Example Concept: Autocorrelation analysis helps identify lagged variables that are significant predictors by examining the correlation of the time series with its past values. Tree-based models like Random Forests or Gradient Boosting can provide feature importance scores, highlighting which features most influence predictions. Recursive Feature Elimination (RFE) iteratively removes less important features to improve model performance, often used with cross-validation to ensure robustness.