Citation

Lian, Tan Khai and Al-Hadi, Ismail Ahmad Al-Qasem and Alomari, Mohammad Ahmed and Al-Andoli, Mohammed Nasser and Jasser, Muhammed Basheer and Gaid, AbdulGuddoos S. A. (2026) A Comparative Study of Deep Learning Models for Bitcoin Price Prediction Using NeuralProphet, RNN, and LSTM. Engineering, Technology & Applied Science Research, 16 (1). pp. 31263-31273. ISSN 2241-4487

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Abstract

Bitcoin has recently emerged as a leading asset in the cryptocurrency market. However, its significant price volatility presents challenges for accurate prediction. Due to this volatility, forecasting Bitcoin prices accurately is difficult and complicates decision-making for investors and traders in the cryptocurrency space. This research compares the accuracy of three prediction models: Long Short-Term Memory (LSTM), Recurrent Neural Network (RNN), and Facebook's NeuralProphet, introduced in 2021, focusing on improving Bitcoin price forecasting accuracy. The study uses daily Bitcoin prices from the past five years to assess model performance. Results indicate that the LSTM model outperforms both NeuralProphet and RNN in prediction accuracy. This comparison holds substantial economic significance, as accurate predictions can assist investors and traders in making informed decisions within the cryptocurrency market.

Item Type:	Article
Uncontrolled Keywords:	Bitcoin, cryptocurrency, Long Short-Term Memory (LSTM), Recurrent Neural Network (RNN), NeuralProphet
Subjects:	H Social Sciences > HG Finance > HG1710 Electronic Funds Transfers
Divisions:	Faculty of Computing and Informatics (FCI)
Depositing User:	Ms Suzilawati Abu Samah
Date Deposited:	04 Jun 2026 03:00
Last Modified:	09 Jun 2026 00:52
URII:	http://shdl.mmu.edu.my/id/eprint/15912

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