Comparison of neural networks for foreign exchange rate prediction

dc.contributor.author	Agarwal, P.
dc.contributor.author	Sahani, A. K.
dc.date.accessioned	2021-06-21T19:58:47Z
dc.date.available	2021-06-21T19:58:47Z
dc.date.issued	2021-06-22
dc.identifier.uri	http://localhost:8080/xmlui/handle/123456789/1887
dc.description.abstract	The paper incorporates a comparison between long short term memory (LSTM), simple recurrent neural networks (SRNN), and gated recurrent unit (GRU) to predict currency exchange rate for 22 countries-currencies against United States Dollar(USD) simultaneously. The models predict foreign currency exchange rates for 30 consecutive days by taking the last 365 days of data as input. The models work with the same number of neural network layers, input, targeted output, optimizer, and learning rate.	en_US
dc.language.iso	en_US	en_US
dc.subject	LSTM	en_US
dc.subject	SRNN	en_US
dc.subject	GRU	en_US
dc.subject	Currency Exchange rate: Foreign exchange rate	en_US
dc.title	Comparison of neural networks for foreign exchange rate prediction	en_US
dc.type	Article	en_US