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ID:40376166
大小:905.79 KB
页数:47页
时间:2019-08-01
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1、TimeSeriesForecastingMatiasG.EnzTimeSeriesForecastingQuickreviewofforecastingDifferentApproachestoForecastingIntroductiontotimeseriesMeasuringForecastErrorDescribingatimeseriesForecastingmethodsforleveldemandWhatisForecasting?Forecastingisthepredi
2、ction,projectionorestimationoftheoccurrencesofuncertainfutureeventsorlevelsofactivity.Examples:WeatherthisweekendStockpricesNextmonth’ssalesinregionX…ResultsofpoorforecastingFundamentalForecastingApproachesWhatdoesthefuturehold?QualitativeRelatio
3、nalTimeSeriesTimeSeriesApproachesConsistsofdatathatarecollected,recorded,orobservedoversuccessiveincrementsoftimeAssumespastbehaviorisgoingtoberepeatedinthefutureSearchforpatternsinthedata--decompositionintodifferentcomponentsManytimeseriestechnique
4、sDescribingaTimeSeriesWecanthinkofahistoricaltimeseriesasbeingcomposedof“pattern”and“noise.”the“pattern”canincludestationarity,trend,seasonality,oracycle,the“noise”israndomwithzeromeanandseriallyuncorrelatedThegoalofforecastingistoforecastthepattern.
5、Noiseisrandomandcan’tbeforecasted.ComponentsoftheTimeSeriesOriginaltimeseriesDecomposedtimeseriesCyclecycleSeasonalseasonalityDemandDemandTrendtrendLevellevelTimeNoisenoiseTimeDemandcanbeexpressedasafunctionofthesecomponents,forexample,D(t)=(L+Tt)[S(t)]
6、+rChartstakenfrom“OperationsManagement,”R.G.Schroeder,IrwinMcGraw-Hill,2000ForecastOptimalityAforecastisoptimalif:allpatternhasbeendiscoveredonlyerrorcomesfromthenoisetermForecastfitreferstohowtheforecastmatches-upagainstpastdataover-fitting(forecas
7、tingtherandomness)isbadForecastErrorAmeaningfulforecastconsistsof2values:Pointestimate+errorForecasterrorinformationisusedfor:SelectingforecastingmethodsEstimatingsafetystocksImprovingforecastingprocessEvaluatingriskSomeTimeSeriesTechniquesSimple
8、ComplexMovingaveragesSpectralanalysisExponentialBox-Jenkinssmoothing(ARIMA)DecompositionKalmanFiltersFourierAnalysis?EvaluatetheforecastusingnewPastdataisavailable.demanddataasitcomesin....DDDDDDD...t-3t-2t-1tt+1t+
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