Decomposition of time series

This is an important technique for all types of time series analysis, especially for seasonal adjustment.

Kendall shows an example of a decomposition into smooth, seasonal and irregular factors for a set of data containing values of the monthly aircraft miles flown by UK airlines.

[6] In policy analysis, forecasting future production of biofuels is key data for making better decisions, and statistical time series models have recently been developed to forecast renewable energy sources, and a multiplicative decomposition method was designed to forecast future production of biohydrogen.

[5] An example of statistical software for this type of decomposition is the program BV4.1 that is based on the Berlin procedure.

The R statistical software also includes many packages for time series decomposition, such as seasonal,[7] stl, stlplus,[8] and bfast.