ISmCurveEstimation.MissingData

Fore Syntax

MissingData: IMissingData;

Fore.NET Syntax

MissingData: Prognoz.Platform.Interop.Stat.SIMissingData;

Description

The MissingData property determines missing data treatment parameters.

Comments

By default missing data is not treated.

Fore Example

Add a link to the Stat system assembly.

Sub UserProc;
Var
    trend: SmCurveEstimation;
    ar: Array[25] Of Double;
    d0: Double;
    MData: IMissingData;
    status, i, s: Integer;
    Forms: IDependenceForms;
    Poly: IDependenceForm;
    Forecast: IForecast;
    Period: IStatPeriod;
Begin
    // Create object for method calculation
    trend := New SmCurveEstimation.Create;
    // Set explained series values
    ar[00] := 4110; ar[01] := 4280; ar[02] := 4459; ar[03] := 4545; ar[04] := 4664;
    ar[05] := 4861; ar[06] := 5195; ar[07] := 4664; ar[08] := 9054; ar[09] := Double.Nan;
    ar[10] := 5948; ar[11] := Double.Nan; ar[12] := 6521; ar[13] := 6788; ar[14] := 7222;
    ar[15] := 7486; ar[16] := 7832; ar[17] := 8153; ar[18] := 8468; ar[19] := Double.Nan;
    ar[20] := 9499; ar[21] := 9866; ar[22] := 10217; ar[23] := 10763; ar[24] := 10683;
    trend.Explained.Value := ar;
    // Set explanatory series parameters
    trend.Explanatory.IsTrend := True;
    // Set sample period
    Period := trend.ModelPeriod;
    Period.FirstPoint := 10;
    Period.LastPoint := 20;
    // Set forecasting period borders
    trend.ForecastFirstPoint := 20;
    trend.ForecastLastPoint := 30;
    // Determine confidence limit relevance of forecasting series
    trend.ForecastConfidenceLevel := 0.85;
    // Determine type of best dependency form selection criterion
    trend.Criterion := DependenceCriterion.R2Adj;
    // Set seasonal component parameters
    trend.SeasonalComponent.Mode := SeasonalityType.Additive;
    trend.SeasonalComponent.Cycle := 4;
    // Set polynomial degree for polynomial dependency form
    trend.PolynomOrder := 4;
    // Set missing data treatment method parameters
    MData := trend.MissingData;
    MData.Method := MissingDataMethod.SampleAverage;

// Execute calculation
 status := trend.Execute;
 If status <> 0 Then
 // If calculation is finished with errors, output them to console window
 Debug.WriteLine(trend.Errors);
 Else
 // If calculation is completed successfully, display values to the console window
 Debug.WriteLine("== Modeling series == ");
 Debug.Indent;
 For i := 0 To trend.Explained.Value.Length - 1 Do
 Debug.WriteLine(trend.Explained.Value[i]);
 End For;
 Debug.Unindent;
 Debug.WriteLine("== Source series ==");
 Debug.Indent;
 For i := 0 To trend.Explained.Value.Length - 1 Do
 Debug.WriteLine(trend.Explained.OriginalValue[i]);
 End For;
 Debug.Unindent;
 Forms := trend.DependenceForms;
 Debug.WriteLine("== Criterion values ==");
 For i := 0 To Forms.Count - 3 Do
 s := trend.SortedModelList[i];
 d0 := Forms.Item(s).CriterionValue;
 Debug.WriteLine(Forms.Item(s).DisplayName + ": " + d0.ToString);
 End For;
 Debug.WriteLine("== Best dependency ==");
 Debug.WriteLine(trend.BestDependenceForm);
 Poly := Forms.Item(2);
 Forecast := Poly.Forecast;
 Debug.WriteLine("Dependency " + Poly.DisplayName + " criterion value " + Poly.CriterionValue.ToString);
 Debug.WriteLine("== Forecasting series ==");
 Debug.Indent;
 For i := trend.ForecastFirstPoint To Forecast.Value.Length - 1 Do
 Debug.WriteLine(Forecast.Value[i]);
 End For;
 Debug.Unindent;
 Debug.WriteLine("== Seasonal component ==");
 Debug.Indent;
 For i := Period.FirstPoint To trend.SeasonalAdjustment.Length - 1 Do
 d0 := trend.SeasonalAdjustment[i];
 Debug.WriteLine(d0.ToString);
 End For;
 Debug.Unindent;
 End If;
End Sub UserProc;

After executing the example the console window displays a modeling and source series, dependency criterion values, best dependency number, the forecasting series calculated by polynomial dependency, seasonal component values.

Fore.NET Example

The requirements and result of the Fore.NET example execution match with those in the Fore example.

Imports Prognoz.Platform.Interop.Stat;
…
Public Shared Sub UserProc();
Var
 trend: SmCurveEstimation;
 ar: Array[25] Of Double;
 d0: Double;
 MData: IMissingData;
 status, i, s: Integer;
 Forms: IDependenceForms;
 Poly: IDependenceForm;
 Forecast: IForecast;
 Period: IStatPeriod;
Begin
 // Create object for method calculation
 trend := New SmCurveEstimation.Create();
 // Set values of the explained series
 ar[00] := 4110; ar[01] := 4280; ar[02] := 4459; ar[03] := 4545; ar[04] := 4664;
 ar[05] := 4861; ar[06] := 5195; ar[07] := 4664; ar[08] := 9054; ar[09] := Double.Nan;
 ar[10] := 5948; ar[11] := Double.Nan; ar[12] := 6521; ar[13] := 6788; ar[14] := 7222;
 ar[15] := 7486; ar[16] := 7832; ar[17] := 8153; ar[18] := 8468; ar[19] := Double.Nan;
 ar[20] := 9499; ar[21] := 9866; ar[22] := 10217; ar[23] := 10763; ar[24] := 10683;
 trend.Explained.Value := ar;
 // Set explanatory series parameters
 trend.Explanatory.IsTrend := True;
 // Set identification period
 Period := trend.ModelPeriod;
 Period.FirstPoint := 10;
 Period.LastPoint := 20;
 // Set forecasting period borders
 trend.ForecastFirstPoint := 20;
 trend.ForecastLastPoint := 30;
 // Determine confidence limits relevance of forecasting series
 trend.ForecastConfidenceLevel := 0.85;
 // Determine type of best dependency form selection criterion
 trend.Criterion := DependenceCriterion.dctR2Adj;
 // Set seasonal component parameters
 trend.SeasonalComponent.Mode := SeasonalityType.sstAdditive;
 trend.SeasonalComponent.Cycle := 4;
 // Set polynomial degree for polynomial dependency form
 trend.PolynomOrder := 4;
 // Set missing data treatment method parameters
 MData := trend.MissingData;
 MData.Method := MissingDataMethod.mdmSampleAverage;
 // Perform calculation
 status := trend.Execute();
 If status <> 0 Then
 // If calculation is finished with errors, output them to console window
 System.Diagnostics.Debug.WriteLine(trend.Errors);
 Else
 // If calculation is completed successfully, display values to the console window
 System.Diagnostics.Debug.WriteLine("== Modeling series == ");
 System.Diagnostics.Debug.Indent();
 For i := 0 To trend.Explained.Value.Length - 1 Do
 System.Diagnostics.Debug.WriteLine(trend.Explained.Value.GetValue(i));
 End For;
 System.Diagnostics.Debug.Unindent();
 System.Diagnostics.Debug.WriteLine("== Source series ==");
 System.Diagnostics.Debug.Indent();
 For i := 0 To trend.Explained.Value.Length - 1 Do
 System.Diagnostics.Debug.WriteLine(trend.Explained.OriginalValue.GetValue(i));
End For; System.Diagnostics.Debug.Unindent(); Forms := trend.DependenceForms; System.Diagnostics.Debug.WriteLine("== Criterion values =="); For i := 0 To Forms.Count - 3 Do s := trend.SortedModelList.GetValue(i) As integer; d0 := Forms.Item[s].CriterionValue; System.Diagnostics.Debug.WriteLine(Forms.Item[s].DisplayName + ": " + d0.ToString()); End For; System.Diagnostics.Debug.WriteLine("== Best dependency =="); System.Diagnostics.Debug.WriteLine(trend.BestDependenceForm); Poly := Forms.Item[2]; Forecast := Poly.Forecast; System.Diagnostics.Debug.WriteLine("Dependency " + Poly.DisplayName + " criterion value " + Poly.CriterionValue.ToString()); System.Diagnostics.Debug.WriteLine("== Forecasting series =="); System.Diagnostics.Debug.Indent(); For i := trend.ForecastFirstPoint To Forecast.Value.Length - 1 Do System.Diagnostics.Debug.WriteLine(Forecast.Value.GetValue(i)); End For; System.Diagnostics.Debug.Unindent(); System.Diagnostics.Debug.WriteLine("== Seasonal component =="); System.Diagnostics.Debug.Indent(); For i := Period.FirstPoint To trend.SeasonalAdjustment.Length - 1 Do d0 := trend.SeasonalAdjustment.GetValue(i) As double; System.Diagnostics.Debug.WriteLine(d0.ToString()); End For; System.Diagnostics.Debug.Indent(); End If; End Sub UserProc;