Deep Learning for Exchange Rate Prediction Within Time Constrain

Abstract

The implementation of an open economic system in Indonesia since 1969 has significant impact to the national economic growth. The high demand and supply of goods from within the country involved in international trade demonstrate a close correlation between export and import activities with the exchange rate of the rupiah. Economic stability is measured through the stability of the rupiah exchange rate against foreign currencies. The balance between demand and supply in the global market is considered crucial for creating a stable economy. History has recorded the Indonesian economic crisis in 1998, where the exchange rate of the rupiah against the US dollar drastically raises and causing challenges to the domestic production cost. This research aiming to make predictions using data science approach based on historical (time series) data. GRU, LSTM, and RNN algorithm being assess to perform the prediction. Results show that RNN algorithms generally outperform GRU and LSTM in making the prediction, particularly with limited time series data. Although RNN is typically superior, in one instance, GRU achieved slightly higher accuracy (0.047% difference) for the CNY to IDR pair over five years. Furthermore, the research highlights the substantial impact of batch size on algorithm accuracy, considering external factors such as interest rates. These findings offer valuable insights for economic decision-making and policy formulation.