nonst {timsac} | R Documentation |
Locally fit autoregressive models to non-stationary time series by AIC criterion.
nonst(y, span, max.order=NULL, plot=TRUE)
y |
a univariate time series. |
span |
length of the basic local span. |
max.order |
highest order of AR model. Default is 2*sqrt(n), where n is the length of the time series y. |
plot |
logical. If TRUE (the default) spectrums are plotted. |
The basic AR model is given by
y(t) = A(1)y(t-1) + A(2)y(t-2) +...+ A(p)y(t-p) + u(t),
where p is order of the AR model and u(t) is innovation variance.
AIC is defined by
AIC = nlog(det(sd)) + 2k
where n is the length of data, sd is the estimates of the innovation variance and k is the number of parameter.
ns |
the number of local spans. |
arcoef |
AR coefficients. |
v |
innovation variance. |
aic |
AIC. |
daic21 |
= AIC2-AIC1. |
daic |
= daic21/n (n is the length of the time series "y" ). |
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. |
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.
# Non-stationary Test Data data(nonstData) nonst(nonstData, span=700, max.order=49)