Lag Feature

Lag features help bring past information into the model, especially in time series and sequential event data. Examples include the previous day’s sales, the average of the last three transactions, or last week’s traffic volume. This allows the system to capture patterns that depend on history. However, lag feature generation requires careful handling of time alignment and leakage risk. In time-aware problems, lag features often produce highly valuable signals.