selectHyperParms: Automatic selection of model hyper-parameters

In tsDyn: Nonlinear Time Series Models with Regime Switching

Automatic selection of model hyper-parameters

Description

Automatic selection of model hyper-parameters

Usage

selectLSTAR(x, m, d=1, steps=d, mL = 1:m, mH = 1:m, thDelay=0:(m-1), 
            fast=TRUE, trace=FALSE)
selectNNET(x, m, d=1, steps=d, size=1:(m+1), maxit=1e3, trace=FALSE)

Arguments

x	time series
m, d, steps	embedding parameters. For their meanings, see help about nlar
mL, mH	Vector of ‘low’ and ‘high’ regimes autoregressive orders
thDelay	Vector of ‘threshold delay’ values
size	Vector of numbers of hidden units in the nnet model
maxit	Max. number of iterations for each model estimation
fast	For LSTAR selection, whether a fast algorithm using starting values fro previous models should be used
trace	Logical. Whether informations from each model should be returned.

Details

Functions for automatic selection of LSTAR and NNET models hyper parameters. An exhaustive search over all possible combinations of values of specified hyper-parameters is performed. Embedding parameters m,d,steps are kept fixed.

Selection criterion is the usual AIC.

For the LSTAR model, two methods are offered:

fast=FALSE

Each model is run separately, each time using the full grid search for starting values.

fast=TRUE

Only the first model is run with a full grid search, while the subsequent use the first model results for their starting values.

Value

A data-frame, with columns giving hyper-parameter values and the computed AIC for each row (only the best 10s are returned)

Author(s)

Antonio, Fabio Di Narzo

Examples

llynx <- log10(lynx)
selectLSTAR(llynx, m=2)
selectNNET(llynx, m=3, size=1:5)

tsDyn documentation built on Oct. 31, 2024, 5:08 p.m.