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, ...)
- an object of class "formula" (or one that can be coerced to that class): a symbolic description of the model to be fitted.
- 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.
- an optional subset containing rows of data to keep. For best results, pass a logical vector of rows to keep. Also supports
- Box-Cox transformation parameter. Ignored if NULL. Otherwise, data are transformed via a Box-Cox transformation.
- 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
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).
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))