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Activity Number: 220521 - Statistical and Machine Learning Efforts on Solar Flare Predictions I
Type: Topic Contributed
Date/Time: Monday, August 9, 2021 : 10:00 AM to 11:50 AM
Sponsor: Section on Physical and Engineering Sciences
Abstract #317808
Title: Solar Flare Index Prediction Using Functional Additive Regression
Author(s): Zuofeng Shang*
Companies: New Jersey Institute of Technology

In this talk, we propose functional additive regression (FAR) method to predict the solar flare index. The predictors are 25 variables obtained from the Space-weather HMI Active Region Patches (SHARP) produced by the SDO/HMI team, which can be found as the data series at the Joint Science Operations Center (JSOC). Each SHARP parameter is a time series observed from May 2010 to December 2017 in one-hour cadence. The FAR method establishes a functional relationship between the solar flare index and the SHARP parameters, so that when a new SHARP parameter enters the model, we are able to predict the future solar flare index. Numerous simulation studies and real-data analysis are provided to demonstrate the effectiveness of the FAR method.

Authors who are presenting talks have a * after their name.

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