Machine Learning-Based Forecasting of AAVE Cryptocurrency: A Comparative Study of Regression, Ensemble, and Deep Learning Models

The volatility of cryptocurrency markets has increased the demand for accurate forecasting models that can help investors and analysts anticipate price movements. This study evaluates the predictive performance of four machine learning algorithms, namely Linear Regression, Random Forest, XGBoost, and Long Short-Term Memory (LSTM), in forecasting the closing price of the AAVE cryptocurrency. The models were trained using historical market data consisting of key indicators such as Open, High, Low, Volume, and Marketcap. Their performance was assessed using Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and the Coefficient of Determination (R²). The results show that Linear Regression produced the most accurate predictions with the lowest MAE (8.13), RMSE (8.76), and the highest R² (0.9924). Random Forest and XGBoost also achieved good results with R² values of 0.9337 and 0.9484, respectively, while the LSTM model performed poorly with an R² of 0.4328. The study concludes that simpler models can outperform more complex algorithms when the dataset is limited and exhibits linear behavior. The findings emphasize that model selection in cryptocurrency forecasting should consider data structure and quantity. Future work should involve larger datasets, higher-frequency data, and hybrid models that integrate ensemble learning and deep learning for improved predictive accuracy.