TSSS (version 1.2.3)

lsar: Decomposition of Time Interval to Stationary Subintervals

Description

Decompose time series to stationary subintervals and estimate local spectrum.

Usage

lsar(y, max.arorder = 20, ns0, plot = TRUE, …)

Arguments

y

a univariate time series.

max.arorder

highest order of AR model.

ns0

basic local span.

plot

logical. If TRUE (default), local spectra are plotted.

…

further arguments to be passed to plot.lsar.

Value

An object of class "lsar", which is a list with the following elements:

model

1: pooled model is accepted. 2: switched model is accepted.

ns

number of observations of local span.

span

start points and end points of local spans.

nf

number of frequencies.

ms

order of switched model.

sds

innovation variance of switched model.

aics

AIC of switched model.

mp

order of pooled model.

sdp

innovation variance of pooled model.

aics

AIC of pooled model.

spec

local spectrum.

tsname

the name of the univariate time series y.

References

Kitagawa, G. (2010) Introduction to Time Series Modeling. Chapman & Hall/CRC.

Examples

# NOT RUN {
# seismic data
data(MYE1F)
lsar(MYE1F, max.arorder = 10, ns0 = 100)
# }

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