Simple RNN-LSTM hybrid deep learning model for Bitcoin and EUR_USD forecasting

Simple RNN-LSTM hybrid deep learning model for Bitcoin and EUR_USD forecasting

Bitcoin
Deep learning
Gated recurrent unit
Long short term memory
Simple recurrent neural network
Time series forecasting

The popularity of deep learning in time series prediction has significantly increased compared to the past. In this article, we utilize deep learning methods, which encompass long short term memory (LSTM) networks, simple recurrent neural network (SimpleRNN) networks, and gated recurrent units (GRU) networks. This research introduces a hybrid foundational model for forecasting future closing prices of EUR_USD in financial time series and Bitcoin, combining SimpleRNN with LSTM, referred to as SimpleRNN-LSTM. To improve the precisions of our hybrid model, we incorporate twenty-one technical indicators into the training data. Then, we compute four measures to evaluate the performance of various prediction models. When predicting currency pairs EUR_USD and Bitcoin, our hybrid foundational model outperforms SimpleRNN, LSTM, and GRU models.

Mohamed EL Mahjouby1, Khalid El Fahssi2, Mohamed Taj Bennani1, Mohamed Lamrini1, Mohamed El Far1

Journal homepage: http://telkomnika.uad.ac.id

Nov 26, 2024

PERI IRAWAN

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