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

Abstract

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, $\ge$M and $\ge$C class flares are better than the average $z$ of the 6,160 strategies by 2.3$\sigma$, 2.1$\sigma$, 3.8$\sigma$ confidence levels, respectively. The best three TSS values were $0.75\pm 0.07$, $0.48\pm 0.02$, and $0.56\pm 0.04$, respectively.

47 pages, 11 figures, accepted for publication in Space Weather: http://onlinelibrary.wiley.com/doi/10.1002/2015SW001257/full

References in corpus (5)

• The Helioseismic and Magnetic Imager (HMI) Vector Magnetic Field Pipeline: SHARPs -- Space-weather HMI Active Region Patches
• Solar Flare Prediction Using SDO/HMI Vector Magnetic Field Data with a Machine-Learning Algorithm
• Toward Reliable Benchmarking of Solar Flare Forecasting Methods
• Solar Magnetic Feature Detection and Tracking for Space Weather Monitoring
• Predicting the magnetic vectors within coronal mass ejections arriving at Earth: 1. Initial Architecture

Cited by in corpus (17)

• Flare-productive active regions
• Solar Flare Prediction Model with Three Machine-Learning Algorithms Using Ultraviolet Brightening and Vector Magnetogram
• Deep Flare Net (DeFN) model for solar flare prediction
• Predicting Solar Flares Using a Long Short-Term Memory Network
• A hybrid supervised/unsupervised machine learning approach to solar flare prediction
• Predicting solar flares with machine learning: investigating solar cycle dependence
• Numerical Simulations of Flare-productive Active Regions: delta-sunspots, Sheared Polarity Inversion Lines, Energy Storage, and Predictions
• Operational solar flare prediction model using Deep Flare Net
• Reliable Probability Forecast of Solar Flares: Deep Flare Net-Reliable (DeFN-R)
• A Framework for Designing and Evaluating Solar Flare Forecasting Systems
• Solar Flare Index Prediction Using SDO/HMI Vector Magnetic Data Products with Statistical and Machine Learning Methods
• Solar Flares Forecasting Using Time Series and Extreme Gradient Boosting Ensembles
• Low Dimensional Convolutional Neural Network For Solar Flares GOES Time Series Classification
• ALEXIS: Recreating the X-Ray Emission from the Full Solar Disk as a Linear Combination of Discrete Regions in the Extreme Ultraviolet and Soft X-Rays
• A Comparative Analysis of Machine-learning Models for Solar Flare Forecasting: Identifying High-performing Active Region Flare Indicators
• The Flare Likelihood and Region Eruption Forecasting (FLARECAST) Project: Flare forecasting in the big data & machine learning era
• Why do some probabilistic forecasts lack reliability?