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.
Usage
RW(data, formula = ~lag(1))
NAIVE(data, formula = ~lag(1))
SNAIVE(data, formula = ~lag("smallest"))
Arguments
data
A data frame
formula
Model specification.
Details
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.