R: Automatic ARMA Model Fitting

autoarmafit {timsac} | R Documentation

## Automatic ARMA Model Fitting

### Description

Provide an automatic ARMA model fitting procedure. Models with various orders are fitted and the best choice is determined with the aid of the statistics AIC.

### Usage

      autoarmafit(y, max.order = NULL)

### Arguments

`y` | a univariate time series.
`max.order` | upper limit of AR order and MA order. Default is 2*sqrt(n), where n is the length of the time series `y`.

### Details

The maximum likelihood estimates of the coefficients of a scalar ARMA model

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

of a time series y(t) are obtained by using DAVIDON's variance algorithm. Where p is AR order, q is MA order and u(t) is a zero mean white noise. Pure autoregression is not allowed.

### Value

`best.model` | the best choice of ARMA coefficients.
`model` | a list with components `arcoef` (Maximum likelihood estimates of AR coefficients), `macoef` (Maximum likelihood estimates of MA coefficients), `arstd` (AR standard deviation), `mastd` (MA standard deviation), `v` (Innovation variance), `aic` (AIC = n log(det(v))+2(p+q)) and `grad` (Final gradient) in AIC increasing order.

### 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=-0.5), n = 1000)
    autoarmafit(y)

[Package timsac version 1.3.6 Index]