FLARECAST uses a machine learning algorithm to compute for a given point in time the probability of occurrence of a solar flare within the next day.
FLARECAST predicts a ? probability for a ? flare (in active region ?) between ? and ?.
Due to Solar inactivity FLARECAST expects no major flares between ? and ?
Due to missing or invalid source data FLARECAST cannot produce predictions.
This product shows the prediction of a flare occurrence within the next 24 hours after the given date, and starting at 3AM. The probability is based on the best available machine learning algorithm tested in FLARECAST, Random Forest. The flare size assumed is either C, M, or X, following the GOES classification. The NOAA active region number(s) is or are indicated, and the diagram in the grey area emphasizes the different probability for each active region. If there are no active regions present, the system cannot make any prediction and issues a corresponding message.
The FLARECAST consortium developed an automated forecasting system for solar flares. The team integrated virtually every solar flare-predicting parameter into an open online application programming interface, flexible enough to facilitate future expansion. We identified the best performers by employing a variety of statistical and machine learning techinques, including standard methods such as Linear Discriminant Analysis, Clustering and Regression Analysis, Neural Networks, as well as innovative approches including Multi-Task Lasso, Simulated Annealing and Random Forest. A robust exploration work package identified promising new predictors and connected flare prediction to other manifestations of solar eruptive activity such as coronal mass ejections.
For more information please visit this page: flarecast.eu
A simple REST API gives access to the FLARECAST flare forecasts of a whole year. You may use any
common HTTP client such as wget, curl or Python requests.
Make sure to pass a valid authentication cookie to the method.
Syntax:
Base URL: http://ssaflarecast.cs.technik.fhnw.ch/
System type: ['test'|'prod']
Command: /api/prediction/flarecast_${year}.xml
Year: four digit year after 2013 or 'latest'.
Example:
http://ssaflarecast.cs.technik.fhnw.ch/prod/api/prediction/flarecast_2019.xml