35: Modeling Autoregressive Conditional Regional Extremes with Applications to Solar Flare Prediction

Music City Center 

This poster studies big data streams with regional-temporal extreme event (REE) structures and solar flare prediction. An autoregressive Conditional Fréchet model with time-varying parameters for regional and its adjacent regional extremes (ACRAE) is proposed. The ACRAE model can quickly and accurately predict rare REEs (i.e., solar flares) in big data streams. The ACRAE model, with some mild regularity conditions, is proved to be stationary and ergodic. The parameter estimators are derived through the conditional maximum likelihood method. The consistency and asymptotic normality of the estimators are established. Simulations are used to demonstrate the efficiency of the proposed parameter estimators. In real solar flare prediction, with the new dynamic extreme value modeling, the occurrence and climax of solar activity can be predicted earlier than with existing algorithms. The empirical study shows that the ACRAE model outperforms the existing prediction algorithms with sampling strategies.

big data

solar flare detection

time series of regional extremes

extreme value theory

tail index dynamics 

Astrostatistics Interest Group