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Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review
Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review
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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 -