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)
formula | Model specification. |
---|
A model specification.
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) ) |
stats::lm()
, stats::model.matrix()
Forecasting: Principles and Practices, Time series regression models (chapter 6)
#> # 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