 |
The first stage is Long period time features extraction. Considering the seasonal periodicity of SST data, the Pearson correlation coefficient and XGboost mode are used to extract the long period time features of SST data, which together with the raw SST data are used as inputs of the 3D CNN model in next phase. Look at this picture. In the training phase of the model, we use the temporal features of the sea surface temperature as features and the sea surface temperature at each point as features to train the XGBOOst model. Thus, for each observation point, we can use the trained xgboost model to predict the relationship between the temporal features and the sea surface temperature, which reflects the seasonality of the sea surface temperature data, denoted by XT subsript ij.
|
IEEE/ICACT20210141 Slide.09
[Big Slide]