Estimates SARIMA models using a state space representation, the Kalman filter, and maximum likelihood.
sarimaSS(y, p, d, q, P_s, D_s, Q_s, s, trend, const)¶
y (Nx1 vector) – data.
p (Scalar) – the autoregressive order.
d (Scalar) – the order of differencing.
q (Scalar) – the moving average order.
P_s (Scalar) – the seasonal autoregressive order.
D_S (Scalar) – the seasonal order of differencing.
Q_s (Scalar) – the seasonal moving average order.
s (Scalar) – the seasonal frequency term.
trend (Scalar) – an indicator variable to include a trend in the model. Set to 1 to include trend, 0 otherwise.
const (Scalar) – an indicator variable to include a constant in the model. Set to 1 to include trend, 0 otherwise.
amo (struct) –
An instance of an arimamtOut structure containing the following members:
Scalar, value of the Akaike information criterion.
Kx1 vector, estimated model coefficients.
Nx1 vector, residual from fitted model.
Scalar, the value of the log likelihood function.
Scalar, value of the Schwartz Bayesian criterion.
Lx1 vector, the Likelihood Ratio Statistic.
KxK matrix, the covariance matrix of estimated model coefficients.
Scalar, mean sum of squares for errors.
Scalar, the sum of squares for errors.
Scalar, the sum of squares for Y data.
an instance of the kalmanResult structure.
new; cls; library tsmt; airline = loadd( getGAUSSHome() $+ "pkgs/tsmt/examples/airline.dat"); // Transform data y = ln(airline); p = 0; d = 1; q = 1; P_s = 0; D_s = 1; Q_s = 1; s=12; trend = 0; const = 0; struct arimamtOut amo; amo = sarimaSS( y, p, d, q, P_s, D_s, Q_s, s, trend, const );