Decomposition method tries to separate a time series data into several components. Decomposition method is often used not only in yielding forecast, but also in yielding information about time series component i.e. trend, cycle, seasonal, and irregular component. There are two relation types among those components, they are multiplicative and additive. Multiplicative type assumes if data value grows up then seasonal pattern will grow up too. While additive type assumes that data value resides in a constant wide at the middle of trend.

In the decomposition method, every cycle of data is assumed to be part of a trend. The decomposition method equations :

Multiplicative type:

Additive type:

The Seasonal Index value is calculated by using a ratio to moving average method.