Citation

Mader M, Mader W, Gluckman BJ, Timmer J, Schelter B. Phys. Rev. E Stat. Nonlin. Soft Matter Phys. 2014; 90(2): 022133.

Affiliation

Freiburg Center for Data Analysis and Modeling (FDM), University of Freiburg, Freiburg, Germany and Institute for Complex Systems and Mathematical Biology, University of Aberdeen, Aberdeen, UK.

Copyright

(Copyright © 2014, American Physical Society, Publisher American Institute of Physics)

DOI

unavailable

PMID

25215714

Abstract

Reliable forecasts of extreme but rare events, such as earthquakes, financial crashes, and epileptic seizures, would render interventions and precautions possible. Therefore, forecasting methods have been developed which intend to raise an alarm if an extreme event is about to occur. In order to statistically validate the performance of a prediction system, it must be compared to the performance of a random predictor, which raises alarms independent of the events. Such a random predictor can be obtained by bootstrapping or analytically. We propose an analytic statistical framework which, in contrast to conventional methods, allows for validating independently the sensitivity and specificity of a forecasting method. Moreover, our method accounts for the periods during which an event has to remain absent or occur after a respective forecast.

Language: en