# splinef

From forecast v7.2
by Rob Hyndman

##### Cubic Spline Forecast

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

- Keywords
- ts

##### Usage

`splinef(y, h=10, level=c(80,95), fan=FALSE, lambda=NULL, biasadj=FALSE, method=c("gcv","mle"), x=y)`

##### Arguments

- y
- 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(51,99,by=3). This is suitable for fan plots.
- lambda
- Box-Cox transformation parameter. Ignored if NULL. Otherwise, forecasts back-transformed via an inverse Box-Cox transformation.
- biasadj
- 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
- Method for selecting the smoothing parameter. If
`method="gcv"`

, the generalized cross-validation method from`smooth.spline`

is used. If`method="mle"`

, the maximum likelihood method from Hyndman et al (2002) is used. - x
- Deprecated. Included for backwards compatibility.

##### 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

`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:
containing the following elements:##### 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/.

##### See Also

##### Examples

```
plot(fcast)
summary(fcast)
```

*Documentation reproduced from package forecast, version 7.2, License: GPL (>= 2)*

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