The following analysis was prepared by J. Scott Armstrong. The SAS grants can be used to address these issues.
Forecasting software can:
But does it?
The attached table identifies which principles are currently used and how other principles could be incorporated.
FORECASTING PRINCIPLES

Used 
Add? 
Need 
Need 





PROBLEM 









1. Setting Objectives 




1.1. Describe decisions that might be affected 


Y 

1.2. Agree on actions for different possible
forecasts 


Y 

1.3. Make forecast independent of
organizational politics. 


Y 

1.4. Consider whether events or series are
forecastable 
1 



1.5. Gain decision makers' agreement on methods.



Y 






2. Structuring the Problem 




2.1. Identify possible outcomes prior to making
forecasts 


Y 

2.2. Tailor the level of data aggregation to
the decisions 


Y 

2.3. Decompose the problem into sub problems 

A 


2.4. Decompose time series by causal forces 

A* 


2.5. Structure problems to deal with important
interactions 

A 


2.6. Structure problems that involve causal
chains 

A 


2.7. Decompose time series by level and trend 

A* 







INFORMATION 









3. Identifying Information Sources 




3.1. Use theory to guide information search on
explanatory variables 


Y 

3.2. Ensure that data match the forecasting
situation 


Y 

3.3. Avoid biased data sources 


Y 

3.4. Use diverse sources of data 


Y 

3.5. Obtain information from similar
(analogous) series or cases 


Y 






4. Collecting Data 




4.1. Use unbiased and systematic procedures to
collect data 


Y 

4.2. Ensure that information is reliable 


Y 

4.3. Ensure information is valid 


Y 

4.4. Obtain all important data 


Y 

4.5. Avoid collection of irrelevant data 


Y 

4.6. Obtain the most recent data 


Y 






5. Preparing Data 




5.1. Clean the data 
2 



5.2. Use transformations as required by
expectations. 
2 



5.3. Adjust intermittent series 
2 



5.4. Adjust for unsystematic past events
(outliers) 
2 



5.5. Adjust for systematic events (e.g.,
seasonality) 
2 



5.6. Use multiplicative adjustments for
seasonality for stable series w trends 
A* 



5.7. Damp seasonal factors for uncertainty 

A* 


5.8. Use graphical displays for data 
2 








METHODS 









6. Selecting Methods 




6.1. Develop list of all important criteria 


Y 

6.2. Ask unbiased experts to rate potential
methods 


Y 

6.3. Use structured forecasting methods rather
than unstructured 


Y 

6.4. Use quantitative methods rather than
qualitative methods 


Y 

6.5. Use causal rather than naÃ¯ve methods 


Y 

6.6. Select simple methods unless evidence
favors complex methods 
2 



6.7 Match forecasting method(s) to the
situation 
2 



6.8. Compare track records of various methods 
2 



6.9. Assess acceptability and understandability
of methods to users 


Y 

6.10. Examine value of alternative forecasting
methods 


Y 






7. Implementing Methods: General 




7.1. Keep methods simple 

A* 


7.2. Provide a realistic representation of the
forecasting situation 


Y 

7.3. Be conservative in situations of
uncertainty or instability 

A* 


7.4. Do not forecast cycles 


Y 

7.5. Adjust for expected events in future 
2 



7.6. Pool similar types of data 

A 


7.7. Ensure consistency with forecasts of
related series 

A 







8. Implementing Methods: Judgment 




8.1. Pretest questions used to solicit
judgmental forecasts 



Judge 
8.2. Use questions that have been framed in
alternative ways 



Judge 
8.3. Ask experts to justify their forecasts 



Judge 
8.4. Use numerical scales with several
categories 



Judge 
8.5. Obtain forecasts from heterogeneous
experts 



Judge 
8.6. Obtain intentions or expectations from
representative samples. 



Judge 
8.7. Obtain forecasts from sufficient number of
respondents 



Judge 
8.8. Obtain multiple estimates of an event from
each expert 



Judge 





9. Implementing Method: Quantitative 




9.1. Tailor the forecasting model to the
horizon 

A* 


9.2. Match model to underlying process 

A* 


9.3. Do not use fit to develop a model 
2 



9.4. Weight the most relevant data more heavily 
2 



9.5. Update models frequently 

A* 







10. Implementing Methods: Quant Models with
Explanatory Variables 




10.1. Use theory and domain expertise to select
casual variables 

A 


10.2. Use all important variables. 

A 


10.3. Use theory and domain expertise to
specify directions of relationships 
A 



10.4. Use theory and domain expertise to
estimate/limit relationships 

A 


10.5. Use different types of data to estimate a
relationship 

A 


10.6. Forecast for at least two alternative
environments 

A 


10.7. Forecast for alternative interventions 

A 


10.8. Apply the same principles to the
forecasts of explanatory variables 
2 



10.9. Shrink forecasts of change if uncertainty
in explanatory variables 

A 







11. Integrating Judgmental and Quantitative
Methods 




11.1. Use structured procedures to do the
integration 


Y 

11.2. Use structured judgment as inputs to
models 

A 


11.3. Use prespecified domain knowledge as
input in selecting, weighting, and modifying quantitative methods 
2 



11.4. Limit subjective adjustments of
quantitative forecasts 

A* 


11.5. Use judgmental bootstrapping instead of
expert forecasts. 



Bootstrp 





12. Combining Forecasts 




12.1. Combine forecasts from approaches that
differ 



Combine 
12.2. Use many approaches (or forecasters),
preferably at least five 



Combine 
12.3. Use formal procedures to combine
forecasts 
2 



12.4. Start with equal weights 



Combine 
12.5. Use trimmed means 



Combine 
12.6.
Use evidence on each methodâ€™s accuracy to vary the weights
on the component forecasts. 



Combine 
12.7. Use domain knowledge to vary the weights
on the component forecasts 



Combine 
12.8. Combine when there is uncertainty about
which method is best. 



Combine 
12.9. Combine when uncertainty exists about
situation 



Combine 
12.10. Combine when it is important to avoid
large errors 



Combine 





EVALUATION 









13. Evaluating Methods 




13.1.
Compare reasonable methods 



Evaluate 
13.2.
Use objective tests of assumptions 
2 



13.3.
Design test situation to match the forecasting problem 
2 



13.4. Describe
conditions associated with the forecasting problem 

A 


13.5.
Tailor the analysis to the decision 



Evaluate 
13.6.
Describe potential forecaster biases 



Evaluate 
13.7.
Assess reliability and validity of the data 



Evaluate 
13.8.
Provide easy access to the data 



Evaluate 
13.9.
Provide full disclosure of methods 



Evaluate 
13.10. Test assumptions for validity 



Evaluate 
13.11. Test client's
understanding of the methods 



Evaluate 
13.12. Use direct replications of the
evaluations to identify mistakes 



Evaluate 
13.13. Use replications of the forecast
evaluations to assess reliability 



Evaluate 
13.14. Use extensions of evaluations for
generalizability. 



Evaluate 
13.15. Conduct extensions of evaluations in
realistic situations 



Evaluate 
13.16. Compare forecasts generated by different
methods 



Evaluate 
13.17. Examine all important criteria 



Evaluate 
13.18. Specify criteria prior to analyzing the
data 



Evaluate 
13.19. Assess face validity 



Evaluate 
13.20. Use error measures that adjust for scale 
2 



13.21. Ensure error measures are valid 



Evaluate 
13.22. Use error measures insensitive to degree
of difficulty in forecasting 



Evaluate 
13.23. Avoid biased error measure 



Evaluate 
13.24. Avoid error measures with high
sensitivity to outliers 



Evaluate 
13.25. Use multiple measures of accuracy 
2 



13.26. Use outofsample (ex ante) error
measures 
2 



13.27. Use ex post accuracy test to
evaluate effects. 

A 


13.28. Do not use adjusted Rsquare to compare
models 

A* 


13.29. Use statistical significance only to
compare the accuracy of reasonable methods 

A* 


13.30. Do not use rootmeansquare errors to
make comparisons 

A* 


13.31. Base comparisons on large sample 


Y 

13.32. Conduct explicit costbenefit analyses 


Y 






14. Assessing Uncertainty 




14.1. Estimate
prediction intervals (PI) 
2 



14.2. Use
objective procedures 
2 



14.3. Develop
PI using realistic representation of situation 



Pred
Int 
14.4. Use
transformations when needed to estimate symmetric PIs 

A* 


14.5. Ensure
consistency over forecast horizon 

A* 


14.6. List
reasons why forecast might be wrong 
1 



14.7. Consider
likelihood of alternative outcomes in assessing PIs 



Pred
Int 
14.8. Obtain
good feedback on accuracy and reasons for errors 



Pred
Int 
14.9. Combine
PIs from alternative methods 
1 



14.10.Use safety factors for PIs 

A* 


14.11.Conduct experiments 



Pred
Int 
14.12.Do not assess uncertainty in a
traditional group meeting 


Y 

14.13. For prediction intervals, incorporate
the uncertainty associated with the prediction of the explanatory
variables 

A 







USING FORECASTS 




15. Presenting Forecasts 









15.1. Provide
clear summary of forecasts and data 

A* 


15.2. Provide clear explanation of methods 
2 



15.3. Describe assumptions 
2 



15.4. Present point forecast and prediction
intervals 
2 



15.5. Present forecasts as scenarios 



Scenario 





16. Learning 




16.1. Consider use of adaptive models 

A 


16.2. Seek feedback about forecasts 



Judge 
16.3. Use a formal review process for
forecasting methods 


Y 

16.4. Use a formal review process for use of
forecasts 


Y 






For detailed description of each principle, see 




Principles of Forecasting, J. Scott Armstrong (ed.), Kluwer, 2001 