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INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT International Peer Reviewed & Refereed Journals, Open Access Journal ISSN Approved Journal No: 2456-4184 | Impact factor: 8.76 | ESTD Year: 2016 Scholarly open access journals, Peer-reviewed, and Refereed Journals, Impact factor 8.76 (Calculate by google scholar and Semantic Scholar | AI-Powered Research Tool) , Multidisciplinary, Monthly, Indexing in all major database & Metadata, Citation Generator, Digital Object Identifier(DOI) |
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| Paper Title: | Artificial intelligence Application to EURO-USD Forex Value prediction using LSTM Tensor flow |
| Authors Name: | Igboanusi Mirian Odigomma |
| Download E-Certificate: | Download |
| Author Reg. ID: |
IJNRD_216968
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| Published Paper Id: | IJNRD2403674 |
| Published In: | Volume 9 Issue 3, March-2024 |
| DOI: | |
| Abstract: | The prediction of financial instruments in exchange markets are time sensitive. The application of AI algorithm and the use of big data of past exchange rate are applied to determine the future value of stock. Artificial intelligence as a disruptive technology has impacted various sectors and industries in the world. The impact on the financial sector is evident in the use for data analysis, investment and portfolio management, trading and forex. In view of these developments, several at- tempts and schemes have been developed in recent time to optimize and ensure accurate forecasting of forex. However, existing solutions comes with computation complexity arising from the use of combined or ensemble artificial models. In this work, a proposed LSTM is used without combing to another model to reduce the computational complexity as well as use of nonlinear model to forecast forex with minimal error and high accuracy. By varying their learning rate, number of hid- den layers, and optimizer. To verify the proposed scheme, we have developed two structures of LSTM. First structure comprised two hidden layers while the second comprised three hidden layers. Data set used is the EURO-USD historical data from investment.com. The incorporated optimizer are “Adam” and gradient decent respectively. With 2 hidden layers, the gradient decent optimizer outperformed the Adam optimizer. However, increasing the number of hidden layers, Adam optimizer achieved less error. Despite this, the best performance with final error rate of 0.0005 (MSE) is with 2 hidden layers and gradient descent optimizer. Thus, the proposed scheme was able to achieve predicted results close to the actual values. This breakthrough opens opportunity for high accuracy software in forex trading. |
| Keywords: | Forex, LSTM, machine learning, prediction, tensor flow. |
| Cite Article: | "Artificial intelligence Application to EURO-USD Forex Value prediction using LSTM Tensor flow", International Journal of Novel Research and Development (www.ijnrd.org), ISSN:2456-4184, Vol.9, Issue 3, page no.g619-g632, March-2024, Available :http://www.ijnrd.org/papers/IJNRD2403674.pdf |
| Downloads: | 00038 |
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2456-4184 | IMPACT FACTOR: 8.76 Calculated By Google Scholar| ESTD YEAR: 2016 An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.76 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator |
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Published Paper ID:IJNRD2403674 Registration ID: 216968 Published In: Volume 9 Issue 3, March-2024 DOI (Digital Object Identifier): Page No: g619-g632 Country: Gumi, Gyeongsangbuk do south Korea, Korea, Republic of Research Area: Science & Technology Publisher : IJ Publication Published Paper URL : https://www.ijnrd.org/viewpaperforall?paper=IJNRD2403674 Published Paper PDF: https://www.ijnrd.org/papers/IJNRD2403674 |
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ISSN: 2456-4184
Impact Factor: 8.76 and ISSN APPROVED
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