nonst {timsac} | R Documentation |
Non-stationary Power Spectrum Analysis
Description
Locally fit autoregressive models to non-stationary time series by AIC criterion.
Usage
nonst(y, span, max.order = NULL, plot = TRUE)
Arguments
y |
a univariate time series. |
span |
length of the basic local span. |
max.order |
highest order of AR model. Default is
|
plot |
logical. If |
Details
The basic AR model is given by
where is order of the AR model and
is innovation variance.
AIC is defined by
where is the length of data,
is the estimates of the
innovation variance and
is the number of parameter.
Value
ns |
the number of local spans. |
arcoef |
AR coefficients. |
v |
innovation variance. |
aic |
AIC. |
daic21 |
= AIC2 - AIC1. |
daic |
= |
init |
start point of the data fitted to the current model. |
end |
end point of the data fitted to the current model. |
pspec |
power spectrum. |
References
H.Akaike, E.Arahata and T.Ozaki (1976) Computer Science Monograph, No.6, Timsac74 A Time Series Analysis and Control Program Package (2). The Institute of Statistical Mathematics.
Examples
# Non-stationary Test Data
data(nonstData)
nonst(nonstData, span = 700, max.order = 49)