Forecast selection and combination are regarded as two competing alternatives. In the literature there is substantial evidence that forecast combination is beneficial, in terms of reducing the forecast errors, as well as mitigating modelling uncertainty as we are not forced to choose a single model. However, whether all forecasts to be combined are appropriate, or not, is typically overlooked and various weighting schemes have been proposed to lessen the impact of inappropriate forecasts. We argue that selecting a reasonable pool of forecasts is fundamental in the modelling process and in this context both forecast selection and combination can be seen as two extreme pools of forecasts. We evaluate forecast pooling approaches and find them beneficial in terms of forecast accuracy. We propose a heuristic to automatically identify forecast pools, irrespective of their source or the performance criteria, and demonstrate that in various conditions it performs at least as good as alternative pools that require additional modelling decisions and better than selection or combination.
- Forecast combination
- Forecast pooling
- Model selection
ASJC Scopus subject areas
- Business, Management and Accounting(all)
- Economics and Econometrics
- Management Science and Operations Research
- Industrial and Manufacturing Engineering
FingerprintDive into the research topics of 'Another look at forecast selection and combination: evidence from forecast pooling'. Together they form a unique fingerprint.
- Management - Professor
- Information, Decisions & Operations - Chair in Management Science; Director of Studies MSc in Operations, Logistics & Supply Chain Management
- Smart Warehousing and Logistics Systems - Member
Person: Research & Teaching, Researcher