Lesson 4: Breaking Down Time Series into Trend, Seasonality, Cycle & Random Noise
This tutorial explains the four fundamental components of a time series—trend, seasonality, cyclicality, and random noise—covers additive and multiplicative decomposition models, provides step‑by‑step Python code with visualizations, and shows how removing these components improves stationarity for forecasting.
