The objective of time series decomposition is to model the long-term trend and seasonality and estimate the overall time series as a combination of them. Two popular models for time series decomposition are:
- Additive model
- Multiplicative model
The additive model formulates the original time series (xt) as the sum of the trend cycle (Ft) and seasonal (St) components as follows:
xt = Ft + St + Єt
The residuals Єt obtained after adjusting the trend and seasonal components are the irregular variations. The additive model is usually applied when there is a time-dependent trend cycle component, but independent seasonality that does not change over time.
The multiplicative decomposition model, which gives the time series ...
