predict_rkhs: Predict functional time series using ARH RKHS.

In fpcb: Predictive Confidence Bands for Functional Time Series Forecasting

Description

using an ARH of order 1 obtain 1 step ahead forecast and 1-alpha predictive confidence bands for the forecasted function.

Usage

predict_rkhs(
  model,
  newdata,
  bands = FALSE,
  B = 100,
  level = 0.95,
  kvec = round(sqrt(2 * B))
)

Arguments

model	a arh_rkhs object containing the functional objects and the lambda coefficients of the d dimensional RKHS representation and the autocorrelation operator.
newdata	an optional data frame in which to look for variables with which to predict. If missing, the fitted values are used.
bands	logical variable indicating if the predictive confidence band is computed. Default = FALSE.
B	number of bootstrap replicates for the band construction. Needed if bands = TRUE. Default = 100.
level	confidence level for the band construction. Needed if bands = TRUE. Default = 0.95.
kvec	number of neighbour points to consider in the computation of the minimum entropy set.

Value

forecast	1 step ahead forecast.
fitted	fitted values.
UB	upper bound of the 1-alpha predictive confidence band.
LB	lower bound of the 1-alpha predictive confidence band.
bootsrap.pred	bootstrap pseudo replicates.
bootsrap.pred.inband	bootstrap pseudo replicates included in the 1-alpha predictive confidence band.
res	estimation residuals.

Author(s)

N. Hernández and J. Cugliari

References

N. Hernández, J. Cugliari, J. Jacques. Simultaneous Predictive Bands for Functional Time Series using Minimum Entropy Sets. arXiv:2105.13627 (2021).

fpcb documentation built on June 7, 2021, 9:08 a.m.