Data Decomposition

What is Data Decomposition?

In time series analysis, data decomposition splits a series into interpretable components—typically level, trend, seasonality, and residual noise—so forecasters can model structure instead of treating the raw signal as a black box.

Additive and multiplicative models cover different seasonality patterns. Decomposition informs forecasting method choice and residual diagnostics.

Authoritative reference: Forecasting: Principles and Practice — Decomposition