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A method to improve binary forecast skill verification.

Thitithep Sitthiyot1, Kanyarat Holasut2

  • 1Department of Banking and Finance, Faculty of Commerce and Accountancy, Chulalongkorn University, Mahitaladhibesra Bld., 10th Fl., Phayathai Rd., Pathumwan, Bangkok 10330, Thailand.

Methodsx
|October 29, 2024
PubMed
Summary
This summary is machine-generated.

This study introduces an improvement factor to enhance forecast skill verification methods. It ensures accurate scoring for easy-to-predict events, improving forecast evaluation for forecasters.

Keywords:
A method to improve binary forecast skill verificationBinary eventDeterministic forecast evaluationDirection-of-changeSkill score

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Area of Science:

  • Meteorology
  • Economics

Background:

  • Existing deterministic binary forecast skill verification methods often award perfect scores for easily predictable events, misrepresenting forecast accuracy.
  • This limitation hinders the accurate assessment of forecaster skill, particularly for common or predictable occurrences.

Purpose of the Study:

  • To introduce an improvement factor that refines deterministic binary forecast skill verification.
  • To address the issue of overscoring perfect forecasts for events that are inherently easy to predict.

Main Methods:

  • Developed an improvement factor with two components: ease of forecasting and event frequency.
  • Validated the improvement factor using two hypothetical datasets.
  • Applied the factor to annual inflation rate forecast and actual data for practical assessment.

Main Results:

  • The improvement factor successfully adjusts scores for easy-to-forecast events, bringing them closer to no-skill forecast benchmarks.
  • Demonstrated that the enhanced methods provide a more realistic evaluation of forecast skill.
  • Empirical data confirmed the practical utility of the improvement factor in assessing forecaster performance.

Conclusions:

  • The proposed improvement factor enhances existing deterministic binary forecast skill verification methods.
  • This leads to more accurate and nuanced evaluation of forecast accuracy, especially for predictable events.
  • The method offers a practical tool for assessing forecaster skills in real-world scenarios.