canarm: Canonical Correlation Analysis of Scalar Time Series

In timsac: Time Series Analysis and Control Package

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

timsac documentation built on Sept. 30, 2023, 5:06 p.m.