# Joint Geoeffectiveness and Arrival Time Prediction of CMEs by a Unified Deep Learning Framework
## 1 Introduction
It only uses the SOHO LASCO C2 white-light corona observations as input, and the error was about 12.4 h. Although the error is large, the input is a directly observed image, which simplifies the tedious work of manual feature acquisition. All above-mentioned four methods have certain limitations, mainly in that they can only predict the geoeffectiveness of CMEs or can only predict the transit/arrival-time of CME events that can reach Earth, and do not combine the two demands in a machine learning or deep learning method. With the introduction of large image data sets such as ImageNet [24] and MS-COCO [25], many simple, highly modularized convolutional neural networks architecture [26; 27; 28; 29; 30; 31; 32; 33; 34; 35] have been proposed in the field of computer vision. VGGNet [27] uses a stack of very small (\(3\times 3\)) convolution filters to increase the depth of the network, and explore the impact of depth on network performance. Resnet [28] presents a residual learning framework to make the deep neural network easier to train, and make the gradient back propagated by the model have better local correlation [36]. GoogLeNet [29] proposes the Inception module, which increases the width and depth of the network without increasing the computational budget and improves the adaptability of the network to different scales through multi-scale processing. Resnext [31] combines the idea of the Inception module with the residual learning, and improves the performance of the model by increasing the cardinality. Mobilenet [32] builds a lightweight deep neural network through depth-wise separable convolutions. In this paper, we present a deep learning framework based on time series of satellites' optical observations that can give both the geoeffectiveness and the arrival time prediction of the CME events. The framework is composed of the collection of time series observation images, the geoeffectiveness prediction algorithm and arrival time prediction algorithm. The contributions of this paper are summarized as follows:  
1. It is the first time that making geoeffectiveness and arrival time predictions of CMEs in a unified deep learning framework and forming an end-to-end prediction. Once we get the observation images, we can immediately get the prediction result without selecting features manually or professional knowledge. 2. For the geoeffectiveness prediction of CMEs, we propose a deep residual network embedded with the attention mechanism and the feature map fusion module. Based on those, we can effectively extract key regional features and fuse the feature from each image. 3. For the arrival time prediction of geoeffective CMEs, we propose a data expansion method to increase the scale of data and deep residual regression network to capture the feature of the observation image. Meanwhile, the cost-sensitive regression loss function is proposed to allow the network focus more attention on hard predicting samples. The remainder of this paper is organized as follows--in Section 2, we introduce our proposed deep learning framework in detail. Section 3 describes the dataset and experimental setting. The experimental results and comparisons are reported in Section 4. Sections 5 and 6 are the discussion and conclusion, respectively.