Download Forecasting Methodology Tree

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
Others
Unstructured
Univariate
Self
Role
Structured
Multivariate
Data- Theorybased based
No role
Role playing
(Simulated
interaction)
Unaided
judgment
Statistical
Extrapolation
models
Intentions/
expectations
Conjoint
analysis
Quantitative
analogies
Data
mining
Neural
nets
Rule-based
forecasting
Linear
Expert
Forecasting
Structured
analogies
Game
theory
Decomposition
Judgmental
bootstrapping
Expert
systems
Causal
models
Classification
Segmentation
Methodology Tree for Forecasting
forecastingprinciples.com
JSA-KCG
November 2007
Sufficient
objective data
Judgmental methods
No
Quantitative methods
Yes
Good
knowledge of
relationships
Large changes
expected
No
Yes
Conflict among a few
decision makers
Policy analysis
No
Yes
No
Highly
repetitive with
learning
Yes
Yes
Yes
Cross-section
Unaided
judgment
Similar
cases exist
No
No
No
Judgmental
bootstrapping/
Decomposition
Large changes
likely
No
Time series
Yes
Good
domain
knowledge
Policy
analysis
Yes
Type of
knowledge
Domain
Yes
Type of
data
Policy
analysis
No
Expert
Forecasting
(Delphi, NGT,
ETE, Markets)
No
Yes
Yes
No
Self
Conjoint
analysis
Role playing
(Simulated
interaction/
Game theory)
Intentions/
expectations
No
Single
method
Use unadjusted
forecast
Structured
analogies
Several
methods provide
useful forecasts
Omitted
information
?
No
Yes
Quantitative
analogies
Expert
systems
Rule-based
forecasting
Extrapolation/
Neural nets/
Data mining
Causal
models/
Segmentation
Yes
Combine
forecasts
Use adjusted
forecast
Selection Tree for Forecasting Methods
forecastingprinciples.com
JSA-KCG
November 2007