Cubic Spline Forecast
Returns local linear forecasts and prediction intervals using cubic smoothing splines.
splinef(y, h=10, level=c(80,95), fan=FALSE, lambda=NULL, biasadj=FALSE, method=c("gcv","mle"), x=y)
- a numeric vector or time series
- Number of periods for forecasting
- Confidence level for prediction intervals.
- If TRUE, level is set to seq(51,99,by=3). This is suitable for fan plots.
- Box-Cox transformation parameter. Ignored if NULL. Otherwise, forecasts back-transformed via an inverse Box-Cox transformation.
- 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 for selecting the smoothing parameter. If
method="gcv", the generalized cross-validation method from
smooth.splineis used. If
method="mle", the maximum likelihood method from Hyndman et al (2002) is used.
- Deprecated. Included for backwards compatibility.
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.
summaryis used to obtain and print a summary of the results, while the function
plotproduces a plot of the forecasts and prediction intervals.The generic accessor functions
residualsextract useful features of the value returned by
splinef.An object of class
"forecast"containing the following elements: containing the following elements:
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/.