tslm: Fit a linear model with time series components

Description Usage Arguments Details Value Author(s) See Also Examples

View source: R/lm.R

Description

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

Usage

1
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()

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)

Mitchell O'Hara-Wild and Rob J Hyndman

See Also

forecast.lm, lm.

Examples

1
2
3
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))

robjhyndman/forecast documentation built on Dec. 6, 2018, 8:54 a.m.