Foreign currency exchange rate prediction using neuro-fuzzy systems
| Autor: | Plamen Angelov, Yoke Leng Yong, Elnaz Shafipour, Xiaowei Gu, David Chek Ling Ngo, Yunli Lee |
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| Rok vydání: | 2018 |
| Předmět: |
050208 finance
Neuro-fuzzy Antecedent (logic) Computer science 05 social sciences Computational intelligence 02 engineering and technology Mixture model computer.software_genre Currency 0502 economics and business 0202 electrical engineering electronic engineering information engineering General Earth and Planetary Sciences Foreign currency exchange 020201 artificial intelligence & image processing Data mining Foreign exchange market computer General Environmental Science |
| Zdroj: | INNS Conference on Big Data |
| ISSN: | 1877-0509 |
| DOI: | 10.1016/j.procs.2018.10.523 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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