Improving future predictions

While the basic iterative forecasting strategy provides a straightforward way to predict future time steps, there are ways to improve the performance and robustness of the model. First, you can do some feature engineering for the future steps. You can include exogenous variables, such as external factors (e.g., weather, economic indicators), in your time series model to improve the accuracy of future predictions. Your model may also benefit from incorporating more complex seasonal patterns (e.g., daily, monthly) as additional features to help the model capture long-term trends.

Another way to improve future predictions is to use ensemble modeling. Instead of relying on a single model, you can create an ensemble of multiple XGBoost models, each trained with different sets of features or hyperparameters. Averaging the predictions from different models can often yield better performance.

Lastly, you can apply hyperparameter tuning. Fine-tuning the model...