"Time Series Stacking"

Time Series Stacking is an advanced forecasting technique that combines predictions from multiple models to improve accuracy and robustness. It involves training various base models on historical data, each capturing different patterns and relationships. These models' predictions are then used as inputs for a meta-model, which learns to optimally combine them for the final forecast. This approach leverages the strengths of different models, providing better performance than any single model alone, and is flexible enough to handle diverse types of time series data.