On searching the best mode for forex forecasting: bidirectional long short-term memory default mode is not enough

Seng Hansun, Farica Perdana Putri, Abdul Q. M. Khaliq, Hugeng Hugeng


Presently, the Forex market has become the world’s largest financial market with more than US$5 trillion daily volume. Therefore, it attracts many researchers to learn its traded currency pairs characteristics and predict their future values. Here, we propose simple three layers Bidirectional long short-term memory (Bi-LSTM) networks for Forex forecasting with four different merge modes. Moreover, the proposed model is also compared to the conventional long short-term memory (LSTM) networks with the same architecture. Five major Forex currency pairs, namely AUD/USD, EUR/USD, GBP/USD, USD/CHF, and USD/JPY, with more than ten years of historical records are considered in this study. It is revealed from the experimental results that among four available merge modes, the concatenation mode as the default merge mode in Bi-LSTM networks is actually the less preferred mode for Forex forecasting (Root mean square error 0.30685517, mean absolute error 0.27442235, mean absolute percentage error 0.827108%). Moreover, Bi-LSTM average mode gets the highest R2 score that could achieve 89.579%. Therefore, the proposed three layers Bi-LSTM networks could provide a baseline result for developing a good trading strategy in Forex forecasting.


Bidirectional long short-term memory; Currency pair; Deep learning; Forex forecasting; Long short-term memory;

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DOI: http://doi.org/10.11591/ijai.v11.i4.pp1596-1606


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