Enhancing cryptocurrency price forecasting: Performance evaluation of baseline versus Bayesian-optimized LSTM models

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Type of the article: Research Article

Abstract
Cryptocurrency markets are highly volatile, making price prediction a complex yet essential task for investors, financial engineers, and institutions. The purpose of this study is to evaluate whether Bayesian optimization of technical indicator parameters significantly improves the forecasting performance of Long Short-Term Memory (LSTM) models compared to baseline configurations. The study used daily Bitcoin and Ethereum price data from January 2016 to September 2025. Six technical indicators representing trend, momentum, volatility, and volume-based technical indicators are constructed and dynamically optimized through Bayesian optimization. The optimized indicators are then used as inputs to an LSTM forecasting framework. The study found that the baseline LSTM model achieved moderate predictive accuracy, where Ethereum outperformed Bitcoin. After optimization, both models exhibited improved performance, reducing the forecasting error for Bitcoin by 36.4% and for Ethereum by 12.2%. LSTM model with Bayesian optimized indicators showed a higher forecasting accuracy as compared to the baseline model, with 32% and 18.6% improvements for Bitcoin and Ethereum, respectively. These findings suggest that combining optimized technical indicators with LSTM models enhances predictive power in cryptocurrency markets. The approach offers a robust forecasting framework for traders, analysts, and algorithmic systems in high-volatility environments.

Acknowledgment
“This work was funded by the Deanship of Scientific Research, Vice Presidency for Graduate Studies and Scientific Research, King Faisal University, Saudi Arabia [Project No. KFU261690].”

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    • Figure 1. Boxplots for detecting outliers in key numeric features
    • Figure 2. Technical indicators trends for SMA and EMA for BTC and ETH
    • Figure 3. Technical indicators trends for Bollinger bBands and ATR for BTC
    • Figure 4. Technical indicators trends for Bollinger Bands and ATR for BTC
    • Figure 5. Baseline LSTM model predictions
    • Figure 6. Bayesian optimization convergence
    • Figure 7. Default vs. optimized parameters
    • Figure 8. Forecasting accuracy – Baseline vs. optimized LSTM models
    • Table 1. Descriptive statistics of the data
    • Table 2. Baseline LSTM model performance
    • Table 3. Hyperparameter space definition
    • Table 4. Best parameter configurations
    • Table 5. Forecasting accuracy – Baseline vs. optimized LSTM models
    • Conceptualization
      Abdulilah I. Mubarak
    • Data curation
      Abdulilah I. Mubarak
    • Formal Analysis
      Abdulilah I. Mubarak
    • Funding acquisition
      Abdulilah I. Mubarak
    • Investigation
      Abdulilah I. Mubarak
    • Methodology
      Abdulilah I. Mubarak
    • Project administration
      Abdulilah I. Mubarak
    • Resources
      Abdulilah I. Mubarak
    • Software
      Abdulilah I. Mubarak
    • Supervision
      Abdulilah I. Mubarak
    • Validation
      Abdulilah I. Mubarak
    • Visualization
      Abdulilah I. Mubarak
    • Writing – original draft
      Abdulilah I. Mubarak
    • Writing – review & editing
      Abdulilah I. Mubarak