tslm: Fit a linear model with time series components

In ttnsdcn/forecast-package: Forecasting functions for time series

Description

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

Usage

tslm(formula, data, lambda=NULL, ...)

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.
lambda	Box-Cox transformation parameter. Ignored if NULL. Otherwise, data are transformed via a Box-Cox transformation.
...	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

Returns an object of class "lm".

Author(s)

Rob J Hyndman

See Also

forecast.lm, lm.

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))

ttnsdcn/forecast-package documentation built on June 1, 2019, 2:49 a.m.