splinef

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Cubic Spline Forecast

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

Keywords
ts
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
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 splinef.

An object of class "forecast" containing the following elements:

• modelA list containing information about the fitted model
• methodThe name of the forecasting method as a character string
• meanPoint forecasts as a time series
• lowerLower limits for prediction intervals
• upperUpper limits for prediction intervals
• levelThe confidence values associated with the prediction intervals
• xThe original time series (either object itself or the time series used to create the model stored as object).
• onestepfOne-step forecasts from the fitted model.
• fittedSmooth estimates of the fitted trend using all data.
• residualsResiduals from the fitted model. That is x minus one-step forecasts.

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/.

smooth.spline, arima, holt.

• splinef
Examples
fcast <- splinef(uspop,h=5)
plot(fcast)
summary(fcast)
Documentation reproduced from package forecast, version 6.0, License: GPL (>= 2)

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