Fit a linear model with time series components
tslm is used to fit linear models to time series including trend and
seasonality components.
tslm(formula, data, subset, lambda = NULL, biasadj = FALSE, ...)
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
|
lambda |
Box-Cox transformation parameter. If |
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 |
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).
Returns an object of class "lm".
Mitchell O'Hara-Wild and Rob J Hyndman
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))
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