  We propose a new method for decomposing seasonal data: STR (a Seasonal-Trend
decomposition using Regression). Unlike other decomposition methods, STR allows
for multiple seasonal and cyclic components, covariates, seasonal patterns that
may have non-integer periods, and seasonality with complex topology. It can be
used for time series with any regular time index including hourly, daily,
weekly, monthly or quarterly data. It is competitive with existing methods when
they exist, but tackles many more decomposition problem than other methods
allow.
  STR is based on a regularized optimization, and so is somewhat related to
ridge regression. Because it is based on a statistical model, we can easily
compute confidence intervals for components, something that is not possible
with most existing decomposition methods (such as STL, X-12-ARIMA, SEATS-TRAMO,
etc.).
  Our model is implemented in the R package stR, so can be applied by anyone to
their own data.
