autoarmafit {timsac} | R Documentation |
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.
autoarmafit(y, max.order=NULL, tmp.file=NULL)
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. |
tmp.file |
a character string naming a file written intermediate results of model fitting. If NULL (default) output no file. |
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 zwro mean white noise. Pure autoregression is not allowed.
best.order |
the order of the best ARMA model. |
best.model |
Tte best choice of ARMA coefficients. |
model |
a list with components named 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. |
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.
# "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 <- autoarmafit(y) z$best.order z$best.model