The volatile nature of cryptocurrency markets presents a significant challenge for traders and investors seeking reliable price forecasts. Recent advancements in artificial intelligence (AI) have led to the development of various predictive models aimed at improving accuracy in cryptocurrency price prediction. This study provides a comparative analysis of AI models used for cryptocurrency forecasting, including machine learning approaches such as Support Vector Machines (SVM), Random Forest (RF), and deep learning techniques like Long Short-Term Memory (LSTM) networks, Transformer-based models, and hybrid ensembles. The analysis evaluates each model's performance based on key metrics such as mean absolute error (MAE), root mean square error (RMSE), and directional accuracy. Additionally, factors influencing model efficacy, such as feature selection, data preprocessing, and market sentiment integration, are explored. Findings indicate that deep learning models, particularly LSTM and Transformer-based architectures, exhibit superior performance in capturing the non-linear dependencies and temporal patterns of cryptocurrency markets. However, hybrid models integrating multiple AI techniques show promise in enhancing prediction robustness. This research underscores the importance of model selection and data preprocessing in optimizing cryptocurrency price predictions and offers insights into future developments in AI-driven financial forecasting.
Forecast, Prediction, Artificial Intelligence, Investment, currency, Cryptography, Analysis, Security, Valuation, Strategy
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