A Comparative Study of Deep Learning Models for Bitcoin Price Prediction Using NeuralProphet, RNN, and LSTM
Received: 18 April 2025 | Revised: 30 June 2025, 17 July 2025, and 5 August 2025 | Accepted: 8 August 2025 | Online: 9 February 2026
Corresponding author: Mohammad Ahmed Alomari
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.
Keywords:
Bitcoin, cryptocurrency, Long Short-Term Memory (LSTM), Recurrent Neural Network (RNN), NeuralProphetDownloads
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Copyright (c) 2025 Tan Khai Lian, Ismail Ahmad Al-Qasem Al-Hadi, Mohammad Ahmed Alomari, Mohammed Nasser Al-Andoli, Muhammed Basheer Jasser, AbdulGuddoos S. A. Gaid
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