A Comparative Evaluation of SARIMAX, LSTM, and Prophet Models for Cryptocurrency Price Trend Prediction
Received: 15 October 2025 | Revised: 21 November 2025 and 19 December 2025 | Accepted: 21 December 2025 | Online: 4 April 2026
Corresponding author: Drissia Ennagoura
Abstract
Cryptocurrency price prediction is challenging due to strong nonlinearity and high volatility. This paper comparatively evaluates three forecasting models for Ethereum (ETH): SARIMAX with exogenous technical indicators, Long Short-Term Memory (LSTM) networks, and Facebook Prophet. Relative Strength Index (RSI), Moving Average Convergence Divergence (MACD), and Exponential Moving Average (EMA) are incorporated to enhance signal quality. Empirical results reveal clear trade-offs between predictive accuracy, profitability, and risk. SARIMAX achieves the highest directional accuracy (75.00%) with limited profitability, while LSTM yields the highest cumulative profit (23.84%) at the cost of higher drawdown. Prophet provides a balanced compromise between accuracy and risk. The study contributes by jointly evaluating statistical forecasting accuracy and trading-oriented performance metrics, offering practical insights into model suitability for different investor risk profiles.
Keywords:
cryptocurrency, Ethereum (ETH), price prediction, SARIMAX, LSTM, Facebook Prophet, technical indicators, algorithmic tradingDownloads
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Copyright (c) 2026 Drissia Ennagoura, Kamal El Kehal, Abdelhamid Berdai, Safae Merzouk, Khalid El Fahsi, Mohamed El Mahjouby, Mohamed El Far, Mohamed Taj Bennani
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