Hybrid gated recurrent unit bidirectional-long short-term memory model to improve cryptocurrency prediction accuracy
Abstract
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%.
Keywords
Cryptocurrency, Deep learning, Gated recurrent unit, Long short-term memory, Mean absolute percentage error, Root mean square error
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PDFDOI: http://doi.org/10.11591/ijai.v12.i1.pp251-261
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IAES International Journal of Artificial Intelligence (IJ-AI)
ISSN/e-ISSN 2089-4872/2252-8938
This journal is published by the Institute of Advanced Engineering and Science (IAES) in collaboration with Intelektual Pustaka Media Utama (IPMU).