  Foreign currency exchange plays a vital role for trading of currency in the
financial market. Due to its volatile nature, prediction of foreign currency
exchange is a challenging task. This paper presents different machine learning
techniques like Artificial Neural Network (ANN), Recurrent Neural Network (RNN)
to develop prediction model between Nepalese Rupees against three major
currencies Euro, Pound Sterling and US dollar. Recurrent Neural Network is a
type of neural network that have feedback connections. In this paper,
prediction model were based on different RNN architectures, feed forward ANN
with back propagation algorithm and then compared the accuracy of each model.
Different ANN architecture models like Feed forward neural network, Simple
Recurrent Neural Network (SRNN), Gated Recurrent Unit (GRU) and Long Short Term
Memory (LSTM) were used. Input parameters were open, low, high and closing
prices for each currency. From this study, we have found that LSTM networks
provided better results than SRNN and GRU networks.
