Summary

Forecasting future values in time series with XGBoost involves more than just making a single prediction. By iteratively updating the lagged features and using the model to predict multiple future steps, you can extend the forecasting horizon and generate valuable insights for future time periods. However, this process also introduces challenges, such as error propagation, which can be mitigated with better feature engineering and model optimization.

You have now learned about using XGBoost to forecast time series data. You prepared the data by creating lag features and adding date-based features, such as the day of the week, as well as rolling statistics, such as moving averages or rolling means. Then, you learned how to properly split time series data to not lose the inherent ordering. You wrapped up the chapter by predicting values, evaluating the prediction, and forecasting future values. By following these steps and continuously refining the model, XGBoost becomes a...