R: Prediction Program

prdctr {timsac} | R Documentation

## 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`<=i<=`s`+`h`).
`ys` | `predct[i]` - `y[i]` (`r`<=i<=n).
`pstd` | `predct[i]` + (standard deviation) (`s`<=i<=`s`+`h`).
`p2std` | `predct[i]` + 2*(standard deviation) (`s`<=i<=`s`+`h`).
`p3std` | `predct[i]` + 3*(standard deviation) (`s`<=i<=`s`+`h`).
`mstd` | `predct[i]` - (standard deviation) (`s`<=i<=`s`+`h`).
`m2std` | `predct[i]` - 2*(standard deviation) (`s`<=i<=`s`+`h`).
`m3std` | `predct[i]` - 3*(standard deviation) (`s`<=i<=`s`+`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)

[Package timsac version 1.3.6 Index]