# Prediction of Ocean Weather Based on Denoising AutoEncoder and Convolutional LSTM
## 4 Applications
### Description of the Learning Models for Worldwide Data
The shifting prediction is a method of predicting a specific point and using the predicted data as an input to predict the following continuously. For example, if the learning model is trained as a model that predicts one step forward, it cannot predict after four steps. In this case, the result of predicting the first step forward can be used as input again to predict the second step forward. By repeating this process, we can predict 4 steps forward and even can predict 100 steps forward. Figure 12 shows a comparison between normal prediction and shifting prediction. In the process of using deep learning models, we confirmed various studies on shifting prediction. [PERSON] et al. [21] performed a similar approach and named it \"iterative prediction\". If the satellite communication is lost, we cannot get nowcasting information. However, we can predict any days in the future from past data by generating our own prediction model. In addition, using shifting forecasting allows for predicting the far future as well; thus, we can make a long-term plan. In this study, the difference in accuracy between the two methods was analyzed. As explained earlier, it takes a lot of time and effort to train models by dividing the world into 12 areas. Therefore, it is necessary to find the appropriate number of input data to shorten the training time and to achieve affordable prediction accuracy. The original data are in intervals of 6 h. However, by adjusting it to an interval of one day, we compared the accuracy of making predictions using data from a longer period as a look back. Finally, as explained in Section 3.3, we compared and analyzed  
Figure 12: Comparison between normal prediction and shifting prediction. whether it is accurate to predict eight parameters of data with a single model and to divide the model through correlation analysis. Table 4 shows the types of models trained in this study for comparison. Case 1 shows how much look back range could be accepted for the performance of the computer used in this study. Cases 3, 4, 5, 6, and 7 were compared to analyze the accuracy of the prediction model as the look forward increased. When predicting the same time, Cases 6 and 8 were used to determine whether it is correct to predict the time right or to make a shifting prediction. By comparing Cases 7 and 9, when predicting the same time, it was confirmed whether the prediction accuracy is maintained even if the number of data points for prediction is reduced. In Case 8, six channels (SST, MWH, MWP, MWD, WU, and WV) constituted a single learning model. In Case 9, a total of two models was trained. One is a prediction model that includes only one channel (SST), and the other is a prediction model that includes five channels (MWH, MWP, MWD, WU, and WV) to consider the correlation of each channel. The last two cases were selected to see which one was more accurate. For accurate verification, K fold cross-validation is generally used, and training is performed commonly through a total of 10 fold cross-validation [22]. However, since the model of this study takes about one week for training each model, it is difficult to perform all cross-validation at the laboratory level. Due to this limitation, the cross-validation was not implemented here.