At First, you need to have a time-series prediction task like SST prediction or others so that you can get a reasonable dataset. The dataset is a structured dataset, in the case of sea surface temperature field prediction, the original dataset is m rows and n columns, each column represents an observation point in the field, each row represents a time point, and each value in it represents the temperature measurement at a point in time. With a sample of data, it is possible to extract temporal features based on the time of each row, and with the temporal features and temperature values, it is possible to extract the periodic patterns of the temporal data through the XGBOOST model, adding these as periodic features to the original data, and then, by cutting the data into 3D data through a sliding window method, the correct input to the model is obtained. Then, we can build the 3DCNN-LSTM model to complete the prediction task.
In fact, the method in the paper can be used not only for the task of sea surface temperature prediction, but also for other tasks of spatial and temporal series, such as rainfall prediction, video prediction, etc.
