In this tutorial, we will explore a range of feature engineering techniques for time series forecasting using popular machine learning algorithms such as XGBoost, LightGBM, and CatBoost. We'll begin by transforming time series data into a tabular format and demonstrate how to create window and lag features, as well as features that capture seasonality and trends.

We'll cover best practices for encoding categorical variables, decomposing time series, identifying outliers, and avoiding common pitfalls such as data leakage and look-ahead bias. Additionally, we’ll touch on more advanced topics like intermittency and hierarchical forecasting.

The session will also delve into cross-validation methods - specifically backtesting methods suited for time series data. We'll examine why traditional K-fold cross-validation is inappropriate for time-dependent datasets and highlight alternative approaches along with their trade-offs.

Finally, we’ll review best practices for evaluating model performance. This includes a comprehensive overview of error metrics, discussing their strengths, weaknesses, and the contexts in which each should be used.