## 5 Results
### The Results of the Short-Period Prediction using the LSTM Model
In Figure 12, under the same conditions as in Figure 9, using the EMD-LSTM model, the original data are decomposed into 14 components using the EMD algorithm, which are individually predicted with LSTM fitting and finally merged to obtain the prediction results. The EMD-LSTM model fits better than the LSTM model because the EMD algorithm can split the data series by detecting the peak and valley values of the two curves, which directly and effectively improves the lag in the prediction. According to the above tests, a lower learning rate has few accuracy improvements. At the same time, the training times and training time increased significantly, so we chose a learning rate of \(10^{-3}\) for subsequent model training. By observing the actual Dst data, the data from 2019 are selected as the test set to predict the quiet period, and the data from 1 January 2023 to 21 May are selected as the test set for the active period prediction. The results are as follows. Figure 13 shows the results of using the 2018 Dst data with one-hour ahead forecasting for 2019. The results show that the fitted values agree well with the actual values. The RMSE is 2.64 nT, the CC is 0.97, and the precision of the results is between those obtained by LSTM using 90 and 1096 days of data. Figure 12: Results of the EMD-LSTM model when using the same parameters as in Figure 9.  
Figure 13: Results of model prediction through 750 training sessions at a learning rate of \(10^{-3}\). In order to further test the model, the dramatic changes in the Dst index in 2023 are now predicted based on the 2022 data, and we expect to model and predict the corresponding geomagnetic storms. The following are the results based on the LSTM model. Figure 14 shows the one-hour ahead prediction of the data from 1 January to 21 May 2023, with an RMSE of 7.34 nT and a CC of 0.96, using the Dst data of 2022. The storm with the largest amplitude occurred on 24 April, and its prediction results are shown in Figure 15, with an RMSE of 20.47 nT and a CC of 0.91 before and after the storm (300 h). It is found that the fitting before and after the storm valley is good, while the prediction of the main phase is not good, and the difference is more than 100 nT. components rather than the entire Dst index, so using EMD-LSTM can make the prediction closer to the actual value. We first predict the data from 2019. Figure 17 shows the result of using the Dst data from 2018, making a one-hour ahead prediction and predicting the data from 2019. The RMSE is 3.29 nT, and the CC is 0.95, which makes the prediction effect more satisfactory. Compared with the LSTM method, the RMSE increased by 0.65 nT and the CC decreased by 0.02. Next, we test the prediction for 2023. Figure 18 shows different parts of the 2022 Dst index decomposed using EMD. Figure 19 shows a one-hour ahead prediction from 1 January to 21 May 2023 using the 2022 Dst data. The results show that the RMSE is 8.87 nT and the CC is 0.93. There is an increase of 1.53 nT in the RMSE and a decrease of 0.03 in the CC compared with the LSTM method, and the fitting of the main phase is obviously better than the LSTM method. Figure 16: Data decomposition using EMD based on the 2018 Dst index. Figure 17: Results of model prediction through 600 training sessions with a learning rate of \(10^{-3}\). For further inspection, we list the modeling of the geomagnetic storm that occurred on 24 April 2023. Figure 20 shows that the modeling is highly consistent in the start timing, amplitude, and recovery value of the magnetic storm. In the period before and after the storm (300 h), the RMSE is 16.91 nT and the CC is 0.96; they reduced by 3.56 nT and improved by 0.05 compared with the LSTM model. The fitted values are slightly higher during the initial and main phases, while they are more consistent during the recovery phase. The Dst index during the main phase can be effectively and precisely predicted.