Methodology Tree for Forecasting

1
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
Statistical
Judgmental
Univariate
Multivariate
Self
Others
Data- based
Theory- based
Role
No role
Unstructured
Structured
Extrapolation models
Data mining
Unaided judgment
Role playing(Simulatedinteraction)
Intentions/ expectations
Quantitative analogies
Neural nets
Conjoint analysis
Rule-based forecasting
Feedback
No feedback
Linear
Classification
Segmentation
Causal models
Prediction markets
Delphi
Decom-position
Structured analogies
Expert systems
Judgmental bootstrapping
Game theory
Methodology Tree for Forecasting forecastingprinci
ples.com JSA-KCG September 2005
2
Sufficient objective data
Judgmental methods
Quantitative methods
No
Yes
Large changes expected
Good knowledge of relationships
Yes
No
Yes
No
Policy analysis
Conflict among a few decision makers
Type of data
Large changes likely
Yes
No
Yes
No
Time series
Cross-section
Yes
No
Accuracy feedback
Similar cases exist
Policy analysis
Policy analysis
Good domain knowledge
No
Yes
No
No
Yes
Yes
Unaided judgment
Type of knowledge
No
Yes
Yes
No
Domain
Self
Delphi/ Predictionmarkets
Judgmental bootstrapping/ Decomposition
Conjoint analysis
Intentions/ expectations
Role playing(Simulatedinteraction/ Game theory)
Structured analogies
Expert systems
Rule-based forecasting
Extrapolation/ Neural nets/Data mining
Causal models/ Segmentation
Quantitative analogies
Several methods provide useful forecasts
Yes
No
Single method
Combine forecasts
Omitted information?
Selection Tree for Forecasting Methods forecasting
principles.com JSA-KCG January 2006
Yes
No
Use adjusted forecast
Use unadjusted forecast