RW()
returns a random walk model, which is equivalent to an ARIMA(0,1,0)
model with an optional drift coefficient included using drift()
. naive()
is simply a wrapper
to rwf()
for simplicity. snaive()
returns forecasts and
prediction intervals from an ARIMA(0,0,0)(0,1,0)m model where m is the
seasonal period.
RW(formula, ...) NAIVE(formula, ...) SNAIVE(formula, ...)
formula | Model specification (see "Specials" section). |
---|---|
... | Not used. |
A model specification.
The random walk with drift model is $$Y_t=c + Y_{t-1} + Z_t$$ where \(Z_t\) is a normal iid error. Forecasts are
given by $$Y_n(h)=ch+Y_n$$. If there is no drift (as in
naive
), the drift parameter c=0. Forecast standard errors allow for
uncertainty in estimating the drift parameter (unlike the corresponding
forecasts obtained by fitting an ARIMA model directly).
The seasonal naive model is $$Y_t= Y_{t-m} + Z_t$$ where \(Z_t\) is a normal iid error.
The lag
special is used to specify the lag order for the random walk process.
If left out, this special will automatically be included.
lag(lag = NULL)
lag | The lag order for the random walk process. If lag = m , forecasts will return the observation from m time periods ago. This can also be provided as text indicating the duration of the lag window (for example, annual seasonal lags would be "1 year"). |
The drift
special can be used to include a drift/trend component into the model. By default, drift is not included unless drift()
is included in the formula.
drift(drift = TRUE)
drift | If drift = TRUE , a drift term will be included in the model. |
#> # A mable: 1 x 1 #> rw #> <model> #> 1 <RW w/ drift>#> # A mable: 1 x 1 #> `NAIVE(value)` #> <model> #> 1 <NAIVE>#> # A mable: 1 x 1 #> snaive #> <model> #> 1 <SNAIVE>