prediction.profile.ll: Nonlinear forecasting at varying lags using local polynomial...
In nlts: Nonlinear Time Series Analysis

Description Usage Arguments Details Value References See Also Examples

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.

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

`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.

see ll.order for details.

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.

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

ll.order

   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)