prdctr {timsac}

Prediction Program

Description

Operate on a real record of a vector process and compute predicted values.

Usage

prdctr(y, r, s, h, arcoef, macoef = NULL, impulse = NULL, v, plot = TRUE)

Arguments

y

a univariate time series or a multivariate time series.

r

one step ahead prediction starting position R.

s

long range forecast starting position S.

h

maximum span of long range forecast H.

arcoef

AR coefficient matrices.

macoef

MA coefficient matrices.

impulse

impulse response matrices.

v

innovation variance.

plot

logical. If TRUE (default), the real data and predicted values are plotted.

Details

One step ahead Prediction starts at time R and ends at time S. Prediction is continued without new observations until time S+H. Basic model is the autoregressive moving average model of y(t) which is given by

y(t) - A(t)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

predct

predicted values : predct[i] (r\le i \les+h).

ys

predct[i] - y[i] (r\le i \le n).

pstd

predct[i] + (standard deviation) (s\le i \les+h).

p2std

predct[i] + 2*(standard deviation) (s\le i \les+h).

p3std

predct[i] + 3*(standard deviation) (s\le i \les+h).

mstd

predct[i] - (standard deviation) (s\le i \les+h).

m2std

predct[i] - 2*(standard deviation) (s\le i \les+h).

m3std

predct[i] - 3*(standard deviation) (s\le i \les+h).

References

H.Akaike, E.Arahata and T.Ozaki (1975) Computer Science Monograph, No.6, Timsac74, A Time Series Analysis and Control Program Package (2). 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)
y1 <- y[1:900]
z <- autoarmafit(y1)
ar <- z$model[[1]]$arcoef
ma <- z$model[[1]]$macoef
var <- z$model[[1]]$v
y2 <- y[901:990]
prdctr(y2, r = 50, s = 90, h = 10, arcoef = ar, macoef = ma, v = var)