Feature Engineering in Time Series Forecasting

Feature engineering is a crucial element in Time Series Forecasting.Features can be designed to capture how patterns, trends, and seasonalityevolve over time. The following sections introduce practical techniquesinspired by calendar information and Fourier terms: lag features, rollingand seasonal window aggregations, exponentially weighted moving averages,and temporal embeddings. It also shows common issues arising,such as data leakage, and clarifies why well-defined forecast horizons areso important. Finally, an experimental illustration using German electricityload data demonstrates the importance of Feature Engineering inforecasting performance on real-world time series. The aim is to give anintuitive and accessible overview that helps practitioners turn time seriesinto richer, more informative inputs for their forecasting models.