Time series decomposition
One of the goals 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/model each of the components.
An example can shed more light on the possibilities. We can imagine a time series with a clear trend, either increasing or decreasing. On one hand, we could use the decomposition to extract the trend component and remove it from our time series before modeling the remaining series. Then, we can always add it back after the rest of the components are accounted for. On the other hand, we could provide enough data or adequate features for our algorithm to model the trend itself.
The components of time series 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...
