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Testing the reliability of forecasting systems.

J Bröcker1

  • 1School of Mathematical, Physical and Computational Sciences, University of Reading, Reading, UK.

Journal of Applied Statistics
|December 19, 2022
PubMed
Summary

This study introduces new statistical tests to evaluate the reliability of forecasting systems. The methods ensure accurate assessment of forecast performance by accounting for data dependencies, crucial for environmental predictions.

Keywords:
Forecastingenvironmental statisticsidentifiabilityreliability

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Area of Science:

  • Statistics
  • Meteorology
  • Environmental Science

Background:

  • Evaluating the statistical reliability of forecasting systems is a significant challenge.
  • Existing methods often overlook the complex dependence structure within verification-forecast pairs, potentially leading to inaccurate assessments.
  • Forecasting systems are considered reliable if they exhibit nominal statistical behavior, such as providing accurate expected values or quantiles.

Purpose of the Study:

  • To develop statistically rigorous tests for evaluating the reliability of forecasting systems.
  • To address the challenge of unknown dependence structures in verification-forecast data.
  • To provide a framework for reliable forecast evaluation applicable to environmental forecasting.

Main Methods:

  • Development of statistical tests based on archives of verification-forecast pairs.
  • Leveraging the information provided by forecast reliability to model the dependence structure.
  • Derivation of rigorous results on the asymptotic distribution of test statistics under minimal assumptions.

Main Results:

  • New statistical tests for forecast reliability are presented.
  • The methods account for the dependence structure of verification-forecast pairs, improving test accuracy.
  • Rigorous asymptotic distributions for test statistics are obtained, enabling robust reliability testing.

Conclusions:

  • The proposed statistical tests offer a more reliable method for evaluating forecasting systems.
  • Accounting for data dependence is crucial for avoiding incorrect rejection of reliable forecasts.
  • The methods are applicable to environmental forecasting and include a Python implementation.