An Empirical Evaluation of Alternative Forecasting Combinations

Combining naive forecasting models as an alternative to using any one model has shown promise for improving forecasting accuracy at reasonable additional cost. This study extends previous combination modeling by selectively including models in combination rather than using all individual models in a...

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Bibliographic Details
Published in:Management science 1987-10, Vol.33 (10), p.1267-1276
Main Authors: Russell, Thomas D, Adam, Everett E., Jr
Format: Article
Language:English
Subjects:
Analytical forecasting
Applied sciences
Data smoothing
Decision making models
Economic forecasts
Error function
Errors
Exact sciences and technology
Forecasting models
Forecasting techniques
forecasting: combination modeling
forecasting: naive models
Management science
Modeling
Operational research and scientific management
Operational research. Management science
Planning. Forecasting
Statistical analysis
Statistical forecasts
Term weighting
Time series
Time series forecasting
Time series models
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Summary:Combining naive forecasting models as an alternative to using any one model has shown promise for improving forecasting accuracy at reasonable additional cost. This study extends previous combination modeling by selectively including models in combination rather than using all individual models in a set, evaluating six new model combinations and comparing them to three previously investigated model combinations, investigating the impact of forecasting time horizon, and investigating alternative error measures to mean square error (MSE). Results support using a model-combination that (1) selects the best 3 to 5 models from the 10 models studied and (2) weights the selected models based upon the inverse proportion of their individual accuracy as measured by MSE.
ISSN:0025-1909
1526-5501
DOI:10.1287/mnsc.33.10.1267