Imagine you’re tracking your monthly electricity bill. The total amount you pay is like a recipe: part of it is a slow long-term change (maybe prices rising), part is a repeating pattern (higher in winter, lower in summer), and part is random ups and downs.

Additive decomposition is a way of saying: “Let’s see this total as the sum of these pieces.” In time series data (numbers recorded over time), we break it into:

- a trend (overall direction: up, down, or flat)
- a seasonal pattern (regular repeats, like months or days)
- a random part (noise or surprises)

All added together, they reconstruct the original data.

Notes