Расчёт метода «Линейная регрессия»

SOAP-запрос:

<s:Envelope xmlns:s="http://schemas.xmlsoap.org/soap/envelope/">

<s:Body xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xmlns:xsd="http://www.w3.org/2001/XMLSchema">

<GetMs xmlns="http://www.prognoz.ru/PP.SOM.Som">

<tMs xmlns="">

  <id>IEHFOKNBPENBFOAEDMNIFBMAGBJFMILEEKEMODKJPMDBDAAN!M!S!CKILIGLNBPENBFOAEGCFPILNFBENOEAEENLANPEIMKOMICGHG</id>

  </tMs>

<tArg xmlns="">

<pattern>

  <obInst>false</obInst>

  <all>false</all>

<item>

  <key>89669</key>

<problem>

<metamodel>

  <calculationChain>Get</calculationChain>

<calcChainPattern>

<modelPattern>

<transform>

  <outputs>Get</outputs>

  <formulaCount>true</formulaCount>

<formulas>

<method>

  <series>true</series>

  </method>

  </formulas>

  <displayId>true</displayId>

<equationsFormula>

  <method />

  </equationsFormula>

  <series>Get</series>

  <kind>true</kind>

  <displaySettings>true</displaySettings>

  <additionalAttributes>true</additionalAttributes>

  <calculationType>true</calculationType>

  <calculationDirection>true</calculationDirection>

<transformVariable>

  <slices>Get</slices>

<transformSlice>

  <selection>Get</selection>

  </transformSlice>

  </transformVariable>

  </transform>

  <stochastic>true</stochastic>

  <calculationPeriod>true</calculationPeriod>

  <useModelPeriod>true</useModelPeriod>

  <useExistingData>true</useExistingData>

  <treatNullsAsZeros>true</treatNullsAsZeros>

  <autoName>true</autoName>

  <generatedName>true</generatedName>

  <period>true</period>

  <isExclusive>true</isExclusive>

  <useAutoPeriod>true</useAutoPeriod>

  </modelPattern>

<entryKeys>

  <l>4</l>

  </entryKeys>

  </calcChainPattern>

  </metamodel>

  </problem>

  </item>

  </pattern>

<execParams>

  <k>0</k>

<modelKeys>

  <l>4</l>

  </modelKeys>

  <pdlIndex>1</pdlIndex>

  <scenarioKeys />

  <execMethod>true</execMethod>

  <execCoefficients>true</execCoefficients>

  <execEvaluateSeries>true</execEvaluateSeries>

  <execPairCorrelationMatrix>true</execPairCorrelationMatrix>

  <execARMACoefficients>true</execARMACoefficients>

  <execPDLStatCoefficients>true</execPDLStatCoefficients>

  <execStatCoefficients>true</execStatCoefficients>

  </execParams>

  </tArg>

  </GetMs>

  </s:Body>

  </s:Envelope>

SOAP-ответ:

<soapenv:Envelope xmlns:soapenv="http://schemas.xmlsoap.org/soap/envelope/">

<soapenv:Body>

<GetMsResult xmlns="http://www.prognoz.ru/PP.SOM.Som" xmlns:q1="http://www.prognoz.ru/PP.SOM.Som" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance">

<id xmlns="">

  <id>IEHFOKNBPENBFOAEDMNIFBMAGBJFMILEEKEMODKJPMDBDAAN!M!S!CKILIGLNBPENBFOAEGCFPILNFBENOEAEENLANPEIMKOMICGHG</id>

  </id>

<meta xmlns="">

<item>

  <k>89669</k>

  <id>MODEL_NEW</id>

  <n>MODEL_NEW</n>

  <vis>1</vis>

  <type>Problem</type>

<problemMd>

<metamodel>

  <k>89670</k>

<calculationChain>

<its>

<Item>

  <k>4</k>

  <id>OBJ4</id>

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

  <vis>1</vis>

  <type>Model</type>

  <excluded>0</excluded>

  <graphMeta />

<model>

<transform>

<outputs>

<its>

<Item>

  <k>1</k>

  <id>VARIABLES_89670</id>

  <n>Variables</n>

  <vis>1</vis>

<slices>

<its>

<Item>

  <k>0</k>

  <id>MyOutputVavable|A</id>

  <n>MyOutputVavable|A</n>

  <vis>1</vis>

  <variableKey>1</variableKey>

  <stubKey>89671</stubKey>

<selections>

<its>

<Item>

<id>

  <id>89683</id>

  </id>

  <variant>2</variant>

  </Item>

  </its>

  </selections>

  <aggregator>None</aggregator>

<parametrizedDimensions>

<its>

<Item>

  <dimension>0</dimension>

  <parameter>0</parameter>

  </Item>

  </its>

  </parametrizedDimensions>

<unitInfo>

  <unit>4294967295</unit>

  <measure>4294967295</measure>

  <baseUnit>4294967295</baseUnit>

  <unitsDimensionKey>0</unitsDimensionKey>

  </unitInfo>

  <level>Year</level>

  </Item>

  </its>

  </slices>

  <variableStubKey>89671</variableStubKey>

  <parameterID />

  <kind>Stub</kind>

  <attributeId />

  <attributeType>Series</attributeType>

  </Item>

  </its>

  </outputs>

  <formulaCount>1</formulaCount>

<formulas>

<its>

<it>

  <k>0</k>

  <kind>LinearRegression</kind>

<method>

<linearRegression>

  <constantMode>ManualEstimate</constantMode>

  <constantValue>0.5</constantValue>

  <confidenceLevel>0.85</confidenceLevel>

<ARMA>

<orderAR>

  <l>1</l>

  </orderAR>

  <orderMA />

  <calcInitMode>Auto</calcInitMode>

  <initAR />

  <initMA />

  <initIntercept>NaN</initIntercept>

  <estimationMethod>LevenbergMarquardt</estimationMethod>

  <tolerance>0.0001</tolerance>

  <maxIteration>500</maxIteration>

<coefficientsAR>

  <estimate />

  <standardError />

  <tStatistic />

  <probability />

  </coefficientsAR>

<coefficientsMA>

  <estimate />

  <standardError />

  <tStatistic />

  <probability />

  </coefficientsMA>

  <diff>0</diff>

  <diffSeas>1</diffSeas>

  <orderARSeas />

  <orderMASeas />

  <initARSeas />

  <initMASeas />

  <periodSeas>0</periodSeas>

<coefficientsARSeas>

  <estimate />

  <standardError />

  <tStatistic />

  <probability />

  </coefficientsARSeas>

<coefficientsMASeas>

  <estimate />

  <standardError />

  <tStatistic />

  <probability />

  </coefficientsMASeas>

  <useARMAasInstrums>1</useARMAasInstrums>

  <useAnalyticDeriv>1</useAnalyticDeriv>

  <useBackCast>1</useBackCast>

  </ARMA>

<missingData>

  <specifiedVector />

  <method>Casewise</method>

  <methodParameter>5</methodParameter>

  <specifiedValue>0</specifiedValue>

<specifiedTerm>

  <k>4294967295</k>

  </specifiedTerm>

  </missingData>

<coefficients>

  <d>-0.08396406739747959</d>

  </coefficients>

<pairCorrelationMatrix>

<data>

  <d>1</d>

  <d>-0.7626332420071756</d>

  <d>-0.7626332420071756</d>

  <d>1</d>

  </data>

  </pairCorrelationMatrix>

<armaCoefficients>

<orderAR>

  <l>1</l>

  </orderAR>

  <orderMA />

<coefficientsAR>

<estimate>

  <d>0.8682687093029638</d>

  </estimate>

<standardError>

  <d>0.07799376856974333</d>

  </standardError>

<tStatistic>

  <d>11.13253949931325</d>

  </tStatistic>

<probability>

  <d>2.867720505506099e-10</d>

  </probability>

  </coefficientsAR>

<coefficientsMA>

  <estimate />

  <standardError />

  <tStatistic />

  <probability />

  </coefficientsMA>

  <orderARSeas />

  <orderMASeas />

<coefficientsARSeas>

  <estimate />

  <standardError />

  <tStatistic />

  <probability />

  </coefficientsARSeas>

<coefficientsMASeas>

  <estimate />

  <standardError />

  <tStatistic />

  <probability />

  </coefficientsMASeas>

  </armaCoefficients>

<pdlStatCoefficients>

  <estimatesSum>0</estimatesSum>

  <stdErrSum>0</stdErrSum>

  <tStatSum>0</tStatSum>

  </pdlStatCoefficients>

<statCoefficients>

<intercept>

  <mode>ManualEstimate</mode>

  <estimate>0.5</estimate>

  <standardError>NaN</standardError>

  <tStatistic>NaN</tStatistic>

  <probability>NaN</probability>

  </intercept>

<coefficients>

<estimate>

  <d>-0.08396406739747959</d>

  </estimate>

<standardError>

  <d>0.02130633695023525</d>

  </standardError>

<tStatistic>

  <d>-3.94080256937608</d>

  </tStatistic>

<probability>

  <d>0.0007483835396404626</d>

  </probability>

  </coefficients>

  </statCoefficients>

  </linearRegression>

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

<series>

<input>

  <k>4294967295</k>

  <lag>0</lag>

<inversion>

  <type>None</type>

  <lag>PrecidingValue</lag>

  <previousLag>-1</previousLag>

  <seasonality>None</seasonality>

  <dependence>Linear</dependence>

  <K>3</K>

  </inversion>

<slice>

  <k>0</k>

  <id>MyOutputVavable|A</id>

  <n>MyOutputVavable|A</n>

  <vis>1</vis>

  <variableKey>1</variableKey>

  <stubKey>89671</stubKey>

<selections>

<its>

<Item>

<id>

  <id>89683</id>

  </id>

  <variant>2</variant>

  </Item>

  </its>

  </selections>

  <aggregator>None</aggregator>

<parametrizedDimensions>

<its>

<Item>

  <dimension>0</dimension>

  <parameter>0</parameter>

  </Item>

  </its>

  </parametrizedDimensions>

<unitInfo>

  <unit>4294967295</unit>

  <measure>4294967295</measure>

  <baseUnit>4294967295</baseUnit>

  <unitsDimensionKey>0</unitsDimensionKey>

  </unitInfo>

  <level>Year</level>

  </slice>

  <date>1899-12-30</date>

  </input>

<addFactor>

  <k>4294967295</k>

  </addFactor>

  </series>

<evaluateSeries>

<its>

<Item>

  <scenarioKey>4294967295</scenarioKey>

<fact>

  <d>3</d>

  <d>7</d>

  <d>2</d>

  <d>6</d>

  <d>4</d>

  <d>1</d>

  <d>5</d>

  <d>3.42857143</d>

  <d>3.28571429</d>

  <d>3.14285714</d>

  <d>3</d>

  <d>2.85714286</d>

  <d>2.71428571</d>

  <d>2.57142857</d>

  <d>2.42857143</d>

  <d>2.28571429</d>

  <d>2.14285714</d>

  <d>2</d>

  <d>1.85714286</d>

  <d>1.71428571</d>

  <d>1.57142857</d>

  <d>1.42857143</d>

  <d>1.28571429</d>

  <d>1.14285714</d>

  <d>1</d>

  <d>0.85714286</d>

  <d>0.71428571</d>

  <d>0.57142857</d>

  <d>NaN</d>

  </fact>

<modelling>

  <d>NaN</d>

  <d>6.796910634289292</d>

  <d>1.14416876286019</d>

  <d>4.975957312453287</d>

  <d>4.442458519583021</d>

  <d>3.514234055349136</d>

  <d>0.9217015149591798</d>

  <d>4.40704993968997</d>

  <d>3.0549012709732</d>

  <d>2.943136473929621</d>

  <d>2.831371668203355</d>

  <d>2.71960687115977</d>

  <d>2.607842074116201</d>

  <d>2.496077268389927</d>

  <d>2.384312471346351</d>

  <d>2.272547674302773</d>

  <d>2.160782877259189</d>

  <d>2.049018071532927</d>

  <d>1.937253274489343</d>

  <d>1.825488477445767</d>

  <d>1.713723671719501</d>

  <d>1.601958874675926</d>

  <d>1.490194077632337</d>

  <d>1.378429280588761</d>

  <d>1.266664474862499</d>

  <d>NaN</d>

  <d>NaN</d>

  <d>NaN</d>

  <d>NaN</d>

  </modelling>

<residuals>

  <d>NaN</d>

  <d>0.2030893657107082</d>

  <d>0.8558312371398105</d>

  <d>1.024042687546713</d>

  <d>-0.4424585195830213</d>

  <d>-2.514234055349136</d>

  <d>4.07829848504082</d>

  <d>-0.9784785096899702</d>

  <d>0.2308130190268001</d>

  <d>0.199720666070379</d>

  <d>0.1686283317966448</d>

  <d>0.1375359888402303</d>

  <d>0.106443635883799</d>

  <d>0.07535130161007286</d>

  <d>0.04425895865364904</d>

  <d>0.01316661569722655</d>

  <d>-0.01792573725918922</d>

  <d>-0.04901807153292737</d>

  <d>-0.0801104144893432</d>

  <d>-0.1112027674457667</d>

  <d>-0.1422951017195007</d>

  <d>-0.1733874446759258</d>

  <d>-0.2044797876323372</d>

  <d>-0.2355721405887614</d>

  <d>-0.2666644748624989</d>

  <d>NaN</d>

  <d>NaN</d>

  <d>NaN</d>

  <d>NaN</d>

  </residuals>

<input>

  <d>3</d>

  <d>7</d>

  <d>2</d>

  <d>6</d>

  <d>4</d>

  <d>1</d>

  <d>5</d>

  <d>3.42857143</d>

  <d>3.28571429</d>

  <d>3.14285714</d>

  <d>3</d>

  <d>2.85714286</d>

  <d>2.71428571</d>

  <d>2.57142857</d>

  <d>2.42857143</d>

  <d>2.28571429</d>

  <d>2.14285714</d>

  <d>2</d>

  <d>1.85714286</d>

  <d>1.71428571</d>

  <d>1.57142857</d>

  <d>1.42857143</d>

  <d>1.28571429</d>

  <d>1.14285714</d>

  <d>1</d>

  <d>0.85714286</d>

  <d>0.71428571</d>

  <d>0.57142857</d>

  <d>NaN</d>

  <d>NaN</d>

  <d>NaN</d>

  </input>

<factors>

<its>

<it>

  <termToText>{MyInputVavable[t+1]}</termToText>

<serie>

  <d>32</d>

  <d>16</d>

  <d>56</d>

  <d>60</d>

  <d>72.8</d>

  <d>85.59999999999999</d>

  <d>98.40000000000001</d>

  <d>111.2</d>

  <d>124</d>

  <d>136.8</d>

  <d>149.6</d>

  <d>162.4</d>

  <d>175.2</d>

  <d>188</d>

  <d>200.8</d>

  <d>213.6</d>

  <d>226.4</d>

  <d>239.2</d>

  <d>252</d>

  <d>264.8</d>

  <d>277.6</d>

  <d>290.4</d>

  <d>303.2</d>

  <d>316</d>

  <d>328.8</d>

  <d>NaN</d>

  <d>NaN</d>

  <d>NaN</d>

  <d>NaN</d>

  <d>NaN</d>

  <d>NaN</d>

  </serie>

<transformedSerie>

  <d>-28.54510344827582</d>

  <d>-52.73702463054184</d>

  <d>-20.92894581280785</d>

  <d>-25.12086699507387</d>

  <d>-20.51278817733989</d>

  <d>-15.90470935960589</d>

  <d>-11.29663054187189</d>

  <d>-6.688551724137909</d>

  <d>-2.080472906403926</d>

  <d>2.527605911330056</d>

  <d>7.135684729064025</d>

  <d>11.74376354679805</d>

  <d>16.35184236453202</d>

  <d>20.95992118226602</d>

  <d>25.56800000000001</d>

  <d>30.17607881773398</d>

  <d>34.78415763546798</d>

  <d>39.39223645320195</d>

  <d>44.00031527093594</d>

  <d>48.60839408866994</d>

  <d>53.21647290640394</d>

  <d>57.82455172413788</d>

  <d>62.43263054187187</d>

  <d>67.0407093596059</d>

  <d>71.6487881773399</d>

  <d>NaN</d>

  <d>NaN</d>

  <d>NaN</d>

  <d>NaN</d>

  <d>NaN</d>

  <d>NaN</d>

  </transformedSerie>

<inversion>

  <type>TS</type>

  <lag>PrecidingValue</lag>

  <previousLag>-1</previousLag>

  <seasonality>None</seasonality>

  <dependence>Linear</dependence>

  <K>3</K>

  </inversion>

  <entryKey>1</entryKey>

  </it>

  </its>

  </factors>

  </Item>

  </its>

<dates>

  <it>1990A1</it>

  <it>1991A1</it>

  <it>1992A1</it>

  <it>1993A1</it>

  <it>1994A1</it>

  <it>1995A1</it>

  <it>1996A1</it>

  <it>1997A1</it>

  <it>1998A1</it>

  <it>1999A1</it>

  <it>2000A1</it>

  <it>2001A1</it>

  <it>2002A1</it>

  <it>2003A1</it>

  <it>2004A1</it>

  <it>2005A1</it>

  <it>2006A1</it>

  <it>2007A1</it>

  <it>2008A1</it>

  <it>2009A1</it>

  <it>2010A1</it>

  <it>2011A1</it>

  <it>2012A1</it>

  <it>2013A1</it>

  <it>2014A1</it>

  <it>2015A1</it>

  <it>2016A1</it>

  <it>2017A1</it>

  <it>2018A1</it>

  <it>2019A1</it>

  <it>2020A1</it>

  </dates>

  </evaluateSeries>

<inversionInfo>

  <type>None</type>

  <lag>PrecidingValue</lag>

  <previousLag>-1</previousLag>

  <seasonality>None</seasonality>

  <dependence>Linear</dependence>

  <K>3</K>

  </inversionInfo>

  <doUseR>0</doUseR>

  <supportsR>1</supportsR>

  </method>

  <calendarLevel>Year</calendarLevel>

  <outputSliceKey>0</outputSliceKey>

  </it>

  </its>

  </formulas>

  <displayId>0</displayId>

<series>

  <its />

  </series>

  <kind>Simple</kind>

<displaySettings>

  <displayTermsAs>Derive</displayTermsAs>

  </displaySettings>

  <additionalAttributes />

  <calculationType>Serie</calculationType>

  <calculationDirection>Forward</calculationDirection>

  </transform>

  <stochastic>1</stochastic>

  <calculationPeriod>Forecast</calculationPeriod>

  <useModelPeriod>1</useModelPeriod>

  <useExistingData>0</useExistingData>

  <treatNullsAsZeros>0</treatNullsAsZeros>

  <autoName>1</autoName>

<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>

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  </model>

  </Item>

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<visualController>

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  </visualController>

<suppressEmptyFilter>

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  <suppressEmptyArea>SerieBounds</suppressEmptyArea>

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  <variableTestUseR>0</variableTestUseR>

  <calculateIdentOnFact>0</calculateIdentOnFact>

  </metamodel>

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  <readOnly>0</readOnly>

  </problemMd>

  </item>

  </meta>

  </GetMsResult>

  </soapenv:Body>

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JSON-запрос:

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  {
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    {
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      {
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       "item" : 
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          {
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                   "equationsFormula" : 
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                   "transformVariable" : 
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       "execStatCoefficients" : "true"
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}

JSON-ответ:

{
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      }
    }
  }
}

public static MsCalculationChainEntry GetMsLinearRegr(MsId ms, ulong modelKey, ulong eqKey)
        {
    var getMsOp = new GetMs();
    // Задаём параметры выполнения операции
    getMsOp.tMs = ms;
    getMsOp.tArg = new GetMsArg()
    {  // Задаём шаблон извлечения данных
        pattern = new MsMdPattern()
        {
            obInst = false,
            all = false,
            item = new MsItemPattern()
            {  // Указываем ключ рассчитываемой модели
                key = modelKey,
                problem = new MsProblemPattern()
                {
                    metamodel = new MsMetaModelPattern()
                    {
                        calculationChain = ListOperation.Get,
                        calcChainPattern = new MsCalculationChainPattern()
                        {
                            // Указываем ключ рассчитываемого уравнения
                            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
                                        }
                                    }
                                }
                            }
                        }
                    }
                }
            }
        }, // Задаём параметры расчёта уравнения
        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 }
        }
    };
    // Создаем прокси-объект для выполнения операции
    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;
    // Выводим ошибки и предупреждения
    MsModel eq = chainEntry.model;
    if (eq.warnings != null)
    {
        Console.WriteLine("-- Предупреждения --");
        Console.WriteLine("".PadRight(3) + printArray(eq.warnings));
    }
    if (eq.error == null) //Проверяем, возникли ли ошибки при расчёте уравнения
    {   // Уравнение рассчитано без ошибок, получаем результаты расчёта   
        TsFormula formula = eq.transform.formulas.its.GetValue(0) as TsFormula;
        TsLinearRegressionMethod linR = formula.method.linearRegression;
        Console.WriteLine("-- Значения коэффициентов уравнения --");
        Console.WriteLine("".PadRight(3) + printArray(linR.coefficients));
        // Получаем значения коэффициентов уравнения и выводим в окно консоли
        PrintCoef(linR.statCoefficients);
        // Получаем значения коэффициентов авторегрессии, скользящего среднего и выводим в окно консоли
        printARMA(linR.armaCoefficients);
        Console.WriteLine("-- Матрица корреляции --");
        MsPairCorrelationMatrix pm = linR.pairCorrelationMatrix;
        if (pm.error != null) { Console.WriteLine("".PadRight(3) + "ошибка: " + pm.error); }
        Console.WriteLine("".PadRight(3) + printArray(pm.data));
        Console.WriteLine("-- Коэффициенты лаговых переменных --");
        MsPDLCoefficients pdl = linR.pdlStatCoefficients;
        printPDL(pdl);
        // Выводим ряды, которые были рассчитаны
        Console.WriteLine("-- Доступные ряды данных --");
        printSeries(formula.method.series);
        // Получаем рассчитанные значения и выводим в окно консоли
        MsEvaluateSeriesResult seriesRes = formula.method.evaluateSeries;
        printSeries(seriesRes);
    }
    else // При расчёте уравнения возникли ошибки
    {
        Console.WriteLine("-- Ошибки --");
        Console.WriteLine(eq.error);
    }
    // Возвращаем модель, содержащую уравнение
    return chainEntry;
}
// Процедура вывода значений коэффициентов для лаговых переменных
public static void printPDL(MsPDLCoefficients pdl) 
{
    Console.WriteLine("".PadRight(3) + " - cумма коэффициентов: " + pdl.estimatesSum);
    Console.WriteLine("".PadRight(3) + " - cумма стандартных ошибок: " + pdl.stdErrSum);
    Console.WriteLine("".PadRight(3) + " - cумма t-статистик: " + pdl.tStatSum);
    Console.WriteLine("".PadRight(3) + " - Коэффициенты -"); 
    PrintStatCoef(6, pdl as StatCoefficients);
}
// Процедура вывода идентификаторов рассчитанных рядов
public static void printSeries(MsMethodSeries series) 
{
    if (series.input != null)
        {Console.WriteLine("".PadRight(3) + " - входной ряд");};
    if (series.fitted != null)
        { Console.WriteLine("".PadRight(3) + " - сглаженный ряд"); };
    if (series.forecast != null)
        { Console.WriteLine("".PadRight(3) + " - прогнозный ряд"); };
    if (series.residuals != null)
        { Console.WriteLine("".PadRight(3) + " - ряд остатков"); };
    if (series.lowerConfidenceLevel != null)
        { Console.WriteLine("".PadRight(3) + " - нижняя доверительная граница"); };
    if (series.upperConfidenceLevel != null)
        { Console.WriteLine("".PadRight(3) + " - верхняя доверительная граница"); };
    if (series.dynamicLowerConfidenceLevel != null)
        { Console.WriteLine("".PadRight(3) + " - нижняя динамическая доверительная граница"); };
    if (series.dynamicUpperConfidenceLevel != null)
        { Console.WriteLine("".PadRight(3) + " - верхняя динамическая доверительная граница"); };
    if (series.addFactor != null)
        { Console.WriteLine("".PadRight(3) + " - фактор корректировки прогноза"); };
}