Computes integrated squared forecast error (ISFE) values for functional time series models of various orders.
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, ...)An object of class fts.
Maximum number of principal components to fit.
Minimum number of functional observations to be used in fitting a model.
Forecast horizons over which to average.
Method to use for principal components decomposition. Possibilities are “M”, “rapca” and “classical”.
Indicates if mean term should be included.
Indicates if level term should be included.
Method used for forecasting. Current possibilities are “ets”, “arima”, “ets.na”, “struct”, “rwdrift” and “rw”.
Tuning parameter for robustness when method = "M".
Additional arguments controlling the fitting procedure.
Numeric matrix with (max.order+1) rows and length(h) columns
containing ISFE values for models of orders 0:(max.order).
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.
ftsm, forecast.ftsm, plot.fm, plot.fmres, summary.fm, residuals.fm