• Corpus ID: 251223446

Another look at forecast trimming for combinations: robustness, accuracy and diversity

@inproceedings{Wang2022AnotherLA,
  title={Another look at forecast trimming for combinations: robustness, accuracy and diversity},
  author={Xiaoqian Wang and Yanfei Kang and Feng Li},
  year={2022}
}
Forecast combination is widely recognized as a preferred strategy over forecast selection due to its ability to mitigate the uncertainty associated with identifying a single “best” forecast. Nonetheless, sophisticated combinations are often empirically dominated by simple averaging, which is commonly attributed to the weight estimation error. The issue becomes more problematic when dealing with a forecast pool containing a large number of individual forecasts. In this paper, we propose a new… 

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