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Processing and Optimization of Forecast Queries

Ulrike Fischer. Processing and Optimization of Forecast Queries. Motivation. Time series data appears in many domains. Sales and inventory. Renewable energy ressources. High accuracy possible Sophisticated models Sophisticated estimators. Runtime restrictions

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Processing and Optimization of Forecast Queries

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  1. Ulrike Fischer Processing and Optimization of Forecast Queries

  2. Motivation • Time seriesdataappears in manydomains Salesandinventory Renewableenergyressources • High accuracypossible • Sophisticatedmodels • Sophisticatedestimators • Runtimerestrictions • Large numberof time series • Short amountof time available  Two Optimization Dimensions: Accuracy and Runtime Processing and Optimization of Forecast Queries

  3. Outline • Motivation • Integration ofForecastinginside a DBMS • Processing of Forecast Queries • Optimizationof Forecast Queries in Hierarchies • Summary Processing andOptimizationof Forecast Queries

  4. Model-based Time Series Forecasting • 1. Model Creation • Model Identification • Parameter Estimation • 3. Model Maintenance • Model Evaluation • Threshold-based, time-based … • Model Adaption • Parameter Re-estimation • 2. Model Usage • Forecasting Model • Triple Exponential Smoothing ! ! Processing andOptimizationof Forecast Queries

  5. Time Series Forecasting in DBMS  Transparency and Effienciency M M export M M SQL export M SQL  Reuse of models and results Processing and Optimization of Forecast Queries

  6. Project Overview EU FP7 project Scheduling quantity date 2012 34,000 SELECT date, quantity FROM sales WHERE … FORECAST … Forecasting Aggregation 38,000 2013 … … FlexOffers DWH Supply Demand Processing and Optimization of Forecast Queries

  7. Overview F2DB Forecast Queries Inserts Query Interface Model Usage Model Maintenance Model Index Query Processing & Optimization On-Demand Estimation QP in Hierarchies Hybrid Maintenance PublishSubscribeQueries Model Pool Model Model Model Model M1 Model Creation Time Series Time Series Time Series + := M1 M2 M3 Ensemble Models Base Tables Physical Design AR(2), BFGS, MSE … Processing andOptimizationof Forecast Queries

  8. Outline • Motivation • Integration ofForecastinginside a DBMS • Processing of Forecast Queries • Optimizationof Forecast Queries in Hierarchies • Summary Processing andOptimizationof Forecast Queries

  9. Forecast Query Processing SELECTdate, SUM(quantity) FROMsales WHEREproduct= ‘HTC‘ GROUP BYdate FORECAST 3 • Extension of SQL language • Horizon, measureand time column,model type andparameters, … • Logical query plan • Forecast operatorΨ Physicalquery plan Forecast MHTC Ψk=3 Forecast πdate, quantity BuildModel Aggregate γdate:AGG(sales) Scan σproduct= 'HTC' sales sales Processing andOptimizationof Forecast Queries

  10. Advanced Forecast Query Processing • Data warehousecontains multidimensional data SELECTdate, SUM(quantity) FROMsales WHEREproduct= ‘HTC‘ GROUP BYdate FORECAST 3 days Mobiles Aggregation 3. Disaggregation DisAgg Forecast Forecast 1. Direct MHD2 MSmart Forecast Key Nokia HTC 2. Aggregation MMobiles HD2 Smart Processing andOptimizationof Forecast Queries

  11. Aggregation vs. Disaggregation Top-Down (Disaggregation) Bottom-Up (Aggregation) Complete (Direct) Efficiency Accuracy Model creationeasier Noinformationloss Edwards andOrcuss (1969) Schwarzkopf et. al. (1988) Hubrich (2005) … GrunfeldandGriliches (1960) Grossand Sohl (1990) Zellner and Tobias (2000) ….  Depends on data set, quality of forecast model, correlation … Processing and Optimization of Forecast Queries

  12. Outline • Motivation • Integration ofForecastinginside a DBMS • Processing of Forecast Queries • Optimizationof Forecast Queries in Hierarchies • Summary Processing andOptimizationof Forecast Queries

  13. ConfigurationAdvisor Updates Forecast Queries Workload W Preference α • Problem: Exponentialsearchspace • GreedyAlgorithm(monotonicmaintenancecosts) • Start onemodelatthe top, addmodelsstep-by-step Query Interface Model Advisor Analyze Cost BW + Error EW Create Configuration CW DWH Model Pool Configuration + Strategy WeightedAccuracy WeightedEfficiency Processing and Optimization of Forecast Queries

  14. Performance Comparison • Complete (C) All models, onlydirectforecasts • Bottom-Up (B) Onlymodelsatlevelone, othersuseaggregation • Top-Down (T) Onlyonemodelfor top element, othersusedisaggregation • Greedy (G) Processing andOptimizationof Forecast Queries

  15. Extensions • Observation: aggregation(bottom-up) hardlyused in real datasets • Reason: large numberofchild time series • Sample Aggregation • Use sample ofchildmodels • Group Design • Relax fixedaggregationgroups ? ? Virtual Group ? • aggregation + estimation supportofdisjunctivequeries • Estimateusinghistoricalproportion • Weightedsampling Processing and Optimization of Forecast Queries

  16. Outline • Motivation • Integration ofForecastinginside a DBMS • Processing of Forecast Queries • Optimizationof Forecast Queries in Hierarchies • Summary Processing andOptimizationof Forecast Queries

  17. Summary • DBMS Integration • Sophisticatedmodelscomputationally expensive • DBMS integrationforreuse, transparencyandoptimization • Forecast Queries • New query type withforecasthorizon • Face twootimizationdimensions • HierarchicalForecasting • Reducemaintenancecostswithderivationschemes • Possibleincreaseofaccuracy • Large searchspace Processing andOptimizationof Forecast Queries

  18. Ulrike Fischer Processing and Optimization of Forecast Queries

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