forecast (version 5.3)

splinef: Cubic Spline Forecast

Description

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

Usage

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

Arguments

x

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(50,99,by=1). This is suitable for fan plots.

lambda

Box-Cox transformation parameter. Ignored if NULL. Otherwise, forecasts back-transformed via an inverse Box-Cox transformation.

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 likelihoo

Value

• 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 splinef.

  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 object itself or the time series used to create the model stored as object).
• residuals
  Residuals from the fitted model. That is x minus fitted values.
• fitted
  Fitted values (one-step forecasts)

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

fcast <- splinef(uspop,h=5)
plot(fcast)
summary(fcast)