# splinef

##### 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"`

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)

##### 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.

##### See Also

##### Examples

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

*Documentation reproduced from package forecast, version 4.05, License: GPL (>= 2)*