ftsa (version 1.3)

dynupdateweightselect: Selecting an optimal tuning parameter for the PLS and RR methods

Description

Selecting an optimal tuning parameter for the penalized least squares and ridge regression methods used in the dynupdate.

Usage

dynupdateweightselect(data, method = c("pls", "ridge"), interval = c(0, 10^4), p, 
                      backh = 1, errortype = c("mse", "mae", "mape"))

Value

minimumOptimal tuning parameter.objectiveValue of the error function at the optimal point.

Rdversion

1.1

Details

The optimal tuning parameter is selected by minimizing an averaged forecast error measure. This function also utilizes the optimize function.

References

H. Shang and R. J. Hyndman (2009) "Nonparametric time series forecasting with dynamic updating", In R. S. Anderssen, R. D. Braddock and L.T.H. Newham (eds), 18th World IMACS Congress and MODSIM09 International Congress on Modelling and Simulation. Modelling and Simulation Society of Australia and New Zealand and International Association for Mathematics and Computers in Simulation, July 2009, pp. 1552-1558. ISBN: 978-0-9758400-7-8. http://www.mssanz.org.au/modsim09/D11/shang.pdf

See Also

ftsmweightselect, fplsrweightselect, dynupdate

Examples

dynupdateweightselect(data = ElNino, method = "pls", p = 4, errortype = "mse")