Cryptocurrency Time Series Forecasting Based on Ensemble and Deep Learning Algorithms

A Comprehensive Review

Authors

DOI:

https://doi.org/10.14500/aro.12280

Keywords:

Cryptocurrency, Deep learning, Ensemble deep learning, Price prediction, Time-series

Abstract

Blockchain technology is considered a transformative innovation, offering decentralized, secure, and transparent solutions to various industries, with cryptocurrencies being its most famous application. The volatility and non-linear behavior of cryptocurrency markets pose significant challenges for predicting their prices accurately. Predicting cryptocurrencies prices based on traditional statistical methods often fail to capture the market complex dynamics. Therefore, the recent developments in Artificial Intelligence, especially in deep learning and ensemble-based approaches have presented promising results. This study delivers a comprehensive literature review focusing on applying deep learning and ensemble deep learning algorithms in cryptocurrency time series price prediction. The main deep learning models such as Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), Convolutional Neural Network (CNN), and Recurrent Neural Network (RNN) are examined with a variety of time intervals and cryptocurrency types. The findings present that deep learning models, especially when used in hybrid or ensemble configurations, have obtained promising results. This review highlights the efficacy and significant potential of ensemble deep learning and its capabilities in cryptocurrencies price trend forecasting offering valuable insights for investors and researchers.

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Author Biographies

Hilmi S. Abdullah, Department of IT, Technical College of Informatics - Akre, Akre University for Applied Sciences, Kurdistan Region – F.R. Iraq

Hilmi S. Abdullah is a Lecturer at the Department of Information Technology, Amedi Technical Institute,
Duhok Polytechnic University. He got the B.Sc. degree in Software Engineering and the M.Sc. degree in
Software Engineering. His research interests are in artificial intelligence, blockchain and information
security.

Adnan M. Abdulazeez, Department of Energy, Technical College of Engineering, Duhok Polytechnic University, Kurdistan Region – F.R. Iraq

Adnan M. Abdulazeez is a Professor at the Department of Energy, College of Technical Engineering,
Duhok Polytechnic University. He received the B.Sc. degree in Electrical and Electronic Engineering and
the M.Sc. degree in Control and Computer Engineering from the University of Technology, Baghdad,
Iraq. He obtained the Ph.D. degree in Computer Engineering from the University of Mosul, Iraq. His field
of interest is artificial intelligence. He is a member of the IEEE.

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ARO Journal: Volume 13, No. 2 (2025)

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Published

2025-12-15

How to Cite

Abdullah, H. S. and Abdulazeez, A. M. (2025) “Cryptocurrency Time Series Forecasting Based on Ensemble and Deep Learning Algorithms: A Comprehensive Review”, ARO-THE SCIENTIFIC JOURNAL OF KOYA UNIVERSITY, 13(2), pp. 359–366. doi: 10.14500/aro.12280.

Issue

Vol. 13 No. 2 (2025): Edition Twenty Five

Section

Review Articles

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Copyright (c) 2025 Hilmi S. Abdullah, Adnan M. Abdulazeez

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Received 2025-05-14
Accepted 2025-10-30
Published 2025-12-15

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