forecast (version 7.3)

naive: Naive and Random Walk Forecasts

Description

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.

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.

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:

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.

See Also

Arima

Examples

gold.fcast <- rwf(gold[1:60], h=50)
plot(gold.fcast)

plot(naive(gold,h=50),include=200)
plot(snaive(wineind))