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Data Science Course In Pune

https://www.google.com/maps/place/ExcelR Solutions - Data Science Training Course | Digital Marketing Course in Pune/@18.5584617,73.7889225,17z/data=!3m1!4b1!4m5!3m4!1s0x3bc2bf37817aae43:0x6c49e2eda8b01c77!8m2!3d18.5584566!4d73.7911112<br>Data science Course in Pune has been the in demand and Excelr is serving with experienced faculty in industry, with placement assistance and 24x7 support.

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Data Science Course In Pune

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  1. Forecasting Time Series

  2. My Introduction Name:BharaniKumar Educa+on:IITHyderabad IndianSchoolofBusiness Professionalcer+fica+ons: PMP PMI-ACP PMI-RMP CSM LSSGB LSSBB SSMBB ITIL AgilePM ProjectManagementProfessional AgileCer4fiedPrac44oner RiskManagementProfessional Cer4fiedScrumMaster LeanSixSigmaGreenBelt LeanSixSigmaBlackBelt SixSigmaMasterBlackBelt Informa4onTechnologyInfrastructureLibrary DynamicSystemDevelopmentMethodologyAtern © 2013 ExcelR Solutions. All Rights Reserved

  3. My Introduction 4 RESEARCHin ANALYTICS,DEEP LEARNING&IOT DATASCIENTIST 3 2 DeloiHe DrivenusingUSpolicies 1 Infosys DrivenusingIndianpoliciesunderLargeenterprises ITCInfotech DrivenusingIndianpoliciesSME HSBC DrivenusingUKpolicies © 2013 ExcelR Solutions. All Rights Reserved

  4. AGENDA WhyForecas4ng Learnaboutthevariousexamplesof forecasHng Forecas4ngStrategy Learnaboutdecomposing,forecasHng &combining EDA&Graphical Representa4on Forecas4ng Strategy Forecas4ng Decomposi4on components EDA&GraphicalRepresenta4on Learnaboutexploratorydataanalysis, scaKerplot,Hmeplot,lagplot,ACFplot Why Forecas4ng? Forecas4ngcomponents LearnaboutLevel,Trend,Seasonal, Cyclical,Randomcomponents Forecas4ngModels&Errors LearnaboutvariousforecasHng modelstobediscussed&thevarious errormeasures AGENDA

  5. Why Forecasting Whyforecast,whenyouwouldknowtheoutcomeeventually? Earlyknowledgeisthekey,evenifthatknowledgeisimperfect –  ForseQngproducHonschedules,oneneedstoforecastsales –  Forstaffingofcallcenters,acompanyneedstoforecastthedemandforservice –  Fordealingwithepidemicemergencies,naHonsshouldforecastthevariousflu •  •  © 2013 ExcelR Solutions. All Rights Reserved

  6. Types of forecast ShortTerm orLongTerm MicroScale orMacro Scale Density Forecast Point Forecast Interval Forecast Forecas4ng Classifica4on Qualita4veor Quan4ta4ve Dataor Judgment © 2013 ExcelR Solutions. All Rights Reserved

  7. Who generates Forecast? © 2013 ExcelR Solutions. All Rights Reserved

  8. Who generates Forecast? © 2013 ExcelR Solutions. All Rights Reserved

  9. Time series vs Cross-sectional data 01 Cross-sec4onalData 02 TimeSeriesData © 2013 ExcelR Solutions. All Rights Reserved

  10. Dataset for further discussion Month Jan-91 Feb-91 Mar-91 Apr-91 May-91 Jun-91 Jul-91 Aug-91 Sep-91 Oct-91 Nov-91 Dec-91 Jan-92 Feb-92 Mar-92 Apr-92 May-92 Jun-92 Footfall in thousands 1709 1621 1973 1812 1975 1862 1940 2013 1596 1725 1676 1814 1615 1557 1891 1956 1885 1623 t=1,2,3,…....=Hmeperiodindex Yt=valueoftheseriesatHmeperiodt Yt+k=forecastforHmeperiodt+k,given dataunHlHmet et=forecasterrorforperiodt MonthlyFooWallsofcustomersfrom Jan1991toMarch2004 © 2013 ExcelR Solutions. All Rights Reserved

  11. Forecasting Strategy 01 02 03 04 05 06 07 08 DefineGoal DataCollec4on Explore&VisualizeSeries Pre-ProcessData Par44onSeries ApplyForecas4ngMethod(s) Evaluate&ComparePerformance ImplementForecasts/System © 2013 ExcelR Solutions. All Rights Reserved

  12. Forecasting Strategy – Step 1 #1 Is the goal descriptive or predictive? #2 What is the forecast horizon? • DescripHve=TimeSeries Analysis • Howfarintothefuture?kinYt+k • RollingforwardoratsingleHme point? • PredicHve=TimeSeries ForecasHng Define Goal #4 Forecasting expertise & automation #3 How will the forecast be used? • In-houseforecasHngor consultants? • Whoarethestakeholders? • Numericaloreventforecast? • Howmanyseries?Howofen? • Costofover-predicHon& under-predicHon • Data&sofwareavailability © 2013 ExcelR Solutions. All Rights Reserved

  13. Forecasting Strategy – Step 2 #1DataQuality #2TemporalFrequency • Typicallysmallsample,soneed goodquality • Datasameasseriestobe forecasted • Shouldweusereal-HmeHcket collecHondata? • Balancebetweensignal&noise • AggregaHon/DisaggregaHon Data Collec-on #4.Domainexper4se #3SeriesGranularity? • NecessaryinformaHonsource • Affectsmodelingprocessfromstart toend • LevelofcommunicaHon/ coordinaHonbetweenforecasters& domainexperts • Coverageofthedata– Geographical,populaHon,Hme,… • Shouldbealignedwithgoal © 2013 ExcelR Solutions. All Rights Reserved

  14. Forecasting Strategy Step3 (Explore Series) Season al PaHern s Addi4ve: Yt=Level+Trend+Seasonality+Noise Mul4plica4ve: Yt=LevelxTrendxSeasonalityxNoise Trend NON-SYSTEMATICPART Noise Level Seasonal PaHerns SYSTEMATIC PART © 2013 ExcelR Solutions. All Rights Reserved

  15. Trend Component •  Persistent,overallupwardordownwardpaKern •  DuetopopulaHon,technologyetc. •  OverallUpwardorDownwardMovement •  SeveralyearsduraHon Response Mo., Qtr., Yr. © 2013 ExcelR Solutions. All Rights Reserved

  16. Seasonal Component •  RegularpaKernofup&downfluctuaHons •  Duetoweather,customsetc. •  Occurswithinoneyear •  Example:Passengertrafficduring24hours Summer Response Mo., Qtr. © 2013 ExcelR Solutions. All Rights Reserved

  17. Irregular/Random/Noise Component •  ErraHc,unsystemaHc,‘residual’fluctuaHons •  DuetorandomvariaHonorunforeseenevents –  Unionstrike –  War •  ShortduraHon&nonrepeaHng © 2013 ExcelR Solutions. All Rights Reserved

  18. Time Series Components © 2013 ExcelR Solutions. All Rights Reserved

  19. Time Plot •  PlotsavariableagainstHmeindex •  Appropriateforvisualizingseriallycollecteddata(Hmeseries) •  Bringsoutmanyusefulaspectsofthestructureofthedata •  Example:ElectricalusageforWashingtonWaterPower (Quarterlydatafrom1980to1991) © 2013 ExcelR Solutions. All Rights Reserved

  20. Time plot ElectricalpowerusageforWashingtonWaterPower:1980-1991 1100 1000 Powerusage(KilowaHs) 900 800 700 600 500 400 1980 1982 1984 1986 1988 1990 Year © 2013 ExcelR Solutions. All Rights Reserved

  21. Observations •  Thereisacyclictrend •  Maximumdemandinfirstquarter;minimuminthirdquarter •  Theremayalsobeaslowlyincreasingtrend(tobeexamined) •  AnyreasonableforecastshouldhavecyclicfluctuaHons •  Trend(ifany)needtobeuHlizedforforecasHng •  Forecastwouldnotbeexact–therewouldbesomeerror © 2013 ExcelR Solutions. All Rights Reserved

  22. Time plot © 2013 ExcelR Solutions. All Rights Reserved

  23. Quarterly Sales of Ice-cream © 2013 ExcelR Solutions. All Rights Reserved

  24. Scatter Diagram Cost 859 682 471 708 1094 224 320 651 1049 Age 8 5 3 9 11 2 1 8 12 •  Plotsonevariableagainstanother •  OneofthesimplesttoolsforvisualizaHon ž  Example:MaintenancecostandAge forninebuses(SpokaneTransit) ž  Thisisanexampleofcross-secHonal data(observaHonscollectedinasingle pointofHme) © 2013 ExcelR Solutions. All Rights Reserved

  25. Scatter Plot 1200 Yearlycostofmaintenance(US$) 1000 800 600 400 200 0 0 2 4 6 Ageofbus 8 10 12 14 © 2013 ExcelR Solutions. All Rights Reserved

  26. Observations •  Olderbuseshavehighercostofmaintenance •  ThereissomevariaHon(casetocase) •  Theriseincostisabout$80peryearofage •  Itmaybepossibletouse‘age’toforecastmaintenance cost •  Forecastwouldnotbea‘certain’predicHon–therewould besomeerror © 2013 ExcelR Solutions. All Rights Reserved

  27. Lag plot •  Plotsavariableagainstitsownlaggedsample •  BringsoutpossibleassociaHonbetweensuccessivesamples •  Example:MonthlysaleofVCRsbyamusicstoreinayear =NumberofVCRssoldinHmeperiodt =NumberofVCRssoldinHmeperiodt–k © 2013 ExcelR Solutions. All Rights Reserved

  28. Example of lagged variables Number of VCRs sold in a month Time 1 123 2 130 3 125 4 138 5 145 6 142 7 141 8 146 9 147 10 157 11 150 12 160 Original Laggedonestep 123 130 125 138 145 142 141 146 147 157 150 Laggedtwosteps 123 130 125 138 145 142 141 146 147 157 © 2013 ExcelR Solutions. All Rights Reserved

  29. Lag plot (k = 1) ScaHerplotofVCRsaleswith1-steplaggedVCRsales 160 155 150 145 140 135 130 125 120 120 125 130 135 140 145 150 155 160 © 2013 ExcelR Solutions. All Rights Reserved

  30. Observations •  ThereisareasonabledegreeofassociaHon betweentheoriginalvariableandthelaggedone •  Valueoflaggedvariableisknownbeforehand,so itisusefulforpredicHon •  AssociaHonbetweenoriginalandlaggedvariable maybequan+fiedthroughacorrelaHon © 2013 ExcelR Solutions. All Rights Reserved

  31. Autocorrelation •  CorrelaHonbetweenavariableanditslagged version(oneHme-stepormore) =ObservaHoninHmeperiodt =ObservaHoninHmeperiodt–k =Meanofthevaluesoftheseries =AutocorrelaHoncoefficientfork-steplag © 2013 ExcelR Solutions. All Rights Reserved

  32. Standard error of rk •  Thestandarderroris The standard error of the mean esHmates the variability between samples whereas the standarddeviaHonmeasuresthevariabilitywithin asinglesample. •  Increasesprogressivelywithk,buteventuallyreachesa maximumvalue •  Ifthe‘true’autocorrelaHonis0,thentheesHmaterkshould beintheinterval(–2SE(rk),2SE(rk))95%oftheHme •  SomeHmesSE(rk)isapproximatedby © 2013 ExcelR Solutions. All Rights Reserved

  33. Correlogram or ACF plot •  PlotstheACForAutocorrelaHonfuncHon(rk) againstthelag(k) •  Plus-and-minustwo-standarderrorsare displayedaslimitstobeexceededforstaHsHcal significance •  RevealslaggedvariablesthatcanbepotenHally usefulforforecasHng © 2013 ExcelR Solutions. All Rights Reserved

  34. Correlogram for VCR data © 2013 ExcelR Solutions. All Rights Reserved

  35. ACF plot for electricity usage data © 2013 ExcelR Solutions. All Rights Reserved

  36. Observations •  Everyalternatesampleislarge,manyofthem staHsHcallysignificantalso •  ACFsatlags4,8,12,etcareposiHve •  ACFatlags2,6,10etcarenegaHve •  Allthesepickuptheseasonalaspectofthedata •  Thedatamaybere-examinedafer‘removing’ seasonality © 2013 ExcelR Solutions. All Rights Reserved

  37. ACF of de-seasoned KW data © 2013 ExcelR Solutions. All Rights Reserved

  38. Observations •  De-seasonedserieshassmallACFs •  ThispartofthedatahasliKleforecasHngvalue © 2013 ExcelR Solutions. All Rights Reserved

  39. Typical questions in exploratory analysis Is there a TREND? Alltheplotscontain informaHonregarding thesequesHons Is there a SEASONALITY? Are the data RANDOM? © 2013 ExcelR Solutions. All Rights Reserved

  40. Time series plots © 2013 ExcelR Solutions. All Rights Reserved

  41. Effect of omission of data on the Time series plot © 2013 ExcelR Solutions. All Rights Reserved

  42. Effect of omission of data on the Time series plot © 2013 ExcelR Solutions. All Rights Reserved

  43. Confusing kind of trend due to other type of scaling 20406080 20406080 y y 0 0 0 5 10 t 15 20 0 1 2 3 Log t 4.5 4.5 3.54 3.54 Log y Log y 2.53 2.53 0 5 10 t 15 20 0 1 2 3 Log t © 2013 ExcelR Solutions. All Rights Reserved

  44. Few points on Plots Plothelpsustosummarize&revealpaKerns indata GraphicshelpustoidenHfyanomaliesindata Plothelpsustopresentahugeamountofdatain smallspace&makeshugedatasetcoherent Togetalltheadvantagesofplot,the“AspectRaHo” ofplotisverycrucial TheraHoofHeighttoWidthofaplotiscalled theASPECTRATIO © 2013 ExcelR Solutions. All Rights Reserved

  45. Aspect Ratio •  GenerallyaspectraHoshouldbearound0.618 •  However,forlongHmeseriesdataaspectraHoshouldbe around0.25.TounderstandtheimpactofaspectraHoseethe twoplotsinthenexttwoslides © 2013 ExcelR Solutions. All Rights Reserved

  46. Aspect ratio © 2013 ExcelR Solutions. All Rights Reserved

  47. Aspect ratio © 2013 ExcelR Solutions. All Rights Reserved

  48. Preliminaries for Step 3 of 8-Step forecasting strategy Shouldweuseall historicaldatafor forecas4ng ? Solu4on=DATAPARTIONING Fitthemodelonlyto TRAININGperiod TrainingData Valida4onData Assessperformanceon VALIDATIONperiod © 2013 ExcelR Solutions. All Rights Reserved

  49. Partitioning DeploymodelbyjoiningTraining+ValidaHontoforecasttheFuture © 2013 ExcelR Solutions. All Rights Reserved

  50. How to choose a Validation Period? Forecast Horizon Seasonality Strategytochoose Valida4onData Period Lengthof series Underlying condi4ons affec4ngseries © 2013 ExcelR Solutions. All Rights Reserved

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