Fit a linear model with time series components

The model formula will be handled using stats::model.matrix(), and so the the same approach to include interactions in stats::lm() applies when specifying the formula. In addition to stats::lm(), it is possible to include common_xregs in the model formula, such as trend(), season(), and fourier().

TSLM(formula)

Arguments

formula	Model specification.

Value

A model specification.

Specials

xreg

Exogenous regressors can be included in an ARIMA model without explicitly using the xreg() special. Common exogenous regressor specials as specified in common_xregs can also be used. These regressors are handled using stats::model.frame(), and so interactions and other functionality behaves similarly to stats::lm().

xreg(...)

...	Bare expressions for the exogenous regressors (such as log(x))

See also

stats::lm(), stats::model.matrix() Forecasting: Principles and Practices, Time series regression models (chapter 6)

Examples

as_tsibble(USAccDeaths) %>%
  model(lm = TSLM(log(value) ~ trend() + season()))
#> # A mable: 1 x 1
#>        lm
#>   <model>
#> 1  <TSLM>

library(tsibbledata)
olympic_running %>%
  model(TSLM(Time ~ trend())) %>%
  interpolate(olympic_running)
#> # A tsibble: 312 x 4 [4Y]
#> # Key:       Length, Sex [14]
#>    Length Sex    Year  Time
#>     <int> <chr> <int> <dbl>
#>  1    100 men    1896  12  
#>  2    100 men    1900  11  
#>  3    100 men    1904  11  
#>  4    100 men    1908  10.8
#>  5    100 men    1912  10.8
#>  6    100 men    1916  10.8
#>  7    100 men    1920  10.8
#>  8    100 men    1924  10.6
#>  9    100 men    1928  10.8
#> 10    100 men    1932  10.3
#> # … with 302 more rows