What you'll learn

Forecasting time-based data is a common challenge across industries, but many models fall short because they fail to capture the underlying structure of time itself. Without the right preparation and feature engineering, important signals like trends, seasonality, and temporal relationships are lost.

In this course, Feature Engineering for Time Series Modeling, you'll gain the ability to transform raw time series data into datasets that drive more accurate forecasts.

First, you'll explore how to properly prepare time series data, including handling missing values, aligning timestamps, and reducing noise.

Next, you'll discover how to engineer time-aware features such as lag variables, rolling statistics, and calendar-based signals that capture temporal relationships.

Finally, you'll learn how to apply transformations and modeling strategies that improve forecasting performance while balancing accuracy, interpretability, and operational complexity.

When you're finished with this course, you'll have the skills and knowledge of feature engineering needed to build more accurate and reliable forecasting models.