forecast (version 7.3)

splinef: Cubic Spline Forecast

Description

Returns local linear forecasts and prediction intervals using cubic smoothing splines.

Usage

splinef(y, h=10, level=c(80,95), fan=FALSE, lambda=NULL, biasadj=FALSE, method=c("gcv","mle"), x=y)

Arguments

y

a numeric vector or time series

h

Number of periods for forecasting

level

Confidence level 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.

method

Method for selecting the smoothing parameter. If method="gcv", the generalized cross-validation method from smooth.spline is used. If method="mle", the maximum likelihood method from Hyndman et al (2002) is used.

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 splinef.An object of class "forecast" containing the following elements: containing the following elements:

Details

The cubic smoothing spline model is equivalent to an ARIMA(0,2,2) model but with a restricted parameter space. The advantage of the spline model over the full ARIMA model is that it provides a smooth historical trend as well as a linear forecast function. Hyndman, King, Pitrun, and Billah (2002) show that the forecast performance of the method is hardly affected by the restricted parameter space.

References

Hyndman, King, Pitrun and Billah (2005) Local linear forecasts using cubic smoothing splines. Australian and New Zealand Journal of Statistics, 47(1), 87-99. http://robjhyndman.com/papers/splinefcast/.

See Also

smooth.spline, arima, holt.

Examples

plot(fcast)
summary(fcast)