# Joint Geoeffectiveness and Arrival Time Prediction of CMEs by a Unified Deep Learning Framework
## 6 Conclusions  
In this paper, we propose a deep learning framework based on satellite time series observation images that can predict both the geoeffectiveness and the arrival time of CME events with no requirement for manually feature selection or professional knowledge. We construct a data set with 2400 partial-/full-halo CME events and 246 geoeffective partial-/full-halo CME events from 1996 to 2018. On the geoeffectiveness prediction of CME events, 2400 CME events are randomly divided into training set and testing set at approximately 4:1 ratio. On arrival time prediction of geoeffective CMEs, 246 geoeffective CME events are randomly divided into a training set and a testing set at approximately 8:1 ratio. Extensive experiments are conducted by comparing with state-of-the-art methods.  
The main results of this study can be summarized as follows. First, the deep learning framework has been successfully applied to the geoeffectiveness and arrival time prediction of CME events. Second, on the geoeffectiveness prediction of CMEs, we propose a deep residual network embedded with the attention mechanism and the feature map fusion module to extract key regional features and fuse the features from each image. Third, data expansion method, deep residual regression network and cost-sensitive regression loss function are proposed to improve the performance of the arrival time prediction of CMEs. Fourth, our method is superior to other state-of-the-art deep learning methods on the geoeffectiveness prediction of CMEs, it can achieve 0.270 F1 score and 75.1% accuracy. Fifth, regarding arrival time prediction, our method is better than traditional CNNs and the machine learning/deep learning methods applied previously and the mean absolute error is only 5.8 h.  
In this work, we have demonstrated the importance of satellite time series images. In the future, we will combine the parameter features such as the angular width, average speed, estimated mass, and so forth, with observation images for multimodal machine learning, and the features extracted from the image can be combined with other features to further improve the performance of the model. Furthermore, future work will introduce refined predictions to give a high-level priority to CMEs associated with stronger solar flares (i.e., M class or above flares) to reduce potential damages to the maximum.  
[PERSON] and [PERSON] jointly conceived the idea, performed the experiments and wrote the manuscript; [PERSON] and [PERSON] collected and analyzed the data. [PERSON], [PERSON] and [PERSON] supervised the study and reviewed this paper. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China under No.61872047 and No.61720106007, the 111 Project under No.B18008, the Funds for Creative Research Groups of China under No.61921003, and the Beijing Nova Program under No.Z201100006820124.  
Not applicable.  
**Data Availability Statement:** Publicly available datasets in this study are described in Section 3.1, and the data can be obtained according to the described steps.  
We are grateful to The SOHO LASCO CME catalog. The SOHO LASCO CME catalog is generated and maintained at the CDAW Data Center by NASA and The Catholic University of America in cooperation with the Naval Research Laboratory. SOHO is a project of international cooperation between ESA and NASA.  
The authors declare no conflict of interest.