ftsa: isfe.fts – R documentation

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ftsa

isfe.fts

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.