# Time Series Prediction of Sea Surface Temperature Based on an Adaptive Graph Learning Neural Model  
[PERSON]  
1 Department of Computer Engineering and Science, Shanghai University, Shanghai 200444, China; [EMAIL_ADDRESS] ([PERSON]); [EMAIL_ADDRESS] ([PERSON]); [EMAIL_ADDRESS] ([PERSON].) [PERSON]  
1 Department of Computer Engineering and Science, Shanghai University, Shanghai 200444, China; [EMAIL_ADDRESS] ([PERSON]); [EMAIL_ADDRESS] ([PERSON]); [EMAIL_ADDRESS] ([PERSON].) [PERSON]  
1 Department of Computer Engineering and Science, Shanghai University, Shanghai 200444, China; [EMAIL_ADDRESS] ([PERSON]); [EMAIL_ADDRESS] ([PERSON]); [EMAIL_ADDRESS] ([PERSON].) [PERSON]  
2 Beijing Institute of Applied Meteorology, Beijing 100029, China 223 Shanghai Institute for Advanced Communication and Data Science, Shanghai University, Shanghai 200444, China 23  
understanding of local and global climate characteristics. However, the ocean not only has independent tidal and ocean current systems, but also multi-dimensional information, complex spatio-temporal correlations, a large area, a multi-model, remote correlation, and other problems, which cause great difficulties for the prediction and mechanisms of discovery of SST. At present, the prediction methods of SST time series data can be divided into three categories. The first is a numerically-based approach, which predicts ocean elements according to a set of predefined rules such as those of coupled ocean-atmosphere models (GCMS) [7; 8; 9]. However, these methods not only require extremely high computational resources and a professional knowledge of thermodynamics [10], but also involve complex external data, which requires a large amount of model start-up time and set of integral assumptions. In addition, because these methods need to predict many parameters at the same time, it is a difficult task to accurately predict a single parameter for this method [11]. Secondly, some methods of machine learning technology show a great performance in the prediction of complex time series, for example, KNN [12] for ocean current prediction and SVR [13] for wind field prediction. However, in the process of prediction, these methods only consider the temporal correlation of the data and ignore the spatial information, which leads to the failure of the model to effectively capture the spatio-temporal-dependent information of nonlinear correlation. Thirdly, with the development of deep learning, more and more researchers have begun to use neural networks to predict ocean elements. A lot of methods based on neural networks are widely applied in predicting sea surface temperature [14]. [PERSON] et al. [5] proposed a fully connected network model (FC_LSTM) based on LSTM for sea surface temperature prediction. [PERSON] et al. [15] built a GED model, composed of GRU and an attention mechanism, which can be used to predict multi-scale SST. However, the above methods regard SST prediction as a single-source time-series prediction problem and do not make full use of the hidden relationship between time and space. This will not only lead to a failure to learn the fusion mode of multi-elements in the actual complex marine environment, but also cause the loss of fusion information and a decline in prediction accuracy. At the same time, the graph neural networks (GNNs) have made great progress in dealing with relation dependence. GNNs can make full use of the relationship between variables, especially in the spatio-temporal correlation, due to the characteristics of graph neural networks of replacement invariance, local connectivity, and composition. Existing graph neural network methods usually use GCN-based methods to model unstructured sequences and the inter-dependencies of different variables such as ASTGCN [16], STSGCN [17], and GMAN [18]. These methods take multivariate time series and externally predefined graph structures as inputs to predict future values or labels of multivariate time series. Compared with the previous methods, these methods have made significant improvements.