Hybrid GRU Bi-LSTM model to improve cryptocurrency prediction accuracy

Ferdiansyah Ferdiansyah, Siti Hajar Othman, Raja Zahilah Md Radzi, Deris Stiawan, Tole Sutikno


Cryptocurrency is a virtual or digital currency used in financial systems that utilizes blockchain technology and cryptographic functions to gain transparency, decentralization, and conservation. Cryptocurrency prices have a high level of fluctuation; thus, tools are needed to monitor and predict them. RNN is a deep learning model that is capable of strongly predicting data time series. Some types of Recurrent Nureal Network layers, such as Long Short Term Memory, have been used in previous studies to prediction common used currency. In this study, we used the Gate Recurrent Unit and Bidirectional–LSTM hybrid model to predict cryptocurrency prices to improve the accuracy of previously proposed prediction LSTM Model to predict the Bitcoin,  Using four cryptocurrencies (Bitcoin, Ehtereum, Ripple, and Binance), we obtained very good results with RMSE after normalization the results get closer to 0 and with MAPE values all below <10%.


Cryptocurrency, GRU Bi-LSTM, Deep learning, RMSE,MAPE

DOI: http://doi.org/10.11591/ijai.v12.i1.pp%25p


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