  The decomposition of a time series is an essential task that helps to
understand its very nature. It facilitates the analysis and forecasting of
complex time series expressing various hidden components such as the trend,
seasonal components, cyclic components and irregular fluctuations. Therefore,
it is crucial in many fields for forecasting and decision processes. In recent
years, many methods of time series decomposition have been developed, which
extract and reveal different time series properties. Unfortunately, they
neglect a very important property, i.e. time series variance. To deal with
heteroscedasticity in time series, the method proposed in this work -- a
seasonal-trend-dispersion decomposition (STD) -- extracts the trend, seasonal
component and component related to the dispersion of the time series. We define
STD decomposition in two ways: with and without an irregular component. We show
how STD can be used for time series analysis and forecasting.
