# naive

From forecast v7.3
by Rob Hyndman

##### Naive and Random Walk Forecasts

`rwf()`

returns forecasts and prediction intervals for a random walk with drift model applied to `y`

. This is equivalent to an ARIMA(0,1,0) model with an optional drift coefficient. `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.

- Keywords
- ts

##### Usage

```
naive(y, h=10, level=c(80,95), fan=FALSE, lambda=NULL, biasadj=FALSE, x=y)
rwf(y, h=10, drift=FALSE, level=c(80,95), fan=FALSE, lambda=NULL, biasadj=FALSE,x=y)
snaive(y, h=2*frequency(x), level=c(80,95), fan=FALSE, lambda=NULL, biasadj=FALSE,x=y)
```

##### Arguments

- y
- a numeric vector or time series
- h
- Number of periods for forecasting
- drift
- Logical flag. If TRUE, fits a random walk with drift model.
- level
- Confidence levels for prediction intervals.
- fan
- If TRUE, level is set to seq(51,99,by=3). This is suitable for fan plots.
- lambda
- Box-Cox transformation parameter. Ignored if NULL. Otherwise, forecasts back-transformed via an inverse Box-Cox transformation.
- biasadj
- Use adjusted back-transformed mean for Box-Cox transformations. If TRUE, point forecasts and fitted values are mean forecast. Otherwise, these points can be considered the median of the forecast densities.
- x
- Deprecated. Included for backwards compatibility.

##### 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.

The seasonal naive model is $$Y_t= Y_{t-m} + Z_t$$ where $Z[t]$ is a normal iid error.

##### Value

`forecast`

".The function `summary`

is used to obtain and print a summary of the
results, while the function `plot`

produces a plot of the forecasts and prediction intervals.The generic accessor functions `fitted.values`

and `residuals`

extract useful features of
the value returned by `naive`

or `snaive`

.An object of class `"forecast"`

is a list containing at least the following elements:
is a list containing at least the following elements:##### See Also

##### Examples

```
gold.fcast <- rwf(gold[1:60], h=50)
plot(gold.fcast)
plot(naive(gold,h=50),include=200)
plot(snaive(wineind))
```

*Documentation reproduced from package forecast, version 7.3, License: GPL (>= 2)*

### Community examples

Looks like there are no examples yet.