spline_model().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.
# S3 method for spline_model
forecast(
object,
h = 10,
level = c(80, 95),
fan = FALSE,
lambda = object$lambda,
biasadj = attr(lambda, "biasadj"),
simulate = FALSE,
bootstrap = FALSE,
innov = NULL,
npaths = 5000,
...
)splinef(
y,
h = 10,
level = c(80, 95),
fan = FALSE,
lambda = NULL,
biasadj = FALSE,
method = c("gcv", "mle"),
x = y
)
An object of class forecast.
An object of class spline_model, produced using spline_model().
Number of periods for forecasting. Default value is twice the largest seasonal period (for seasonal data) or ten (for non-seasonal data).
Confidence levels for prediction intervals.
If TRUE, level is set to seq(51, 99, by = 3).
This is suitable for fan plots.
Box-Cox transformation parameter. If lambda = "auto",
then a transformation is automatically selected using BoxCox.lambda.
The transformation is ignored if NULL. Otherwise,
data transformed before model is estimated.
Use adjusted back-transformed mean for Box-Cox
transformations. If transformed data is used to produce forecasts and fitted
values, a regular back transformation will result in median forecasts. If
biasadj is TRUE, an adjustment will be made to produce mean forecasts
and fitted values.
If TRUE, prediction intervals are produced by simulation rather
than using analytic formulae. Errors are assumed to be normally distributed.
If TRUE, then prediction intervals are produced by
simulation using resampled errors (rather than normally distributed errors). Ignored if innov is not NULL.
Optional matrix of future innovations to be used in
simulations. Ignored if simulate = FALSE. If provided, this overrides the bootstrap argument. The matrix
should have h rows and npaths columns.
Number of sample paths used in computing simulated prediction intervals.
Other arguments are ignored.
a numeric vector or univariate time series of class ts
fitting method: maximum likelihood or minimize conditional sum-of-squares. The default (unless there are missing values) is to use conditional-sum-of-squares to find starting values, then maximum likelihood. Can be abbreviated.
Deprecated. Included for backwards compatibility.
An object of class forecast is a list usually containing at least
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.
Residuals from the fitted model. For models with additive errors, the residuals will be x minus the fitted values.
Fitted values (one-step forecasts)
The function summary can be used to obtain and print a summary of the
results, while the functions plot and autoplot produce plots of the forecasts and
prediction intervals. The generic accessors functions fitted.values and residuals
extract various useful features from the underlying model.
Rob J Hyndman
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/.
spline_model()
fit <- spline_model(uspop)
fcast <- forecast(fit)
autoplot(fcast)
summary(fcast)
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