Using Bidirectional, GRU and LSTM Neural Network methods for Multi-Currency Exchange Rates Prediction
Amit R. Nagpure1, Avinash J. Agrawal2
1Amit R. Nagpure, Department of Computer Science and Engineering, Shri Ramdeobaba College of Engineering and Management, Nagpur, India.
2Dr. Avinash J. Agrawal, Department of Computer Science and Engineering, Shri Ramdeobaba College of Engineering and Management, Nagpur, India.
Manuscript received on 05 May 2019 | Revised Manuscript received on 12 May 2019 | Manuscript published on 30 May 2019 | PP: 716-722 | Volume-8 Issue-7, May 2019 | Retrieval Number: G5592058719/19©BEIESP
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Anticipating multi-money trade rates and handling time arrangement data is regularly a critical issue in the monetary market. This paper bids the forecast of top exchanged monetary forms on the globe utilizing diverse profound learning models which incorporate top remote trade (Forex) monetary forms. This paper implements the Recurrent Neural Network models using Bidirectional RNN, Gated Recurrent Unit (GRU) and Long Short-Term Memory (LSTM) network. They foresee the transaction scale between the world’s top exchanged monetary standards, for example, EUR, JPY, GBP, AUD, CAD, CHF, CNY, SEK, NZD, and INR from information by day, over 30 years to March 2019. Index Terms:
Keyword: Bi-directional RNN, Deep Learning, Foreign exchange (Forex), Gated Recurrent Unit (GRU), Long Short-Term Memory (LSTM) network, Multi-currency, Machine learning, Recurrent Neural Network (RNN).
Scope of the Article: Deep Learning