AI-Driven Crypto Time Series Forecasting

High volatility and complexity are usually characteristic of cryptocurrency markets, making the process of price prediction very challenging.Most traditional methods for time series forecasting have been widely applied in predicting financial time series; however, they usually fail in representing the intricate patterns and dynamics inherited in the prices of cryptocurrencies.The paper proposes a new approach by incorporating machine learning techniques into time series forecasting models in order to improve the accuracy of cryptocurrency price predictions.The synergy of state-of-the-art classical timeseries models like ARIMA and SARIMA with advanced machine learning algorithms like Long Short-Term Memory networks and Gradient Boosting Machines.In our approach, the procedureis twofold: time series modeling captures the temporal dependencies, while machine learning models capture the nonlinear relations and interactions.We perform an empirical evaluation on a rich dataset of historical prices of cryptocurrencies and relevant features about market conditions.Our findings prove the fact that the integration of machine learning with traditional timeseries forecasting enhances prediction performance significantly.Key metrics, for instance, MAE and RMSE show drastic improvements compared to baseline models.It is not only going to provide a more accurate forecast framework but also give an idea of how classical and modern combined approaches possess predictive power.