# MoCoformer: Quantifying Temporal Irregularities in Solar Wind for Long-Term Sequence Prediction
## 4 Results
### Univariate Results
Figure 6 illustrates that MoCoformer accurately reflects the trends and fluctuations in the true values, exhibiting superior performance in capturing the peaks and troughs of the time series compared to other models. In particular, in Cases 96 and 196, MoCoformer demonstrates enhanced sensitivity in capturing changes in the true values compared to other models, enabling accurate predictions of increases and decreases in the corresponding periods. However, with regard to numerical values, it appears that MoCoformer tends to be relatively conservative in its predictions. For example, in Case 720, although MoCoformer is able to identify changes in true values, it still encounters difficulties in modeling large fluctuations in them. This observation is consistent with the results of the three evaluation metrics, which indicate that while there have been notable improvements in MAE and MAPE, RMSE values remain substantial. The results of a univariate prediction experiment conducted on the omni-IMF dataset are presented in Figure 7. The figure depicts comparative curves between the ground truth and the outputs of various models. Figure 6: Visualization of the predicted results of various models with the ground truth for different prediction lengths on the omni-Field-Magnitude dataset experiment. Figure 7 illustrates that the MoCoformer model outperforms the other models in capturing trends and changes in the true values, which is particularly evident in Cases 192 and 720. It is also noteworthy that although the MoCoformer model excels in trend capture, its numerical predictions tend towards conservatism, implying a tendency for more conservative numerical outputs. This becomes evident when modeling prominent exceptional values, as illustrated in Case 336. Moreover, the limited size of the omni-IMF dataset contrasts with the relatively higher prominence of exceptional values. No model demonstrates superior performance in predicting these exceptional values. This observation is consistent with the trend observed in the three assessment metrics. The MAE and MAPE demonstrate a more pronounced improvement, while the RMSE shows a comparatively slower improvement. Overall, there is considerable scope for further enhancement and improvement of the models' performance on the omni-IMF dataset, particularly in the prediction of exceptional values. In comparison to other models, MoCoformer demonstrates superior performance in identifying trends and fluctuations in actual values. It is able to recognize trends with exceptional precision. Nevertheless, its numerical predictions frequently exhibit a tendency  
Figure 7: Visualization of the predicted results of various models with the ground truth for different prediction lengths on the omni-IMF dataset experiment. towards conservatism, as evidenced by the table, which shows that there are notable improvements in MAE and MAPE, yet RMSE values remain relatively high. In conclusion, it can be stated that MoCoformer makes a significant contribution to long-term sequence prediction in the solar wind domain. However, the complexity of solar wind data presents numerous challenges for long-term solar wind sequence prediction research.