Decompose a multiple seasonal time series into seasonal, trend and remainder
components. Seasonal components are estimated iteratively using STL. The trend
component is computed for the last iteration of STL. Non-seasonal time series
are decomposed into trend and remainder only. In this case, `supsmu`

is used to estimate the trend.
Optionally, the time series may be Box-Cox transformed before decomposition.

mstl(x, lambda = NULL, iterate = 2, s.window = 21, ...)

## Arguments

x |
Univariate time series of class `msts` or `ts` . |

lambda |
Box-Cox decomposition parameter. If `NULL` , no transformation
is used. If `lambda="auto"` , a transformation is automatically selected. If
lambda takes a numerical value, it is used as the parameter of the Box-Cox transformation. |

iterate |
Number of iterations to use to refine the seasonal component. |

s.window |
Seasonal windows to be used in the decompositions. If scalar,
the same value is used for all seasonal components. Otherwise, it should be a vector
of the same length as the number of seasonal components. |

... |
Other arguments are passed to `stl` . |

## See also

`stl`

, `link[stats]{supsmu}`

## Examples