Description Usage Arguments Details Value Author(s) See Also Examples
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.
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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. |
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.
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:
model |
A list containing information about the fitted model |
method |
The name of the forecasting method as a character string |
mean |
Point forecasts as a time series |
lower |
Lower limits for prediction intervals |
upper |
Upper limits for prediction intervals |
level |
The confidence values associated with the prediction intervals |
x |
The original time series (either |
residuals |
Residuals from the fitted model. That is x minus fitted values. |
fitted |
Fitted values (one-step forecasts) |
Rob J Hyndman
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