Description Usage Arguments Details Value Note Author(s) References See Also Examples
Fits an ARIMA(p,d,q) model using the algorithm given in McLeod and Zhang (2007).
1 |
z |
time series |
order |
model order, c(p,d,q) |
demean |
if TRUE, mean parameter included otherwise assumed zero |
MeanMLEQ |
exact MLE for mean, ignored unless demean=TRUE |
pApprox |
order of approximation to be used |
MaxLag |
maximum number of lags for portmanteau test |
See McLeod and Ying (2007).
A list with class name "FitARMA" and components:
loglikelihood |
value of the loglikelihood |
phiHat |
AR coefficients |
thetaHat |
MA coefficients |
sigsqHat |
innovation variance estimate |
muHat |
estimate of the mean |
covHat |
covariance matrix of the coefficient estimates |
racf |
residual autocorrelations |
LjungBox |
table of Ljung-Box portmanteau test statistics |
res |
innovation residuals, same length as z |
fits |
fitted values, same length as z |
demean |
TRUE if mean estimated otherwise assumed zero |
IterationCount |
number of iterations in mean mle estimation |
convergence |
value returned by optim – should be 0 |
MLEMeanQ |
TRUE if mle for mean algorithm used |
tsp |
tsp(z) |
call |
result from match.call() showing how the function was called |
ModelTitle |
description of model |
DataTitle |
returns attr(z,"title") |
When d>0 and demean=TRUE, the mean of the differenced series is estimated. This corresponds to including a polynomial of degree d.
When d>0, the AIC/BIC are computed for the differenced series and so they are not comparable to the values obtained for models with d=0.
A.I. McLeod, aimcleod@uwo.ca
A.I. McLeod andY. Zhang (2008), Faster ARMA maximum likelihood estimation, Computational Statistics & Data Analysis, 52-4, 2166-2176. DOI link: http://dx.doi.org/10.1016/j.csda.2007.07.020
GetFitARMA
,
print.FitARMA
,
coef.FitARMA
,
residuals.FitARMA
,
fitted.FitARMA
,
arima
1 2 3 4 5 6 7 |
Loading required package: FitAR
Loading required package: lattice
Loading required package: leaps
Loading required package: ltsa
Loading required package: bestglm
Chemical process concentrations
ARIMA(1,0,1)
length of series = 197 , number of parameters = 3
loglikelihood = 228.79 , aic = -451.6 , bic = -441.7
MLE sd Z-ratio
phi(1) 0.9086651 0.04260981 21.32525596
theta(1) 0.5758000 0.08343768 6.90095818
mu 17.0624365 319.55196104 0.05339487
Chemical process concentrations
ARIMA(0,1,1)
length of series = 196 , number of parameters = 2
loglikelihood = 224.73 , aic = -445.5 , bic = -438.9
MLE sd Z-ratio
theta(1) 0.703101044 0.05079214 13.84271366
mu 0.002040816 0.02261138 0.09025615
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