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Foreign currency exchange rate prediction using neuro-fuzzy systems

Yong, Yoke Leng, Lee, Yunli, Gu, Xiaowei, Angelov, Plamen P, Ngo, David Chek Ling, Shafipour, Elnaz (2018) Foreign currency exchange rate prediction using neuro-fuzzy systems. Procedia Computer Science, 144 . pp. 232-238. ISSN 1877-0509. (doi:10.1016/j.procs.2018.10.523) (The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided) (KAR id:90406)

The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided. (Contact us about this Publication)
Official URL
https://doi.org/10.1016/j.procs.2018.10.523

Abstract

The complex nature of the foreign exchange (FOREX) market along with the increased interest towards the currency exchange market has prompted extensive research from various academic disciplines. With the inclusion of more in-depth analysis and forecasting methods, traders will be able to make an informed decision when trading. Therefore, 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 data partitioning on historical observations. While the antecedent part of the neuro-fuzzy system of AnYa type is initialised by the partitioning result, 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 produce promising results when used to forecast the future foreign exchange rates over a long-term period. Although implemented in an offline environment, it could potentially be utilised in real-time application in the future.

Item Type: Article
DOI/Identification number: 10.1016/j.procs.2018.10.523
Uncontrolled keywords: Gaussian Mixture Model; Neuro-Fuzzy; FOREX forecasting
Subjects: Q Science > QA Mathematics (inc Computing science) > QA 75 Electronic computers. Computer science
Divisions: Divisions > Division of Computing, Engineering and Mathematical Sciences > School of Computing
Depositing User: Amy Boaler
Date Deposited: 28 Sep 2021 10:33 UTC
Last Modified: 29 Sep 2021 10:41 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/90406 (The current URI for this page, for reference purposes)
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