SOAP-запрос:
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</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>
<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-запрос:
{
"GetMs" :
{
"tMs" :
{
"id" : "IEHFOKNBPENBFOAEDMNIFBMAGBJFMILEEKEMODKJPMDBDAAN!M!S!CKILIGLNBPENBFOAEGCFPILNFBENOEAEENLANPEIMKOMICGHG"
},
"tArg" :
{
"pattern" :
{
"obInst" : "false",
"all" : "false",
"item" :
{
"key" : "89669",
"problem" :
{
"metamodel" :
{
"calculationChain" : "Get",
"calcChainPattern" :
{
"modelPattern" :
{
"transform" :
{
"outputs" : "Get",
"formulaCount" : "true",
"formulas" :
{
"method" :
{
"series" : "true"
}
},
"displayId" : "true",
"equationsFormula" :
{
"method" : ""
},
"series" : "Get",
"kind" : "true",
"displaySettings" : "true",
"additionalAttributes" : "true",
"calculationType" : "true",
"calculationDirection" : "true",
"transformVariable" :
{
"slices" : "Get",
"transformSlice" :
{
"selection" : "Get"
}
}
},
"stochastic" : "true",
"calculationPeriod" : "true",
"useModelPeriod" : "true",
"useExistingData" : "true",
"treatNullsAsZeros" : "true",
"autoName" : "true",
"generatedName" : "true",
"period" : "true",
"isExclusive" : "true",
"useAutoPeriod" : "true"
},
"entryKeys" :
{
"l" : "4"
}
}
}
}
}
},
"execParams" :
{
"k" : "0",
"modelKeys" :
{
"l" : "4"
},
"pdlIndex" : "1",
"scenarioKeys" : "",
"execMethod" : "true",
"execCoefficients" : "true",
"execEvaluateSeries" : "true",
"execPairCorrelationMatrix" : "true",
"execARMACoefficients" : "true",
"execPDLStatCoefficients" : "true",
"execStatCoefficients" : "true"
}
}
}
}
JSON-ответ:
{
"GetMsResult" :
{
"id" :
{
"id" : "IEHFOKNBPENBFOAEDMNIFBMAGBJFMILEEKEMODKJPMDBDAAN!M!S!CKILIGLNBPENBFOAEGCFPILNFBENOEAEENLANPEIMKOMICGHG"
},
"meta" :
{
"item" :
{
"k" : "89669",
"id" : "MODEL_NEW",
"n" : "MODEL_NEW",
"vis" : "1",
"type" : "Problem",
"problemMd" :
{
"metamodel" :
{
"k" : "89670",
"calculationChain" :
{
"its" :
{
"Item" :
{
"k" : "4",
"id" : "OBJ4",
"n" : "MyOutputVavable|A[t] = A0 + PDL(TS(MyInputVavable[t+1] * 8, Linear), 1, 1) + [AR(1)=A1], (От родителя)-(От родителя)",
"vis" : "1",
"type" : "Model",
"excluded" : "0",
"graphMeta" : "",
"model" :
{
"transform" :
{
"outputs" :
{
"its" :
{
"Item" :
{
"k" : "1",
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"n" : "Variables",
"vis" : "1",
"slices" :
{
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{
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{
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{
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{
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"kind" : "Stub",
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"attributeType" : "Series"
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}
},
"formulaCount" : "1",
"formulas" :
{
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{
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[
{
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"method" :
{
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{
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"constantValue" : "0.5",
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"ARMA" :
{
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{
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},
"orderMA" : "",
"calcInitMode" : "Auto",
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"initMA" : "",
"initIntercept" : "NaN",
"estimationMethod" : "LevenbergMarquardt",
"tolerance" : "0.0001",
"maxIteration" : "500",
"coefficientsAR" :
{
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"standardError" : "",
"tStatistic" : "",
"probability" : ""
},
"coefficientsMA" :
{
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"standardError" : "",
"tStatistic" : "",
"probability" : ""
},
"diff" : "0",
"diffSeas" : "1",
"orderARSeas" : "",
"orderMASeas" : "",
"initARSeas" : "",
"initMASeas" : "",
"periodSeas" : "0",
"coefficientsARSeas" :
{
"estimate" : "",
"standardError" : "",
"tStatistic" : "",
"probability" : ""
},
"coefficientsMASeas" :
{
"estimate" : "",
"standardError" : "",
"tStatistic" : "",
"probability" : ""
},
"useARMAasInstrums" : "1",
"useAnalyticDeriv" : "1",
"useBackCast" : "1"
},
"missingData" :
{
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"method" : "Casewise",
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"specifiedTerm" :
{
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}
},
"coefficients" :
{
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},
"pairCorrelationMatrix" :
{
"data" :
{
"d" :
[
"1",
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"-0.7626332420071756",
"1"
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}
},
"armaCoefficients" :
{
"orderAR" :
{
"l" : "1"
},
"orderMA" : "",
"coefficientsAR" :
{
"estimate" :
{
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},
"standardError" :
{
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"tStatistic" :
{
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},
"probability" :
{
"d" : "2.867720505506099e-10"
}
},
"coefficientsMA" :
{
"estimate" : "",
"standardError" : "",
"tStatistic" : "",
"probability" : ""
},
"orderARSeas" : "",
"orderMASeas" : "",
"coefficientsARSeas" :
{
"estimate" : "",
"standardError" : "",
"tStatistic" : "",
"probability" : ""
},
"coefficientsMASeas" :
{
"estimate" : "",
"standardError" : "",
"tStatistic" : "",
"probability" : ""
}
},
"pdlStatCoefficients" :
{
"estimatesSum" : "0",
"stdErrSum" : "0",
"tStatSum" : "0"
},
"statCoefficients" :
{
"intercept" :
{
"mode" : "ManualEstimate",
"estimate" : "0.5",
"standardError" : "NaN",
"tStatistic" : "NaN",
"probability" : "NaN"
},
"coefficients" :
{
"estimate" :
{
"d" : "-0.08396406739747959"
},
"standardError" :
{
"d" : "0.02130633695023525"
},
"tStatistic" :
{
"d" : "-3.94080256937608"
},
"probability" :
{
"d" : "0.0007483835396404626"
}
}
}
},
"name" : "0.5000 + PDL(X1, 1, 1) + [AR(1)=0.8683]",
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{
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{
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"lag" : "0",
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{
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"seasonality" : "None",
"dependence" : "Linear",
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},
"slice" :
{
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"id" : "MyOutputVavable|A",
"n" : "MyOutputVavable|A",
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{
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{
"Item" :
{
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{
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}
},
"aggregator" : "None",
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{
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{
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{
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}
}
},
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{
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},
"level" : "Year"
},
"date" : "1899-12-30"
},
"addFactor" :
{
"k" : "4294967295"
}
},
"evaluateSeries" :
{
"its" :
{
"Item" :
{
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"fact" :
{
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[
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},
"modelling" :
{
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]
},
"residuals" :
{
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[
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},
"input" :
{
"d" :
[
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]
},
"factors" :
{
"its" :
{
"it" :
[
{
"termToText" : "{MyInputVavable[t+1]}",
"serie" :
{
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[
"32",
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"transformedSerie" :
{
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[
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"inversion" :
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"type" : "TS",
"lag" : "PrecidingValue",
"previousLag" : "-1",
"seasonality" : "None",
"dependence" : "Linear",
"K" : "3"
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]
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}
}
},
"dates" :
{
"it" :
[
"1990A1",
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"1992A1",
"1993A1",
"1994A1",
"1995A1",
"1996A1",
"1997A1",
"1998A1",
"1999A1",
"2000A1",
"2001A1",
"2002A1",
"2003A1",
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"2014A1",
"2015A1",
"2016A1",
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]
}
},
"inversionInfo" :
{
"type" : "None",
"lag" : "PrecidingValue",
"previousLag" : "-1",
"seasonality" : "None",
"dependence" : "Linear",
"K" : "3"
},
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},
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}
]
}
},
"displayId" : "0",
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{
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},
"kind" : "Simple",
"displaySettings" :
{
"displayTermsAs" : "Derive"
},
"additionalAttributes" : "",
"calculationType" : "Serie",
"calculationDirection" : "Forward"
},
"stochastic" : "1",
"calculationPeriod" : "Forecast",
"useModelPeriod" : "1",
"useExistingData" : "0",
"treatNullsAsZeros" : "0",
"autoName" : "1",
"period" :
{
"identificationStartDate" : "1990-01-01",
"identificationEndDate" : "2018-04-24",
"forecastStartDate" : "2018-04-25",
"forecastEndDate" : "2020-01-01",
"identificationStartDateParamID" : "",
"identificationEndDateParamID" : "",
"forecastStartDateParamID" : "",
"forecastEndDateParamID" : "",
"autoPeriod" : "28672",
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"identificationEndOffset" : "0",
"forecastEndOffset" : "0",
"isIdentStartCorrect" : "1",
"isIdentEndCorrect" : "1",
"isForecastEndCorrect" : "1"
},
"isExclusive" : "1",
"useAutoPeriod" : "1",
"generatedName" : "MyOutputVavable|A[t] = A0 + PDL(TS(MyInputVavable[t+1] * 8, Linear), 1, 1) + [AR(1)=A1]",
"warnings" : "",
"readOnly" : "0"
}
}
}
},
"visualController" :
{
"userRPath" : "",
"isRExist" : "0"
},
"suppressEmptyFilter" :
{
"suppressEmpty" : "0",
"suppressEmptyArea" : "SerieBounds"
},
"readOnly" : "0",
"variableTestUseR" : "0",
"calculateIdentOnFact" : "0"
},
"useScenarios" : "0",
"readOnly" : "0"
}
}
}
}
}
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"); };
}