowei; Angelov, Plamen Parvanov; Ling Ngo, David Chek; Shafipour Yourdshahi, Elnaz.

Procedia Computer Science. Vol. 144 Elsevier, 2018. p. 232-238 (Procedia Computer Science; Vol. 144).

Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSNConference contribution/Paper

Harvard

Yong, YL, Lee, Y, Gu, X, Angelov, PP, Ling Ngo, DC & Shafipour Yourdshahi, E 2018, Foreign Currency Exchange Rate Prediction using Neuro-Fuzzy Systems. in Procedia Computer Science. vol. 144, Procedia Computer Science, vol. 144, Elsevier, pp. 232-238, INNS Conference on Big Data and Deep Learning , 17/04/18. https://doi.org/10.1016/j.procs.2018.10.523

APA

Yong, Y. L., Lee, Y., Gu, X., Angelov, P. P., Ling Ngo, D. C., & Shafipour Yourdshahi, E. (2018). Foreign Currency Exchange Rate Prediction using Neuro-Fuzzy Systems. In Procedia Computer Science (Vol. 144, pp. 232-238). (Procedia Computer Science; Vol. 144). Elsevier. https://doi.org/10.1016/j.procs.2018.10.523

Vancouver

Yong YL, Lee Y, Gu X, Angelov PP, Ling Ngo DC, Shafipour Yourdshahi E. Foreign Currency Exchange Rate Prediction using Neuro-Fuzzy Systems. In Procedia Computer Science. Vol. 144. Elsevier. 2018. p. 232-238. (Procedia Computer Science). https://doi.org/10.1016/j.procs.2018.10.523

Author

Yong, Yoke Leng ; Lee, Yunli ; Gu, Xiaowei ; Angelov, Plamen Parvanov ; Ling Ngo, David Chek ; Shafipour Yourdshahi, Elnaz. / Foreign Currency Exchange Rate Prediction using Neuro-Fuzzy Systems. Procedia Computer Science. Vol. 144 Elsevier, 2018. pp. 232-238 (Procedia Computer Science).

Bibtex

@inproceedings{df7633bde6014de39996cba93170395b,
title = "Foreign Currency Exchange Rate Prediction using Neuro-Fuzzy Systems",
abstract = "The complex nature of the foreign exchange (FOREX) market along with the increased interest in the currency exchange market has prompted extensive research from various academic disciples in aiding traders in their in-depth analysis and decision making processes. An approach incorporating the use of historical data along with computational intelligence for analysis and forecasting is proposed in this paper. Firstly, the Gaussian Mixture Model method is applied for the purpose of data partitioning on historical observations. Then, the antecedent part of the neuro-fuzzy system of AnYa type is initialized by the partitioning result and the consequent part is trained using the fuzzily weighted RLS algorithm based on the same data. Numerical examples based on the real currency exchange data demonstrated that the proposed approach trained with historical data is able to produce optimizing results on forecasting the future foreign exchange rates for a very long period, and also show the potential of the proposed approach in real applications.",
keywords = "Gaussian Mixture Model, Neuro-Fuzzy, FOREX forecasting",
author = "Yong, {Yoke Leng} and Yunli Lee and Xiaowei Gu and Angelov, {Plamen Parvanov} and {Ling Ngo}, {David Chek} and {Shafipour Yourdshahi}, Elnaz",
year = "2018",
month = dec
day = "1",
doi = "10.1016/j.procs.2018.10.523",
language = "English",
volume = "144",
series = "Procedia Computer Science",
publisher = "Elsevier",
pages = "232--238",
booktitle = "Procedia Computer Science",
note = " INNS Conference on Big Data and Deep Learning ; Conference date: 17-04-2018",
url = "http://innsbigdata2018.org/",

}

RIS

TY - GEN

T1 - Foreign Currency Exchange Rate Prediction using Neuro-Fuzzy Systems

AU - Yong, Yoke Leng

AU - Lee, Yunli

AU - Gu, Xiaowei

AU - Angelov, Plamen Parvanov

AU - Ling Ngo, David Chek

AU - Shafipour Yourdshahi, Elnaz

PY - 2018/12/1

Y1 - 2018/12/1

N2 - The complex nature of the foreign exchange (FOREX) market along with the increased interest in the currency exchange market has prompted extensive research from various academic disciples in aiding traders in their in-depth analysis and decision making processes. An approach incorporating the use of historical data along with computational intelligence for analysis and forecasting is proposed in this paper. Firstly, the Gaussian Mixture Model method is applied for the purpose of data partitioning on historical observations. Then, the antecedent part of the neuro-fuzzy system of AnYa type is initialized by the partitioning result and the consequent part is trained using the fuzzily weighted RLS algorithm based on the same data. Numerical examples based on the real currency exchange data demonstrated that the proposed approach trained with historical data is able to produce optimizing results on forecasting the future foreign exchange rates for a very long period, and also show the potential of the proposed approach in real applications.

AB - The complex nature of the foreign exchange (FOREX) market along with the increased interest in the currency exchange market has prompted extensive research from various academic disciples in aiding traders in their in-depth analysis and decision making processes. An approach incorporating the use of historical data along with computational intelligence for analysis and forecasting is proposed in this paper. Firstly, the Gaussian Mixture Model method is applied for the purpose of data partitioning on historical observations. Then, the antecedent part of the neuro-fuzzy system of AnYa type is initialized by the partitioning result and the consequent part is trained using the fuzzily weighted RLS algorithm based on the same data. Numerical examples based on the real currency exchange data demonstrated that the proposed approach trained with historical data is able to produce optimizing results on forecasting the future foreign exchange rates for a very long period, and also show the potential of the proposed approach in real applications.

KW - Gaussian Mixture Model

KW - Neuro-Fuzzy

KW - FOREX forecasting

U2 - 10.1016/j.procs.2018.10.523

DO - 10.1016/j.procs.2018.10.523

M3 - Conference contribution/Paper

VL - 144

T3 - Procedia Computer Science

SP - 232

EP - 238

BT - Procedia Computer Science

PB - Elsevier

T2 - INNS Conference on Big Data and Deep Learning

Y2 - 17 April 2018

ER -