Abstract:
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.
