Session 11 - Machine learning and statistical inference techniques

Enrico Camporeale (CWI, The Netherlands), Simon Wing (JHUAPL, USA & CWI, The Netherlands), Jay Johnson (PPPL, USA), Jacob Bortnik (UCLA, USA)
Thursday 17/11, 10:00-13:30

The science of 'making predictions' has been historically based on statistical inference and, more recently, on machine learning techniques. Other areas that are concerned with predictions and are somehow overlapping in scope and methodology include system identification, data assimilation, information theory, signal processing, and uncertainty quantification. All of these disciplines have been studied and developed in context typically unrelated to Space Weather. However, the science behind Space Weather is becoming increasingly multidisciplinary, and the ease of accessing and processing large volumes of data makes these techniques very attractive for the Space Weather community. This session is devoted to contributions that use any of these approaches for Space Weather forecasting.

Poster Viewing
Thursday November 17, 10:00 - 11:00, Poster Area

Thursday November 17, 11:00 - 13:30, Mercator

Click here to toggle abstract display in the schedule

Talks : Time schedule

Thursday November 17, 11:00 - 13:30, Mercator
11:00Blind source separation for better tailored space weather productsDudok de wit, T et al.Oral
11:10FLARECAST Prediction Algorithms: Machine-learning methods for flare prediction and feature selectionPiana, M et al.Oral
11:20Characterization of active regions' time evolution in view of solar flare predictionAttie, R et al.Oral
11:30Solar Flare Forecasting from Magnetic Feature Properties Generated by the SMART AlgorithmBloomfield, D et al.Oral
11:40Application of data assimilation techniques to heliospheric modelling: two preliminary studiesInnocenti, M et al.Invited Oral
11:50Solar wind types from a machine learning point of viewHeidrich-meisner, V et al.Oral
12:005' BreakBreak, Break
12:05NARMAX approach to the Space Weather forecast: results and capabilitiesBalikhin, M et al.Invited Oral
12:20Information theoretical approach to discovering solar wind drivers of the outer radiation beltWing, S et al.Oral
12:30Machine Learning in Radiation Belt PhysicsShprits, Y et al.Invited Oral
12:45On the use of the local ensemble transform Kalman filter (LETKF) for ionospheric data assimilationAngling, M et al.Oral
12:55Development of new geomagnetic index forecasts using the Markov Chain methodJackson, D et al.Oral
13:05An application of machine learning to geomagnetic index prediction: aiding human space weather forecastingBillingham, L et al.Oral
13:15Gaussian Process Models for Prediction of the Dst Index.Chandorkar, M et al.Oral


Thursday November 17, 10:00 - 11:00, Poster Area
1Detecting and tracking ARs emergence with Magnetic BalltrackingAttie, R et al.e-Poster
2Neural network identification of relevant events in time series and preliminary forecast applicationConstantinesc, V et al.p-Poster
3Neural networks and Am predictions, a preliminary studyGruet, M et al.p-Poster