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.
rwf( y, h = 10, drift = FALSE, level = c(80, 95), fan = FALSE, lambda = NULL, biasadj = FALSE, ..., x = y ) naive( y, h = 10, 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 )
y  a numeric vector or time series of class 

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  BoxCox transformation parameter. If 
biasadj  Use adjusted backtransformed mean for BoxCox transformations. If transformed data is used to produce forecasts and fitted values, a regular back transformation will result in median forecasts. If biasadj is TRUE, an adjustment will be made to produce mean forecasts and fitted values. 
...  Additional arguments affecting the forecasts produced. If

x  Deprecated. Included for backwards compatibility. 
An object of class "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:
A list containing information about the fitted model
The name of the forecasting method as a character string
Point forecasts as a time series
Lower limits for prediction intervals
Upper limits for prediction intervals
The confidence values associated with the prediction intervals
The original time series
(either object
itself or the time series used to create the model
stored as object
).
Residuals from the fitted model. That is x minus fitted values.
Fitted values (onestep forecasts)
The random walk with drift model is $$Y_t=c + Y_{t1} + Z_t$$ 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 $$Y_t= Y_{tm} + Z_t$$ where \(Z_t\) is a normal iid error.
Rob J Hyndman