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UFCORIN: A Fully Automated Predictor of Solar Flares in GOES X-Ray Flux  

Takayuki Muranushi   Submitted: 2015-11-06 21:55

We have developed UFCORIN, a platform for studying and automating space weather prediction. Using our system we have tested 6,160 different combinations of SDO/HMI data as input data, and simulated the prediction of GOES X-ray flux for 2 years (2011-2012) with one-hour cadence. We have found that direct comparison of the true skill statistics (TSS) from small cross-validation sets is ill-posed, and used the standard scores (z) of the TSS to compare the performance of the various prediction strategies. The z of a strategy is a stochastic variable of the stochastically-chosen cross-validation dataset, and the z for the three strategies best at predicting X, ≥M and ≥C class flares are better than the average z of the 6,160 strategies by 2.3σ, 2.1σ, 3.8σ confidence levels, respectively. The best three TSS values were 0.75?0.07, 0.48?0.02, and 0.56?0.04, respectively.

Authors: Takayuki Muranushi, Takuya Shibayama, Yuko Hada Muranushi, Hiroaki Isobe, Shigeru Nemoto, Kenji Komazaki, Kazunari Shibata
Projects: SDO-HMI

Publication Status: accepted for publication in Space Weather
Last Modified: 2015-11-09 13:45
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UFCORIN: A Fully Automated Predictor of Solar Flares in GOES X-Ray Flux

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