Nonlinear forecasting at verying lags using local polynomial regression.

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Description

A wrapper function around ll.order to calculate prediction profiles (a la Sugihara \& May 1990 and Yao \& 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

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    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 predicition.

order

The candidate orders. The default is 1:5.

deg

The degree of the local polynomial.

bandwidth

The candidate bandwidths to be considered.

Details

see ll.order for details.

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 otpimal bandwidth for each step.

df

the degrees of freedom for each step.

Author(s)

Ottar N. Bjornstad onb1@psu.edu

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

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.

See Also

ll.order

Examples

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   data(plodia)

     fit1 <- prediction.profile.ll(sqrt(plodia), step=1:3, order=1:3,
          bandwidth = seq(0.5, 1.5, by = 0.5))

    ## Not run: plot.ppll(fit1)

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