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Automated Bayesian Network Construction for Economic Indicator Prediction

This thesis explores an algorithm for constructing Bayesian networks to predict economic and development indicators of 248 countries. It addresses challenges like missing values, dimensionality, and interpretability. The study compares related algorithms and discusses the limitations of manual network building. Leveraging domain knowledge and automatic methods can optimize model performance. The proposed algorithm integrates domain knowledge, statistical structure learning, and resource restrictions to streamline network construction and evaluation. By categorizing variables and considering dependencies, the model aims to provide a reliable framework for economic prediction.

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Automated Bayesian Network Construction for Economic Indicator Prediction

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  1. An Algorithm for the Automatic Construction of Bayesian Networks with limited Domain Knowledgeas applied to the prediction of Economic and Development Indicators of 248 Countries and World Regions presented to the Faculty of the Graduate School at the University of Missouri-Columbia In Partial Fulfillment of the Requirements for the Degree Master of Science in Computer Science By Fernando Javier Torre-Mora Dr. Yi Shang, Thesis Supervisor MAY 2016

  2. Agenda

  3. The Problem Computational Motivation Related algorithms

  4. Missing value problem

  5. Dimensionality problem

  6. Interpretability problem • García, Fernández, Luengo, and Herrera 2009 • Casillas, Cordón, Herrera, and Magdalena 2013 • Liu, Cocea and Gegov2016 Trustworthyness Meaningfulness Useful knowledge

  7. Bayesian Network PrevCompensation Prev Capital Formation Inflation Prev Capital PrevConsumption PrevInvestment Capital-labor ratio Capital Formation Compensation Interest Exogenous Capital Workers Consumption Investment GPD

  8. Bayesian Network drawbacks • Usually built manually, then trained • Requires full knowledge of how domain works • Automatic methods exist • Order dependent (Cooper and Herskovits 1992) • Real example: 2016 Inflation 2015 Wages 2016 Wages

  9. Bayesian Network drawbacks • Usually built manually • Requires full knowledge of how domain works • Automatic methods exist • Order dependent (Cooper and Herskovits 1992) • Real example: 2016 Wages 2016 Inflation 2015 Wages

  10. Bayesian Network drawbacks • Usually built manually • Requires full knowledge of how domain works • Automatic methods exist • Order dependent (Cooper and Herskovits 1992) • Real example: • Counter-intuitive results • Genetic Algorithm (Chickering and Heckerman 1997) • Random ordering • Counter-intuitiveness may be optimized • Statistical structure learning (Friedman et al. 1999) • Evaluates all possible candidates

  11. Statistical structure-learning: all vs all PrevCompensation Prev Capital Formation Inflation n=16⇒ Prev Capital PrevConsumption PrevInvestment 20,922,789,888,000comparisons approx.O(n!) Capital-labor ratio Capital Formation Compensation Interest Exogenous Capital Workers Consumption Investment GPD

  12. Statistical structure learning: categories PrevCapFrom PrevCompens Inflation Prev Capital Capital Form Prev Invest PrevConsump Cap-labor ratio Compensation Interest Capital GPD Investment Exogenous Workers Consumption

  13. Statistical structure learning: groups Resource Restrictions Smoothing Estimation Parameter Stickiness Resource Restrictions Estimation Parameters Economy

  14. Grouped all vs all n=16⇒ 1,213,440 comparisons O(c!n22c) • Estimation parameter stickiness • Resource Restriction smoothing • Resource restrictions • Estimation parameters • Economy

  15. Friedman et al. 1999 main drawback PrevCompensation Prev Capital Formation Inflation Prev Capital PrevConsumption PrevInvestment Capital-labor ratio Capital Formation Compensation Interest Exogenous Capital Workers Consumption Investment GPD

  16. Agenda

  17. Proposed Algorithm Basic idea Representation Pipeline Applicability

  18. Basic idea: Domain Knowledge graph PrevCapFrom PrevCompens Inflation Prev Capital Capital Form Prev Invest PrevConsump Cap-labor ratio Compensation Interest Capital GPD Investment Exogenous Workers Consumption

  19. Proposed Smets-WoutersDomain Knowledge Resource Restrictions Smoothing n=16⇒ 306 Comparisons O Estimation Parameter Stickiness Resource Restrictions Estimation Parameters Economy

  20. Proposed Domain Knowledge

  21. Proposed UNESCODomain Knowledge Previous Year Economy Innovation Education Production Economy

  22. UNESCO variables Previous Year Economy PreviousGDP PreviousGrowth Innovation Education Journal Assistance Secondary Primary Tertiary Government Trademarks Production Economy Services Industry Manufacturing Agriculture Growth GDP Unemployment

  23. UNESCO sample generated network PreviousGrowth PreviousGDP Primary Secondary Tertiary Assistance Journal Government Trademarks Manufacturing Unemployment Agriculture Services Industry GDP Growth

  24. UNESCO Domain Knowledge model representation Previous Year Economy Categorization hashmap Category relation Graph Prev GDP Prev Year Econ Prev Growth Primary Innovation Educ Secondary Education Tertiary Industry Prod Services Unemployment Innov Journal Economy Production Trademarks Government Econ Prev GDP Prev Growth

  25. Basic Dataflow Bayesian Network Construction Training Data Test data Bayesian Network Evaluation

  26. Basic Dataflow Bayesian Network Construction Training Data Test data Bayesian Network Evaluation Domain Knowledge variable categories Dependency evaluation Domain Knowledge model • Weighted Domain Knowledge model Domain Knowledge graph

  27. Dependency evaluation Compensation Inflation Interest PrevCompens Capital Form Prev Cap From Cap-labor ratio

  28. Arc Evaluation 1− > min • Standard Error for Least SquaresLinear Regression (STE) • Consistent with Correlation Coefficient • Consistent with Mutual Information Coefficient • Non-Symmetric 1− Capital Form ? Prev Cap From Weight

  29. Dependency evaluation (cont.) Compensation Inflation Interest PrevCompens Capital Form Prev Cap From Cap-labor ratio Weight

  30. Dependency evaluation (cont.) PrevCompens Prev Cap From Inflation Compensation Inflation Interest PrevCompens Compensation Capital Form Capital Form Interest Cap-labor ratio Prev Cap From Cap-labor ratio Threshold

  31. Basic Dataflow (cont) Bayesian Network Construction Training Data Test data Bayesian Network Evaluation Domain Knowledge variable categories • Shenoy-Shafer algorithm CPT computation Dependency evaluation Domain Knowledge model • Weighted Domain Knowledge model • Untrained Bayesian Network • Trained Bayesian Network • Accuracy Domain Knowledge graph

  32. Sample applicability 1 Background Eyes Foreground Vegetation Detection

  33. Sample applicability 2 Molecular function Eyes Biological process Cellular Componen Phenotype

  34. Sample applicability 3 Environment Drugs Activity Health Mood

  35. Sample applicability 3 Temp Precip Humid Coffee Speed Alcohol illegal Posture Talking Heart Mood Survey Body Temp Breath

  36. Agenda

  37. Dataset Dataset Motivation Related work in the domain Missing value problem Domain Knowledge models

  38. The economic prediction problem Dot com crash Great Recession Thai Dong collapse

  39. Machine Learning economic prediction(Manually designed, algorithmically trained) Shaaf, Mohamed, 2000 Gonzalez, Steven, 2000 US Treasury Bond Prev. year GDP Growth rate Prev Employ Cons. Confid. Real reaturn Curr employ Gov spend. Curr. Year GDP Curr. Year GDP

  40. The Smets-Wouters economic model for the United States, 2007 PrevCompensation Prev Capital Formation Inflation Prev Capital PrevConsumption PrevInvestment Capital-labor ratio Capital Formation Compensation Interest Exogenous Capital Workers Consumption Investment GPD

  41. Data availability for each Smets-Woutersvariable, United States PrevCompensation Prev Capital Formation Inflation Prev Capital PrevConsumption PrevInvestment Capital-labor ratio Capital Formation Compensation Interest Exogenous Capital Workers Consumption Investment GPD

  42. Data availability for each Smets-Woutersvariable, global average PrevCompensation Prev Capital Formation Inflation Prev Capital PrevConsumption PrevInvestment Capital-labor ratio Capital Formation Compensation Interest Exogenous Capital Workers Consumption Investment GPD

  43. Years needed to adapt the Smets-Wouters model

  44. Smets-Wouters categories Resource Restrictions Smoothing PrevCapFrom PrevCompens Inflation Estimation Parameter Stickiness Prev Capital Resource Restrictions Capital Form Prev Invest PrevConsump Cap-labor ratio Compensation Interest Estimation Parameters Economy Capital Investment Exogenous GPD Consumption Workers

  45. Proposed Smets-Wouters Domain Knowledge (revisited) Resource Restrictions Smoothing Estimation Parameter Stickiness Resource Restrictions Estimation Parameters Economy

  46. Smets-Wouters Domain Knowledge model representation (revisited) Resource Restrictions Smoothing Categorization hashmap Category relation Graph Inflation Res RestrSmth PrevCompens Prev Cap From PrevConsump Estimation Parameter Stickiness Est Para Sticki Resource Restrictions Prev Invest Prev Capital Res Restr Compensation Interest Capital Form Est Para Cap-labor ratio Consumption Estimation Parameters Economy Investment Econ Workers Capital GDP

  47. Proposed UNESCO Domain Knowledge (revisited) Previous Year Economy Innovation Education Production Economy

  48. UNESCO Domain Knowledge model representation (revisited) Previous Year Economy Categorization hashmap Category relation Graph Prev GDP Prev Year Econ Prev Growth Primary Innovation Educ Secondary Education Tertiary Industry Prod Services Unemployment Innov Journal Economy Production Trademarks Government Econ Prev GDP Prev Growth

  49. Data availability for each UNESCO variable, global average

  50. Agenda

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