Methodology Tree for Forecasting The Methodology Tree for Forecasting classifies all possible types of forecasting methods into categories and shows how they relate to one another. Dotted lines represent possible relationships. Knowledge source Judgmental Statistical Others Self Univariate Multivariate Data- based Theory- based Unstructured Structured Role No role Extrapolation models Data mining Unaided judgment Role playing (Simulated interaction) Intentions/ expectations Quantitative analogies Neural nets Conjoint analysis Rule-based forecasting Feedback No feedback Linear Classification Prediction markets Delphi Structured analogies Game theory Decom-position Judgmental bootstrapping Expert systems Causal models Segmentation Methodology Tree for Forecasting forecastingprinciples.com JSA-KCG September 2005

Role playing (Simulated interaction/ Sufficient objective data Judgmental methods No Yes Quantitative methods Large changes expected Good knowledge of relationships No Yes No Yes Policy analysis Conflict among a few decision makers Type of data Large changes likely No Yes No Yes Cross-section Time series No Yes Accuracy feedback Policy analysis Similar cases exist Policy analysis Good domain knowledge Yes No Yes No No Yes Unaided judgment Type of knowledge No Yes Yes No Domain Self Delphi/ Prediction markets Judgmental bootstrapping/ Decomposition Conjoint analysis Intentions/ expectations Role playing (Simulated interaction/ Game theory) Structured analogies Quantitative analogies Expert systems Rule-based forecasting Extrapolation/ Neural nets/ Data mining Causal models/ Segmentation Several methods provide useful forecasts No Yes Single method Combine forecasts Omitted information? Selection Tree for Forecasting Methods forecastingprinciples.com JSA-KCG January 2006 Use unadjusted forecast No Yes Use adjusted forecast