nlts (version 1.0-2)

prediction.profile.ll: Nonlinear forecasting at varying lags using local polynomial regression.

Description

A wrapper function around ll.order to calculate prediction profiles (a la Sugihara and May 1990 and Yao and Tong 1994). The method uses leave-one-out cross-validation of the local regression (with CV optimized bandwidth) against lagged-abundances at various lags.

Usage

prediction.profile.ll(x, step = 1:10, order = 1:5, deg = 2,
  bandwidth = c(seq(0.3, 1.5, by = 0.1), 2:10))

Arguments

x

A time series without missing values.

step

The vector of time steps for forward prediction.

order

The candidate orders. The default is 1:5.

deg

The degree of the local polynomial.

bandwidth

The candidate bandwidths to be considered.

Value

An object of class "ppll" consisting of a list with the following components:

step

the prediction steps considered.

CV

the cross-validation error.

order

the optimal order for each step.

bandwidth

the optimal bandwidth for each step.

df

the degrees of freedom for each step.

Details

see ll.order for details.

References

Sugihara, G., and May, R.M. (1990) Nonlinear forecasting as a way of distinguishing chaos from measurement error in time series. Nature 344, 734-741. https://doi.org/10.1038/344734a0

Yao, Q. and Tong, H. (1994) Quantifying the influence of initial values on non-linear prediction. Journal of Royal Statistical Society B, 56, 701-725.

Fan, J., Yao, Q., and Tong, H. (1996) Estimation of conditional densities and sensitivity measures in nonlinear dynamical systems. Biometrika, 83, 189-206. https://doi.org/10.1093/biomet/83.1.189

See Also

ll.order

Examples

# NOT RUN {
   data(plodia)

     fit <- prediction.profile.ll(sqrt(plodia), step=1:3, order=1:3,
          bandwidth = seq(0.5, 1.5, by = 0.5))

    
# }
# NOT RUN {
plot(fit)
# }