ISlARMA.OrderARSeas

Syntax

OrderARSeas: Array;

Description

The OrderARSeas property determines seasonal autoregression order.

Comments

OrderARSeas is an integer array.It is used when calculating autoregression and moving average in the ARIMA model.

If the order of seasonal autoregression is defined, corresponding coefficients will be estimated: ISlARMA.CoefficientsARSeas.

Example

Add a link to the Stat system assembly.

Sub UserProc;
Var
    lr: ISmLinearRegress;
    W: Array[40] Of Double;
    ARMA: ISlARMA;
    AR, MA: Array[1] Of Integer;
    Inits: Array[2] Of Double;
    res: Integer;
    d: Double;
    CoefficientsAR, CoefficientsMA: ICoefficients;
    ModelCoefficients: IModelCoefficients;
Begin
    lr := New SmLinearRegress.Create;
// explained series values
    w[00] := 6209; w[10] := 7132; w[20] := 9907; w[30] := 14242;
    w[01] := 6385; w[11] := 7137; w[21] := 10333; w[31] := 14704;
    w[02] := 6752; w[12] := 7473; w[22] := 10863; w[32] := 13802;
    w[03] := 6837; w[13] := 7722; w[23] := 11693; w[33] := 14197;
    w[04] := 6495; w[14] := 8088; w[24] := 12242; w[34] := Double.Nan;
    w[05] := 6907; w[15] := 8516; w[25] := 12227; w[35] := 15589;
    w[06] := 7349; w[16] := 8941; w[26] := 12910; w[36] := 15932;
    w[07] := 7213; w[17] := 9064; w[27] := 13049; w[37] := 16631;
    w[08] := 7061; w[18] := 9380; w[28] := 13384; w[38] := Double.Nan;
    w[09] := 7180; w[19] := 9746; w[29] := 14036; w[39] := 17758;
// Sample period
    lr.ModelPeriod.FirstPoint := 1;
    lr.ModelPeriod.LastPoint := 23;
    lr.Forecast.LastPoint := 35;
    lr.MissingData.Method := MissingDataMethod.Casewise;
    lr.Explained.Value := w;
    ModelCoefficients := lr.ModelCoefficients;
// a constant is used in the model
    ModelCoefficients.Intercept.Mode := InterceptMode.AutoEstimate;
    ARMA := lr.ARMA;
// seasonal autoregression order
    AR[0] := 1;
    ARMA.OrderARSeas := AR;
// seasonal moving average order

    MA[0] := 1;
    ARMA.OrderMASeas := MA;
// seasonal autoregression initial approximations
    Inits[0] := 0.0025;
    ARMA.InitARSeas := Inits;
// initial approximations of seasonal moving average
    Inits[0] := 0.0035;
    ARMA.InitMASeas := Inits;
// seasonal difference
    ARMA.DiffSeas := 0;
// seasonality period
    ARMA.PeriodSeas := 4;
// difference
    ARMA.Diff := 2;
// optimization method
    ARMA.EstimationMethod := ARMAEstimationMethodType.GaussNewton;
// model calculation
    res := lr.Execute;
    Debug.WriteLine(lr.Errors);
    If (res = 0) Then
    // seasonal autoregression coefficients
        Debug.WriteLine("Seasonal autoregression coefficients' estimates");
        CoefficientsAR := ARMA.CoefficientsARSeas;
        d := CoefficientsAR.Estimate[0];
        Debug.WriteLine(" Value: " + d.ToString);
        d := CoefficientsAR.Probability[0];
        Debug.WriteLine(" Probability: " + d.ToString);
    // seasonal moving average coefficients
        Debug.WriteLine("Seasonal moving average coefficients' estimates");
        CoefficientsMA := ARMA.CoefficientsMASeas;
        d := CoefficientsMA.Estimate[0];
        Debug.WriteLine(" Value: " + d.ToString);
        d := CoefficientsMA.Probability[0];
        Debug.WriteLine(" Probability: " + d.ToString);
    End If;
End Sub UserProc;

After executing the example a linear regression model is created, its parameters are determined, and the orders of seasonal autoregression and seasonal moving average are defined. The console window displays estimations of model coefficients.