A Model for Predicting Cryptocurrency Prices Using Meta-Synthesis Methods - Jahed, MohammadDaniyal; Fathi, Zadollah; Zomorrodian, Gholamreza (Business, Marketing, and Finance Open, 2025)
Abstract: The aim of this study was to predict Bitcoin prices by employing machine learning algorithms in combination with the HHO (Harris Hawks Optimization) and AO (Aquila Optimizer) optimization algorithms. In this study, the most influential variables affecting Bitcoin price were first identified using the meta-synthesis method. Subsequently, the proposed model, which integrates CNN and LSTM architectures, was designed and implemented to analyze complex patterns and temporal dependencies. To enhance prediction accuracy and optimize parameter tuning, the Harris Hawks Optimization (HHO) and Aquila Optimization (AO) algorithms were employed. The obtained results demonstrate that the proposed hybrid model outperforms previous methods in terms of prediction accuracy (based on indicators such as RMSE and MAPE) and adaptability to market volatility. In the present study, the proposed hybrid model exhibited superior performance compared to prior methods in terms of prediction accuracy (measured by metrics such as RMSE and MAPE) and flexibility in coping with market fluctuations. This model can assist investors, market analysts, and financial policymakers in risk management, informed decision-making, and optimizing investment strategies.