# Joint Geoeffectiveness and Arrival Time Prediction of CMEs by a Unified Deep Learning Framework
## 1 Introduction  
Coronal Mass Ejections (CMEs) are fierce eruption phenomena in the solar atmosphere, which is one important source of severe space weather events [1]. When arriving at Earth, the magnetic field and plasma in a CME will disturb Earth's space environment and may cause geomagnetic storms [2]. Magnetic storms are threats to the high-tech modern society, which relies on stable satellite communication, global navigation and electricity power supplements in almost every aspect [3]. To reduce the potential damages of CMEs, a reliable forecasting model is necessary. Generally speaking, it takes 1-5 days for CMEs to propagate from the Sun to Earth, which makes it theoretically feasible to predict its geoeffectiveness and the arrival time in advance. Many efforts have been made to achieve a fast and accurate forecast in traditional methods. Based on the observations from Large Angle and Spectrometric Coronagraph (LASCO) onboard the Solar and Heliospheric Observatory (SOHO) [4], many empirical models have been developed, such as the empirical shock arrival (ESA) [5] model, the Cone model [6] and Self-similar Expansion model [7]. These models are based on the-path-fit of observed morphology evolution of CMEs in the coronagraph and with a presumed propagation model. Although limited by the projection effects [8] from one single coronagraph, these models' correctness and arrival time prediction is acceptable. The launch of the Solar-Terrestrial Relations Observatory (STEREO) [9] twin star provides additional view points away from the Sun-Earth line. With the observations from the Heliosphere Imagers (HIs) [10], the projection effects have been further reduced and the improved empirical models have been proven to be adequately effective [11]. Most of the empirical models are mathematical models with no requirement of background heliospheric measurements. Hydrodynamic models, like the drag-based model [12] and shock-based models [13; 14], take into consideration the acceleration or deceleration of a CME caused by the ambient solar wind. However, those models are kinetic models without magnetic fields and with limited physical meanings, and the solar wind parameters used as input in these models need to be measured in-situ or manually set based on historical measurements. This situation was changed by magnetohydrodynamics (MHD) [15; 16; 17] models. With properly set input parameters and boundary conditions, a MHD model could track the whole transit of a CME from the Sun to the Earth at the cost of high computing power consumption [18]. The demands of supercomputers make such forecast models hard to distribute and to give a near real-time forecast. In recent years, with the continuous development of machine learning and deep learning, related methods have also been applied to geoeffectiveness and arrival time prediction of CMEs. [PERSON] et al. [19] attempts to use the logistic regression which uses only initial CME parameters as input to predict the geoeffectiveness of CMEs, but it doesn't fit well on the training set. [PERSON] et al. [20] uses the fully connected neural network (FCNN) to analyse the transit time for 153 CME events using the initial velocity of the CME and the central meridian distance of the associated flare as inputs, the mean absolute error (MAE) obtained is about 11.6 h. [PERSON] et al. [21] uses the support vector machine (SVM) [22] to predict the arrival time for 182 geoeffective partial-/full-halo CME events, and the MAE is about 5.9 h. However, 5.9 h is the best result in a random 100,000 testing set. The above machine learning methods require manual collection of parameters and if the relevant parameters are missing, it may cause a wide range of errors. [PERSON] et al. [23] first uses the convolutional neural network to estimate the arrival time of geoeffective CME events based on satellite observation images.