Cubic Spline Forecast
Returns local linear forecasts and prediction intervals using cubic smoothing splines.
splinef(x, h=10, level=c(80,95), fan=FALSE, lambda=NULL, method=c("gcv","mle"))
- a numeric vector or time series
- Number of periods for forecasting
- Confidence level for prediction intervals.
- If TRUE, level is set to seq(50,99,by=1). This is suitable for fan plots.
- Box-Cox transformation parameter. Ignored if NULL. Otherwise, forecasts back-transformed via an inverse Box-Cox transformation.
- Method for selecting the smoothing parameter. If
method="gcv", the generalized cross-validation method from
smooth.splineis used. If
method="mle", the maximum likelihoo
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.
- An object of class "
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
An object of class
"forecast"containing 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
objectitself or the time series used to create the model stored as
onestepf One-step forecasts from the fitted model. fitted Smooth estimates of the fitted trend using all data. residuals Residuals from the fitted model. That is x minus one-step forecasts.
Hyndman, King, Pitrun and Billah (2005) Local linear forecasts using cubic smoothing
splines. Australian and New Zealand Journal of Statistics, 47(1), 87-99.
fcast <- splinef(uspop,h=5) plot(fcast) summary(fcast)