Time Series Components (Trend, Seasonality, Noise)

Summary

Any time series can be thought of as a mix of trend (long-term direction), seasonality (repeating patterns tied to a calendar), and noise (unexplained randomness) — decomposing these helps you model each part appropriately.

• 1Plot a real time series and visually identify trend and seasonal patterns.
• 2Use a decomposition function (e.g. in statsmodels) to separate these components programmatically.
• 3Learn to distinguish genuine seasonality from a one-off event that looks similar.