`tslm`

is used to fit linear models to time series including trend and
seasonality components.

tslm(formula, data, subset, lambda = NULL, biasadj = FALSE, ...)

## Arguments

formula |
an object of class "formula" (or one that can be coerced to
that class): a symbolic description of the model to be fitted. |

data |
an optional data frame, list or environment (or object coercible
by as.data.frame to a data frame) containing the variables in the model. If
not found in data, the variables are taken from environment(formula),
typically the environment from which lm is called. |

subset |
an optional subset containing rows of data to keep. For best
results, pass a logical vector of rows to keep. Also supports
`subset()` functions. |

lambda |
Box-Cox transformation parameter. If `lambda="auto"` ,
then a transformation is automatically selected using `BoxCox.lambda` .
The transformation is ignored if NULL. Otherwise,
data transformed before model is estimated. |

biasadj |
Use adjusted back-transformed mean for Box-Cox
transformations. If transformed data is used to produce forecasts and fitted values,
a regular back transformation will result in median forecasts. If biasadj is TRUE,
an adjustment will be made to produce mean forecasts and fitted values. |

... |
Other arguments passed to `lm()` |

## Value

Returns an object of class "lm".

## Details

`tslm`

is largely a wrapper for `lm()`

except that
it allows variables "trend" and "season" which are created on the fly from
the time series characteristics of the data. The variable "trend" is a
simple time trend and "season" is a factor indicating the season (e.g., the
month or the quarter depending on the frequency of the data).

## See also

## Author

Mitchell O'Hara-Wild and Rob J Hyndman

## Examples