Abstract:
The exchange rate is a very important economic
indicator for countries with market economies as its
fluctuations heavily affect the most areas in the economy.
Accordingly, predicting future values of foreign exchange rates
is very important in policymaking. This study was conducted
to perform multi-step ahead predictions on foreign exchange
rates of Sri Lankan Rupee against three international
currencies using Artificial Neural Network models, to measure
the accuracies of these models and identify shortcomings if
present. MUlti-Layer Perceptron, Simple Recurrent Neural
Network, Long Short-Term Memory, Gated Recurrent Unit
and Convolutional Neural Network architectures were
employed for this study. Most of the models except few Gated
Recurrent Unit models were able to predict lO-days-ahead
exchange rates with a higher level of accuracies (97%-99%).
According to the findings Stateful Simple Recurrent Neural
Networks with one input layer, a hidden layer, a flatten layer
and an output layer performed as the best architecture to
predict the three exchange rates selected.
