Time Series Fundamentals

#Learning Objectives

• Decompose a time series into trend, seasonality, and residual components and test for stationarity
• Implement lag features and rolling statistics to convert a time series into a supervised learning problem
• Build a walk-forward validation framework that prevents future data from leaking into training
• Explain why random train/test splits are invalid for time series and demonstrate the performance gap versus proper temporal splits