isfe.fts: Integrated Squared Forecast Error for models of various...

In ftsa: Functional Time Series Analysis

Integrated Squared Forecast Error for models of various orders

Description

Computes integrated squared forecast error (ISFE) values for functional time series models of various orders.

Usage

isfe.fts(data, max.order = N - 3, N = 10, h = 5:10, method = 
 c("classical", "M", "rapca"), mean = TRUE, level = FALSE, 
  fmethod = c("arima", "ar", "ets", "ets.na", "struct", "rwdrift", 
   "rw", "arfima"), lambda = 3, ...)

Arguments

data	An object of class fts.
max.order	Maximum number of principal components to fit.
N	Minimum number of functional observations to be used in fitting a model.
h	Forecast horizons over which to average.
method	Method to use for principal components decomposition. Possibilities are “M”, “rapca” and “classical”.
mean	Indicates if mean term should be included.
level	Indicates if level term should be included.
fmethod	Method used for forecasting. Current possibilities are “ets”, “arima”, “ets.na”, “struct”, “rwdrift” and “rw”.
lambda	Tuning parameter for robustness when method = "M".
...	Additional arguments controlling the fitting procedure.

Value

Numeric matrix with (max.order+1) rows and length(h) columns containing ISFE values for models of orders 0:(max.order).

Note

This function can be very time consuming for data with large dimensionality or large sample size. By setting max.order small, computational speed can be dramatically increased.

Author(s)

Rob J Hyndman

References

R. J. Hyndman and M. S. Ullah (2007) "Robust forecasting of mortality and fertility rates: A functional data approach", Computational Statistics and Data Analysis, 51(10), 4942-4956.

See Also

ftsm, forecast.ftsm, plot.fm, plot.fmres, summary.fm, residuals.fm

ftsa documentation built on Sept. 11, 2023, 5:09 p.m.