Calculating the Linear Regression Method

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  <constantMode>ManualEstimate</constantMode>

  <constantValue>0.5</constantValue>

  <confidenceLevel>0.85</confidenceLevel>

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  <k>4294967295</k>

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  <d>-0.7626332420071756</d>

  <d>-0.7626332420071756</d>

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  <d>0.8682687093029638</d>

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  <d>0.07799376856974333</d>

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  <d>11.13253949931325</d>

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  <stdErrSum>0</stdErrSum>

  <tStatSum>0</tStatSum>

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  <mode>ManualEstimate</mode>

  <estimate>0.5</estimate>

  <standardError>NaN</standardError>

  <tStatistic>NaN</tStatistic>

  <probability>NaN</probability>

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<estimate>

  <d>-0.08396406739747959</d>

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  <d>0.02130633695023525</d>

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  <d>-3.94080256937608</d>

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<probability>

  <d>0.0007483835396404626</d>

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  <name>0.5000 + PDL(X1, 1, 1) + [AR(1)=0.8683]</name>

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  <lag>0</lag>

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  <date>1899-12-30</date>

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  <d>3</d>

  <d>7</d>

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  <d>6</d>

  <d>4</d>

  <d>1</d>

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  <d>3.28571429</d>

  <d>3.14285714</d>

  <d>3</d>

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  <d>32</d>

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  <d>-28.54510344827582</d>

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  <lag>PrecidingValue</lag>

  <previousLag>-1</previousLag>

  <seasonality>None</seasonality>

  <dependence>Linear</dependence>

  <K>3</K>

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<dates>

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  <it>1994A1</it>

  <it>1995A1</it>

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  <it>2003A1</it>

  <it>2004A1</it>

  <it>2005A1</it>

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  <it>2007A1</it>

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  <it>2016A1</it>

  <it>2017A1</it>

  <it>2018A1</it>

  <it>2019A1</it>

  <it>2020A1</it>

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  <lag>PrecidingValue</lag>

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  <K>3</K>

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  <doUseR>0</doUseR>

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  <kind>Simple</kind>

<displaySettings>

  <displayTermsAs>Derive</displayTermsAs>

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  <additionalAttributes />

  <calculationType>Serie</calculationType>

  <calculationDirection>Forward</calculationDirection>

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  <stochastic>1</stochastic>

  <calculationPeriod>Forecast</calculationPeriod>

  <useModelPeriod>1</useModelPeriod>

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<period>

  <identificationStartDate>1990-01-01</identificationStartDate>

  <identificationEndDate>2018-04-24</identificationEndDate>

  <forecastStartDate>2018-04-25</forecastStartDate>

  <forecastEndDate>2020-01-01</forecastEndDate>

  <identificationStartDateParamID />

  <identificationEndDateParamID />

  <forecastStartDateParamID />

  <forecastEndDateParamID />

  <autoPeriod>28672</autoPeriod>

  <identificationStartOffset>0</identificationStartOffset>

  <identificationEndOffset>0</identificationEndOffset>

  <forecastEndOffset>0</forecastEndOffset>

  <isIdentStartCorrect>1</isIdentStartCorrect>

  <isIdentEndCorrect>1</isIdentEndCorrect>

  <isForecastEndCorrect>1</isForecastEndCorrect>

  </period>

  <isExclusive>1</isExclusive>

  <useAutoPeriod>1</useAutoPeriod>

  <generatedName>MyOutputVavable|A[t] = A0 + PDL(TS(MyInputVavable[t+1] * 8, Linear), 1, 1) + [AR(1)=A1]</generatedName>

  <warnings />

  <readOnly>0</readOnly>

  </model>

  </Item>

  </its>

  </calculationChain>

<visualController>

  <userRPath />

  <isRExist>0</isRExist>

  </visualController>

<suppressEmptyFilter>

  <suppressEmpty>0</suppressEmpty>

  <suppressEmptyArea>SerieBounds</suppressEmptyArea>

  </suppressEmptyFilter>

  <readOnly>0</readOnly>

  <variableTestUseR>0</variableTestUseR>

  <calculateIdentOnFact>0</calculateIdentOnFact>

  </metamodel>

  <useScenarios>0</useScenarios>

  <readOnly>0</readOnly>

  </problemMd>

  </item>

  </meta>

  </GetMsResult>

  </soapenv:Body>

  </soapenv:Envelope>

JSON request:

{
 "GetMs" : 
  {
   "tMs" : 
    {
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    {
     "pattern" : 
      {
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             "calcChainPattern" : 
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                   "formulas" : 
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       "scenarioKeys" : "",
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       "execPairCorrelationMatrix" : "true",
       "execARMACoefficients" : "true",
       "execPDLStatCoefficients" : "true",
       "execStatCoefficients" : "true"
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}

JSON response:

{
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              {
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                                  },
                                 "pdlStatCoefficients" : 
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                                     "probability" : 
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                                },
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                                   "slice" : 
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                                 "addFactor" : 
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                                },
                               "evaluateSeries" : 
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                                      },
                                     "input" : 
                                      {
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                                      },
                                     "factors" : 
                                      {
                                       "its" : 
                                        {
                                         "it" : 
                                          [
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                                  {
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                      {
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                     "calculationDirection" : "Forward"
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                   "stochastic" : "1",
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                }
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            {
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           "calculateIdentOnFact" : "0"
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         "useScenarios" : "0",
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      }
    }
  }
}

public static MsCalculationChainEntry GetMsLinearRegr(MsId ms, ulong modelKey, ulong eqKey)
        {
    var getMsOp = new GetMs();
    // Set operation execution parameters
    getMsOp.tMs = ms;
    getMsOp.tArg = new GetMsArg()
    {  // Set data getting pattern
        pattern = new MsMdPattern()
        {
            obInst = false,
            all = false,
            item = new MsItemPattern()
            {  // Specify calculated model key
                key = modelKey,
                problem = new MsProblemPattern()
                {
                    metamodel = new MsMetaModelPattern()
                    {
                        calculationChain = ListOperation.Get,
                        calcChainPattern = new MsCalculationChainPattern()
                        {
                            // Specify calculated equation key
                            entryKeys = new long[] { (long)eqKey },
                            modelPattern = new MsModelPattern()
                            {
                                autoName = true,
                                calculationPeriod = true,
                                generatedName = true,
                                isExclusive = true,
                                period = true,
                                stochastic = true,
                                treatNullsAsZeros = true,
                                useAutoPeriod = true,
                                useExistingData = true,
                                useModelPeriod = true,
                                transform = new MsFormulaTransformPattern()
                                {
                                    additionalAttributes = true,
                                    calculationDirection = true,
                                    calculationType = true,
                                    displayId = true,
                                    displaySettings = true,
                                    formulaCount = true,
                                    outputs = ListOperation.Get,
                                    series = ListOperation.Get,
                                    kind = true,
                                    formulas = new TsFormulaPattern()
                                    {
                                        method = new TsMethodPattern()
                                        {
                                            series = true
                                        }
                                    },
                                    equationsFormula = new TsFormulaPattern()
                                    {
                                        method = new TsMethodPattern() { }
                                    },
                                    transformVariable = new MsFormulaTransformVariablePattern()
                                    {
                                        slices = ListOperation.Get,
                                        transformSlice = new MsFormulaTransformSlicePattern()
                                        {
                                            selection = ListOperation.Get
                                        }
                                    }
                                }
                            }
                        }
                    }
                }
            }
        }, // Set equation calculation parameters
        execParams = new MsMdExecParams()
        {
            k = 0,
            execMethod = true,
            execEvaluateSeries = true,
            scenarioKeys = new long[] { },
            execPDLStatCoefficients = true,
            pdlIndex = 1,
            execPairCorrelationMatrix = true,
            execStatCoefficients = true,
            execCoefficients = true,
            execARMACoefficients = true,
            modelKeys = new long[] { (long)eqKey }
        }
    };
    // Create a proxy object for operation calculation
    var somClient = new SomPortTypeClient();
    GetMsResult getMsResult = somClient.GetMs(getMsOp);
    MsMetaModel meta = getMsResult.meta.item.problemMd.metamodel;
    MsCalculationChainEntry chainEntry = meta.calculationChain.its.GetValue(0) as MsCalculationChainEntry;
    // Display errors and warnings
    MsModel eq = chainEntry.model;
    if (eq.warnings != null)
    {
        Console.WriteLine("-- Warnings --");
        Console.WriteLine("".PadRight(3) + printArray(eq.warnings));
    }
    if (eq.error == null) //Check if errors occurred on equation calculation
    {   // Equation is calculated without errors, get calculation results   
        TsFormula formula = eq.transform.formulas.its.GetValue(0) as TsFormula;
        TsLinearRegressionMethod linR = formula.method.linearRegression;
        Console.WriteLine("-- Equation coefficient values --");
        Console.WriteLine("".PadRight(3) + printArray(linR.coefficients));
        // Get equation coefficient values and display into console window
        PrintCoef(linR.statCoefficients);
        // Get values of autoregression coefficient, moving average coefficient and display into console window
        printARMA(linR.armaCoefficients);
        Console.WriteLine("-- Correlation matrix --");
        MsPairCorrelationMatrix pm = linR.pairCorrelationMatrix;
        if (pm.error != null) { Console.WriteLine("".PadRight(3) + "error: " + pm.error); }
        Console.WriteLine("".PadRight(3) + printArray(pm.data));
        Console.WriteLine("-- Lag variable coefficients --");
        MsPDLCoefficients pdl = linR.pdlStatCoefficients;
        printPDL(pdl);
        // Display calculated series
        Console.WriteLine("-- Available data series --");
        printSeries(formula.method.series);
        // Get calculated values and display into console window
        MsEvaluateSeriesResult seriesRes = formula.method.evaluateSeries;
        printSeries(seriesRes);
    }
    else // Errors occurred on equation calculation
    {
        Console.WriteLine("-- Errors --");
        Console.WriteLine(eq.error);
    }
    // Return the model that contains equation
    return chainEntry;
}
// Procedure of displaying coefficient values for lag variables
public static void printPDL(MsPDLCoefficients pdl) 
{
    Console.WriteLine("".PadRight(3) + " - sum of coefficients: " + pdl.estimatesSum);
    Console.WriteLine("".PadRight(3) + " - sum of standard errors: " + pdl.stdErrSum);
    Console.WriteLine("".PadRight(3) + " - sum of t-statistics: " + pdl.tStatSum);
    Console.WriteLine("".PadRight(3) + " - Coefficients -"); 
    PrintStatCoef(6, pdl as StatCoefficients);
}
// Procedure of displaying calculated series identifiers
public static void printSeries(MsMethodSeries series) 
{
    if (series.input != null)
        {Console.WriteLine("".PadRight(3) + " - input series");};
    if (series.fitted != null)
        { Console.WriteLine("".PadRight(3) + " - smoothed series"); };
    if (series.forecast != null)
        { Console.WriteLine("".PadRight(3) + " - forecasting series"); };
    if (series.residuals != null)
        { Console.WriteLine("".PadRight(3) + " - residual series"); };
    if (series.lowerConfidenceLevel != null)
        { Console.WriteLine("".PadRight(3) + " - lower confidence limit"); };
    if (series.upperConfidenceLevel != null)
        { Console.WriteLine("".PadRight(3) + " - upper confidence limit"); };
    if (series.dynamicLowerConfidenceLevel != null)
        { Console.WriteLine("".PadRight(3) + " - lower dynamic confidence limit"); };
    if (series.dynamicUpperConfidenceLevel != null)
        { Console.WriteLine("".PadRight(3) + " - upper dynamic confidence limit"); };
    if (series.addFactor != null)
        { Console.WriteLine("".PadRight(3) + " - forecast correction factor"); };
}