In:
INFORMS Journal on Data Science, Institute for Operations Research and the Management Sciences (INFORMS), Vol. 1, No. 1 ( 2022-04), p. 96-113
Abstract:
Forecasting competitions are the equivalent of laboratory experimentation widely used in physical and life sciences. They provide useful, objective information to improve the theory and practice of forecasting, advancing the field, expanding its usage, and enhancing its value to decision and policymakers. We describe 10 design attributes to be considered when organizing forecasting competitions, taking into account trade-offs between optimal choices and practical concerns, such as costs, as well as the time and effort required to participate in them. Consequently, we map all major past competitions in respect to their design attributes, identifying similarities and differences between them, as well as design gaps, and making suggestions about the principles to be included in future competitions, putting a particular emphasis on learning as much as possible from their implementation in order to help improve forecasting accuracy and uncertainty. We discuss that the task of forecasting often presents a multitude of challenges that can be difficult to capture in a single forecasting contest. To assess the caliber of a forecaster, we, therefore, propose that organizers of future competitions consider a multicontest approach. We suggest the idea of a forecasting-“athlon” in which different challenges of varying characteristics take place.
Type of Medium:
Online Resource
ISSN:
2694-4022
,
2694-4030
DOI:
10.1287/ijds.2021.0003
Language:
English
Publisher:
Institute for Operations Research and the Management Sciences (INFORMS)
Publication Date:
2022
detail.hit.zdb_id:
3101534-7
