Flare forecasting and feature ranking using SDO/HMI data. (arXiv:1812.07258v1 [astro-ph.SR])
<a href="http://arxiv.org/find/astro-ph/1/au:+Piana_M/0/1/0/all/0/1">Michele Piana</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Campi_C/0/1/0/all/0/1">Cristina Campi</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Benvenuto_F/0/1/0/all/0/1">Federico Benvenuto</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Guastavano_S/0/1/0/all/0/1">Sabrina Guastavano</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Massone_A/0/1/0/all/0/1">Anna Maria Massone</a>
We describe here the application of a machine learning method for flare
forecasting using vectors of properties extracted from images provided by the
Helioseismic and Magnetic Imager in the Solar Dynamics Observatory (SDO/HMI).
We also discuss how the method can be used to quantitatively assess the impact
of such properties on the prediction process.
We describe here the application of a machine learning method for flare
forecasting using vectors of properties extracted from images provided by the
Helioseismic and Magnetic Imager in the Solar Dynamics Observatory (SDO/HMI).
We also discuss how the method can be used to quantitatively assess the impact
of such properties on the prediction process.
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