Time-series feature engineering for XGBoost, 101:
• Lag features
• Rolling aggregations (usually std, var)
• Differences between lags/rolling features
• Group-level aggregations
• Rolling group-level aggregations
• Calendar features (dayofweek, month, etc)
• Rolling features by dayofweek, month, etc.
• Rolling features by group and calendar
• Expanding window features
• Expanding window by group and calendar
• Normalized features (i.e. % of greatest observed value)
• Predictions from stats models, like ETS
• Any external variables, like price
And many, many more (depending on your data)