Solar Flare Prediction Using Two-tier Ensemble with Deep Learning and Gradient Boosting Machine

This paper describes a machine learning approach to the solar flare prediction competition, a track in IEEE Big Data 2019 Big Data Cup. The competition task is to predict whether or not there is a solar flare event basing on a given time series of solar magnetic field parameters. Our method involves exploring and constructing data-driven machine learning models for the classification task of two imbalanced class labels from time series. Specifically, the investigated models include boosting, logistic regression, multilayer perceptron neural network, and long short-term memory neural network. These models have been successfully deployed and combined in an ensemble framework with two tiers in our final proposed solution for this competition. Our proposed approach ranked at the second place in the competition (the first on the private board and the eleventh on the public board).