 Using shapely values to define subgroups of forecasts for combiningZhenni Ding, Huayou Chen and Ligang Zhou Journal of Forecasting, 2023, vol. 42, issue 4, 905-923 Abstract: This paper proposes an algorithm based on the Shapley value method to select a superior subset from a group of multiple individual forecasts. The Shapley value of a forecast in a combination is an indicator that describes the contribution of the forecast to the combination, which is expressed as the function of four factors in this paper: The accuracy of the forecast, the average accuracy of other forecasts in the combination, the average degree of diversity between the forecast and other forecasts in the combination, and the average degree of diversity between any other two forecasts in the combination. Based on the Shapley value method, the order of different forecasts entering the screening process is determined and the selection algorithm is continuously executed until all the redundant forecasts have been removed from the combination. The effectiveness, superiority, and robustness of the proposed algorithm have been verified via three experiments. The results of empirical studies indicate that the combination of individual forecasts from the selected group markedly outperforms the best individual forecast and the combination of all available forecasts. Date: 2023 Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:wly:jforec:v:42:y:2023:i:4:p:905-923 Access Statistics for this article Journal of Forecasting is currently edited by Derek W. Bunn More articles in Journal of Forecasting from John Wiley & Sons, Ltd. |
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