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Modeling Autoregressive Conditional Regional Extremes with Applications to Solar Flare Detection

Abstract Number:

2139 

Submission Type:

Contributed Abstract 

Contributed Abstract Type:

Poster 

Participants:

Steven Moen (1), Jili Wang (1), Zhengjun Zhang (2)

Institutions:

(1) University of Wisconsin-Madison, N/A, (2) University of Chinese Academy of Sciences, N/A

Co-Author(s):

Jili Wang  
University of Wisconsin-Madison
Zhengjun Zhang  
University of Chinese Academy of Sciences

First Author:

Steven Moen  
University of Wisconsin-Madison

Presenting Author:

Steven Moen  
University of Wisconsin-Madison

Abstract Text:

This poster studies big data streams with regional-temporal extreme event (REE) structures and solar flare detection. An autoregressive conditional Fréchet model with time-varying parameters for regional and its adjacent regional extremes (ACRAE) is proposed. The ACRAE model can quickly detect rare REEs (i.e., solar flares) in big data streams and predict solar activity. The ACRAE model, with some mild regularity conditions, is proven to be stationary and ergodic. The parameter estimators are derived through the conditional maximum likelihood method, and 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 detection, using the new dynamic extreme value modeling, the occurrence and climax of solar activity can be detected earlier than with existing algorithms. This is demonstrated with multiple events, including the major solar storm at the end of 2023. The empirical study shows that the ACRAE model outperforms the existing detection algorithms with sampling strategies. Joint work with Jili Wang and Zhengjun Zhang.

Keywords:

big data|solar flare detection|time series of regional extremes|extreme value theory|tail index dynamics|

Sponsors:

Astrostatistics Interest Group

Tracks:

Miscellaneous

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