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All Journal TELKOMNIKA (Telecommunication Computing Electronics and Control)
Khalid El Fahssi
University Sidi Mohamed Ben Abdellah
Computer Science & IT

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Simple RNN-LSTM hybrid deep learning model for Bitcoin and EUR_USD forecasting Mohamed EL Mahjouby; Khalid El Fahssi; Mohamed Taj Bennani; Mohamed Lamrini; Mohamed El Far
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 23, No 1: February 2025
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v23i1.25925

Abstract

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.
Co-Authors Mohamed El Far Mohamed EL Mahjouby Mohamed Lamrini Mohamed Taj Bennani