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

Mohamed EL Mahjouby (University Sidi Mohamed Ben Abdellah)
Khalid El Fahssi (University Sidi Mohamed Ben Abdellah)
Mohamed Taj Bennani (University Sidi Mohamed Ben Abdellah)
Mohamed Lamrini (University Sidi Mohamed Ben Abdellah)
Mohamed El Far (University Sidi Mohamed Ben Abdellah)

TELKOMNIKA (Telecommunication Computing Electronics and Control)
Vol 23, No 1: February 2025

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.