DeepTime is a hybrid machine learning model developed for the specific task of forecasting market volatility in non-stationary time series data. It integrates several temporal modeling techniques, including dynamic feature selection, attention mechanisms, recurrent networks, convolutional layers, and ensemble learning. This combination allows DeepTime to capture evolving patterns across various time scales, providing a robust and reliable predictive framework that accommodates the dynamic and non-stationary nature of financial markets. Its architecture is designed for precise volatility forecasting and advanced time series analysis in the financial sector.