Methodology Tree for Forecasting

1. 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. Methodology Tree for Forecasting Knowledge source Statistical Judgmental Univariate Multivariate Others Self Data- based Theory- based Role No role Unstructured Structured Extrapolation models Data mining Intentions/ expectations Role playing(Simulatedinteraction) Unaided judgment Quantitative analogies Neural nets Conjoint analysis Rule-based forecasting Linear Classification Structured analogies Decom-position Expert Forecasting Game theory Judgmental bootstrapping Expert systems Segmentation Causal models Methodology Tree for Forecasting forecastingprinciples.com JSA-KCG November 2007

2. Sufficient objective data Judgmental methods Quantitative methods No Yes Large changes expected Good knowledge of relationships No Yes No Yes Conflict among a few decision makers Policy analysis Type of data Large changes likely No Yes No Yes No Yes Cross-section Time series Highly repetitive with learning Similar cases exist Policy analysis Policy analysis Good domain knowledge Yes No No Yes Yes No Unaided judgment Type of knowledge No Yes Yes No Domain Self Expert Forecasting (Delphi, NGT, ETE, Markets) Judgmental bootstrapping/ Decomposition Conjoint analysis Intentions/ expectations Role playing(Simulatedinteraction/ 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 Combine forecasts Single method Omitted information? Selection Tree for Forecasting Methods forecastingprinciples.com JSA-KCG November 2007 No Yes Use adjusted forecast Use unadjusted forecast