# tslm

From forecast v7.3
by Rob Hyndman

##### Fit a linear model with time series components

`tslm`

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

- Keywords
- stats

##### Usage

`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. Ignored if NULL. Otherwise, data are transformed via a Box-Cox transformation.
- biasadj
- Use adjusted back-transformed mean for Box-Cox transformations. If TRUE, point forecasts and fitted values are mean forecast. Otherwise, these points can be considered the median of the forecast densities.
- ...
- Other arguments passed to
`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).

##### Value

##### See Also

##### Examples

```
y <- ts(rnorm(120,0,3) + 1:120 + 20*sin(2*pi*(1:120)/12), frequency=12)
fit <- tslm(y ~ trend + season)
plot(forecast(fit, h=20))
```

*Documentation reproduced from package forecast, version 7.3, License: GPL (>= 2)*

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