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Paper
Title

Foreign Exchange Rate Forecasting Using Improved Artificial Neural Networks in Incomplete Data

Year: 1391
COI: IIEC08_121
Language: EnglishView: 917
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Authors

m K.hashei - Isfahan University Of Technology
f Mokhatab Rafiei
m Bijari

Abstract:

In financial markets and specifically exchange rate markets, the environment is fun of uncertainties and changes occur rapidly. Therefore, forecasting in thesesituations requires methods that also work efficiently with incomplete data. In this paper, an improved version of artificial neural networks is proposed by applying the fuzzylogic in order to yield more accurate results, especiaUy for cases where inadequate historical data are available. In our proposed model, instead of using crisp connected weights in traditional artificial neural networks, they are considered as fuzzy numbers to reduce the required data in the training process and improve the performance of the proposed model, especially with scant data. The empirical results of exchangerate forecasting indicate that the proposed model can be an effective way to improve the forecasting accuracy, especially in incomplete data situations.

Keywords:

Artificial Neural Networks (ANNs) , Fuzzy logic , Time series forecasting , Hybrid models , Exchange rate

Paper COI Code

This Paper COI Code is IIEC08_121. Also You can use the following address to link to this article. This link is permanent and is used as an article registration confirmation in the Civilica reference:

https://civilica.com/doc/172941/

How to Cite to This Paper:

If you want to refer to this Paper in your research work, you can simply use the following phrase in the resources section:
K.hashei, m and Mokhatab Rafiei, f and Bijari, m,1391,Foreign Exchange Rate Forecasting Using Improved Artificial Neural Networks in Incomplete Data,08th International Industrial Engineering Conference,Tehran,https://civilica.com/doc/172941

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Type of center: دانشگاه دولتی
Paper count: 14,170
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