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.
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)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: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.
Arimagold.fcast <- rwf(gold[1:60], h=50)
plot(gold.fcast)
plot(naive(gold,h=50),include=200)
plot(snaive(wineind))
Run the code above in your browser using DataCamp Workspace