Integrated Squared Forecast Error for models of various orders

Computes ISFE values for functional time series models of various orders.

Usage

isfe(...)

# S3 method for class 'demogdata'
isfe(
  data,
  series = names(data$rate)[1],
  max.order = N - 3,
  N = 10,
  h = 5:10,
  ages = data$age,
  max.age = max(ages),
  method = c("classical", "M", "rapca"),
  fmethod = c("arima", "ar", "arfima", "ets", "ets.na", "struct", "rwdrift", "rw"),
  lambda = 3,
  ...
)

Arguments

...: Additional arguments control the fitting procedure.
data: demogdata object.
series: name of series within data holding rates (1x1)
max.order: Maximum number of basis functions to fit.
N: Minimum number of functional observations to be used in fitting a model.
h: Forecast horizons over which to average.
ages: Ages to include in fit.
max.age: Maximum age to fit.
method: Method to use for principal components decomposition. Possibilities are “M”, “rapca” and “classical”.
fmethod: Method used for forecasting. Current possibilities are “ets”, “arima”, “ets.na”, “struct”, “rwdrift” and “rw”.
lambda: Tuning parameter for robustness when method="M".

Value

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

References

Hyndman, R.J., and Ullah, S. (2007) Robust forecasting of mortality and fertility rates: a functional data approach. Computational Statistics & Data Analysis, 51, 4942-4956. https://robjhyndman.com/publications/funcfor/

Author

Rob J Hyndman