The goal of time series decomposition is to increase our understanding of the data by breaking down the series into multiple components. It provides insight in terms of modeling complexity and which approaches to follow in order to accurately capture each of the components.
These components can be divided into two types: systematic and non-systematic. The systematic ones are characterized by consistency and the fact that they can be described and modeled. By contrast, the non-systematic ones cannot be modeled directly.
The following are the systematic components:
- level: The mean value in the series.
- trend: An estimate of the trend, that is, the change in value between successive time points at any given moment. It can be associated with the slope (increasing/decreasing) of the series.
- seasonality: Deviations from the mean caused by repeating short-term...
