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Combining Interval Forecasts

Author

Listed:
  • Anil Gaba

    (INSEAD, Singapore 138676)

  • Ilia Tsetlin

    (INSEAD, Singapore 138676)

  • Robert L. Winkler

    (Fuqua School of Business, Duke University, Durham, North Carolina 27708)

Abstract

When combining forecasts, a simple average of the forecasts performs well, often better than more sophisticated methods. In a prescriptive spirit, we consider some other parsimonious, easy-to-use heuristics for combining interval forecasts and compare their performance with the benchmark provided by the simple average, using simulations from a model we develop and data sets with forecasts made by professionals in their domain of expertise. We find that the empirical results closely match the results from our model, thus providing some validation for the theoretical model. The relative performance of the heuristics is influenced by the degree of overconfidence in and dependence among the individual forecasts, and different heuristics come out on top under different circumstances. The results provide some good, easy-to-use alternatives to the simple average with an indication of the conditions under which each might be preferable, enabling us to conclude with some prescriptive advice.

Suggested Citation

  • Anil Gaba & Ilia Tsetlin & Robert L. Winkler, 2017. "Combining Interval Forecasts," Decision Analysis, INFORMS, vol. 14(1), pages 1-20, March.
    • Handle: RePEc:inm:ordeca:v:14:y:2017:i:1:p:1-20
    DOI: 10.1287/deca.2016.0340
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    References listed on IDEAS

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    Cited by:

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    5. Ariel Karlinsky & Orsola Torrisi, 2023. "The Casualties of War: An Excess Mortality Estimate of Lives Lost in the 2020 Nagorno-Karabakh Conflict," Population Research and Policy Review, Springer;Southern Demographic Association (SDA), vol. 42(3), pages 1-24, June.
    6. Ying Han & David Budescu, 2019. "A universal method for evaluating the quality of aggregators," Judgment and Decision Making, Society for Judgment and Decision Making, vol. 14(4), pages 395-411, July.
    7. Taylor, James W. & Taylor, Kathryn S., 2023. "Combining probabilistic forecasts of COVID-19 mortality in the United States," European Journal of Operational Research, Elsevier, vol. 304(1), pages 25-41.
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    9. Ashkan Zarnani & Soheila Karimi & Petr Musilek, 2019. "Quantile Regression and Clustering Models of Prediction Intervals for Weather Forecasts: A Comparative Study," Forecasting, MDPI, vol. 1(1), pages 1-20, October.
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    13. Wang, Piao & Tao, Zhifu & Liu, Jinpei & Chen, Huayou, 2023. "Improving the forecasting accuracy of interval-valued carbon price from a novel multi-scale framework with outliers detection: An improved interval-valued time series analysis mode," Energy Economics, Elsevier, vol. 118(C).
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    16. Steffen Keck & Wenjie Tang, 2021. "Elaborating or Aggregating? The Joint Effects of Group Decision-Making Structure and Systematic Errors on the Value of Group Interactions," Management Science, INFORMS, vol. 67(7), pages 4287-4309, July.
    17. Makridakis, Spyros & Spiliotis, Evangelos & Assimakopoulos, Vassilios & Chen, Zhi & Gaba, Anil & Tsetlin, Ilia & Winkler, Robert L., 2022. "The M5 uncertainty competition: Results, findings and conclusions," International Journal of Forecasting, Elsevier, vol. 38(4), pages 1365-1385.
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    20. Wang, Xiaoqian & Hyndman, Rob J. & Li, Feng & Kang, Yanfei, 2023. "Forecast combinations: An over 50-year review," International Journal of Forecasting, Elsevier, vol. 39(4), pages 1518-1547.

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