BigDataisalso BigComputeBetterPriceForecastingusingMachineLearning

AvishkarMisraPhD– avishkar.misra@oracle.comBigData&AnalyticsPlatformTeam

OracleBigDataApplianceandOracleBigDataCloudServiceofferafantasticcomputationplatformtoscaleupexperimentationandspeeduptimetoinsight.TheOracleBigData&AnalyticsPlatformteam,workedwithaleadingNorthAmericancommodityproducerin2016,tohelpimprovethegranularityandaccuracyoftheirpredictionforthepriceofthecommodity.Anaccuratepriceforecastmodelhasapotentialbenefitof$25.6Movera3yearperiodforthecommodityproducer.WeusedPythonandPySpark runningonOracleBigDataAppliancetotryout39,936modelingcombinationsinamatterofminutestofindabetteralgorithm.Themoreaccuratealgorithmwasabletopredictthepricewithin+/-5%oftheactualprice73%ofthetimecomparedto40%forthealgorithmthecustomerhaddevelopedandfine-tunedovermanyyears.Oracle’sBigDataplatformsofferawaytodramaticallyscalethenumberofexperiments,helpingthescientistsandanalystsquicklynarrowtheirfocusonthemostrelevantandeffectivetechniquestoimpacttheirbusiness’sbottomline.

BlogPost:https://a-misra.com/2016/08/01/big-data-is-also-big-compute/

Abstract

BigData&DataScienceAdvisoryServicesCustomerEngagementsthatbringindustryexperienceandbestpracticestodemonstrateandprototypesolutionsforcustomerinlinewiththeirstrategicbusinessgoals.

2

SolutionArchitecture

3

DataScience&MachineLearning

4

AnalyticsDesignHub

1

BusinessValueDefinition

Teamskill-sets:• DataScientists• DataWranglers• Architects• BusinessAnalysts

Teambackgroundsfrom:• Amazon• MSFT• Deloitte• IBM• Teradata• etc.

Define,design,developanddemonstratethevalueofasolutionquickly.

ExecutiveCommitment

DiscoverySession

ExecutiveReadout Deploy

BusinessCase

AnalyticSprint

Architecture&

Roadmap

4-6weeks

Agriculture:aerialimageanalysis1

2 Utilities:frauddetection

Tree

BigDataAnalyticsSprint

AlargeNorthAmericanLumberproducerlookingtoimproveweeklypriceforecasts

Today:CommodityPriceForecasting

Spotmarkettradersspeculatedonweeklycommoditypriceusing:

priorweek’smarketprice90-dayforecast– Tunedover10yearsgutinstinct

Decide:sell vshold inventory

MoreaccuratepricepredictioncanÛMargin

LCBQ - KDWesternSpruce-Pine-Fir#2&Btr2x4random.

Commonbenchmark,sinceithashighcorrelationwithothertypesoflumber.

Lumberlumbereverywhere

Normalizethedataforweeklypredictionsby

• over-sampling

• aggregation

• sub-sampling

Whatfactorsinfluencetheprice?

CurrentPrice Weekly(everyFriday)

EconomicActivityinConstruction&Building Monthly (3-monthlag)

CurrencyExchangeRates Daily

Weather*new Hourly

Focus:59majorpopulationcenters– wherepeopleliveandlikelytorenovate+build.

WeatherData:

• WeeklyTotalPrecipitation

• WeeklyAverageTemperature

Weather,butforwhere?

Time-seriesprediction,socannotusetraditionalk-foldvalidation.

EvaluationMethodology

...

2-years

For104weeks:• Trainmodelsforeachweek.• Predictfornextweek.• Evaluatepredictionaccuracy

Q:MachineLearningpipelineoptions– Whichdowepick?

FeatureGeneration None,RollingWindow {7,13,26,52},PolynomialInteractions,All 6

FeatureNormalizer None, Min-Max 2

FeatureSelector None, PCA 2

ModelingObjective Classifier, Regression 2

ModelingAlgorithm LogisticRegressionDecisionTreesExtraTreesAda BoostGradientBoostingRandomForest

LinearRegressionRidgeRegressionLassoRegressionDecisionTreeRegressorAda BoostTreeRegressorExtraTreeRegressorGradientBoostingRegressorRandomForestRegressor

6 |8

Answer:Experiment!

OracleBigDataCloudService

DataScientists

Ifyoudoublethenumberofexperimentsyoudoperyearyou’regoingtodoubleyourinventiveness.

– JeffBezos

Trainedandevaluated- 14,976Classifiers

Classification– Canwepredictifthepricewillgoup?

Good

Poor

Trainedandevaluated- 22,464Regressors

Regressors – Canwepredicttheprice?

Poor

Good

Evaluated37,440modelsinlessthan15mins!!

Takeaways

Classifiers:• Ensemblearebetter- ExtraTrees,GradientBoosting,RandomForest,AdaBoost.• Min-Maxnormalizationoffeaturevaluesnotuseful• PrincipalComponentAnalysisveryhelpful

Regressors:• Lotofmodelover-fitting!• ExtraTreeRegressors workverywell• PrincipalComponentAnalysisnothelpful

Insightsà Abetterpricepredictionalgorithm

Target isActualPrice+/- 5%

LinearRegression:40% oftimeintarget.

Extra-TreeRegression:73% oftime intarget.

Û $19.4mto$25.6mmarginover3years

PriceForecastingforCommodityGoodsObjective:Improvepriceforecastingonfinishedcommoditygoodstooptimizemargindollarsacrossallchannelsandenhancecustomerupsellingcapabilitiesfortraders

• OracleBigDataCloudServices,including:ClouderaHadoopDist.,Spark,Jupyter,Connectors,DataIntegrator,OracleRAdvancedAnalyticsforHadoop,Spatial&Graph,Storage

• SalesReps(spotmarkettraders)reliedonpriorweekmarketpricingandlong-range(90)dataforecaststomakea‘gutinstinct’decision–weeklypriceforecastsdidnotexist

• BusinessAnalystsmanuallypulledinformationfromavarietyofsourcesintoanExcelworkbook– oftenhavingtoretypemarkettrendinformationfromPDFs

• Whilethecurrentprocesswasdeemedhelpfulforlongrangestrategicforecasting,tradersneededshort-termforecastsinordertobetterpricegoodsanddeterminewhethertosellorholdinventoryonaweeklybasis

BusinessChallenge/Opportunity

ProposedSolution

Example

Leading North American Lumber Producer for Builders

and Home Improvement Centers

PotentialBenefits• Overa3yearhorizon,potentialincrementalgrossmarginfromimprovedpricingandreducedoperatingcostsrangedfrom$19.4mto$25.6minincrementalbenefits,anda4,342% ROI

AnalyticSprintResults• Demonstratedtheinclusionofexternaldatasetssuchasweather intothemixofdatatoimproveforecastaccuracy

• Demonstratednewmachinelearningtechniquesandtheabilitytoscaleclientexperimentationwithforecastmodelingto37k analyticmodelsinamatterofminutes

• Improvedpredictiongranularity frommonthlytoweekly,andaccuracytowithin+/- 5%oftheactualmarketprice73% ofthetime,comparedwith40% forthealgorithmusedbytheclient

BigData&DataScienceAdvisoryServicesGetintouchwithus!

Email:avishkar.misra@oracle.com

Blog:http://a-misra.com

OracleExternal:https://www.oracle.com/big-data

OracleInternal:http://bdcoe.us.oracle.com

Thiswork:https://a-misra.com/2016/08/01/big-data-is-also-big-compute/