Example Concept: In time series feature engineering, one common pitfall is not accounting for the temporal order of data. Unlike static datasets, time series data is sequential, and features must be engineered to respect this order. For instance, using future data to predict past events can lead to data leakage. Another issue is not addressing seasonality and trends, which can skew model predictions if not properly managed. Additionally, failing to handle missing values or outliers can result in inaccurate models.