R: Cubic Spline Forecast

splinef {forecast}

R Documentation

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)

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.

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.

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 meanf.

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)

Author(s)

Rob J Hyndman

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/.

Examples

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

[Package forecast version 3.24 Index]