canarm {timsac}R Documentation

Canonical Correlation Analysis of Scalar Time Series

Description

Fit an ARMA model to stationary scalar time series through the analysis of canonical correlations between the future and past sets of observations.

Usage

canarm(y, lag=NULL, max.order=NULL, plot=TRUE)

Arguments

y a univariate time series.
lag maximum lag. Default is 2*sqrt(n), where n is the length of the time series y.
max.order upper limit of AR order and MA order, must be less than or equal to lag. Default is lag.
plot logical. If TRUE (default) parcor is plotted.

Details

The ARMA model of stationary scalar time series y(t) (t=1,...,n) is given by

y(t) - a(1)y(t-1) -...- a(p)y(t-p) = u(t) - b(1)u(t-1) -...- b(q)u(t-q),

where p is AR order and q is MA order.

Value

arinit AR coefficients of initial AR model fitting by the minimum AIC procedure.
v innovation vector.
aic AIC.
aicmin minimum AIC.
order.maice order of minimum AIC.
parcor partial autocorrelation.
nc total number of case.
future number of present and future variables.
past number of present and past variables.
cweight future set canonical weight.
canocoef canonical R.
canocoef2 R-squared.
chisquar chi-square.
ndf N.D.F.
dic DIC.
dicmin minimum DIC.
order.dicmin order of minimum DIC.
arcoef AR coefficients a(i) (i = 1,...,p).
macoef MA coefficients b(i) (i = 1,...,q).

References

H.Akaike, E.Arahata and T.Ozaki (1975) Computer Science Monograph, No.5, Timsac74, A Time Series Analysis and Control Program Package (1). The Institute of Statistical Mathematics.

Examples

  # "arima.sim" is a function in "stats".
  # Note that the sign of MA coefficient is opposite from that in "timsac".
  y <- arima.sim(list(order=c(2,0,1), ar=c(0.64,-0.8), ma=c(-0.5)), n=1000)
  z <- canarm(y, max.order=30)
  z$arcoef
  z$macoef

[Package timsac version 1.2.1 Index]