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 of class
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
Deprecated. Included for backwards compatibility.
An object of class "
summary is used to obtain and print a summary of the
results, while the function
plot produces a plot of the forecasts and
The generic accessor functions
extract useful features of the value returned by
An object of class
"forecast" containing the following elements:
A list containing information about the fitted model
The name of the forecasting method as a character string
Point forecasts as a time series
Lower limits for prediction intervals
Upper limits for prediction intervals
The confidence values associated with the prediction intervals
The original time series (either
object itself or the time
series used to create the model stored as
One-step forecasts from the fitted model.
Smooth estimates of the fitted trend using all data.
Residuals from the fitted model. That is x minus one-step forecasts.
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.
Hyndman, King, Pitrun and Billah (2005) Local linear forecasts using cubic smoothing splines. Australian and New Zealand Journal of Statistics, 47(1), 87-99. https://robjhyndman.com/publications/splinefcast/.
fcast <- splinef(uspop,h=5) plot(fcast)summary(fcast)#> #> Forecast method: Cubic Smoothing Spline #> #> Model Information: #> $beta #>  9.458893e-13 #> #> $call #> splinef(y = uspop, h = 5) #> #> #> Error measures: #> ME RMSE MAE MPE MAPE MASE #> Training set 0.7288723 4.643268 3.247273 -1.379552 9.580963 0.04654289 #> ACF1 #> Training set -0.4369553 #> #> Forecasts: #> Point Forecast Lo 80 Hi 80 Lo 95 Hi 95 #> 1980 225.6523 219.6912 231.6133 216.5356 234.7689 #> 1990 248.1045 233.3293 262.8798 225.5077 270.7013 #> 2000 270.5568 244.8162 296.2974 231.1900 309.9236 #> 2010 293.0091 254.5045 331.5136 234.1214 351.8967 #> 2020 315.4613 262.6160 368.3067 234.6413 396.2814