`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, ...)

## Arguments

formula |
Model specification (see "Specials" section). |

... |
Not used. |

## 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.

## Specials

### lag

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"). |

### drift

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. |

## Examples

#> # A mable: 1 x 1
#> rw
#> <model>
#> 1 <RW w/ drift>

#> Model not specified, defaulting to automatic modelling of the `value` variable. Override this using the model formula.

#> # A mable: 1 x 1
#> `NAIVE()`
#> <model>
#> 1 <NAIVE>

#> # A mable: 1 x 1
#> snaive
#> <model>
#> 1 <SNAIVE>