In this paper, the 3DCNN-LSTM-AT technique shows the highest accuracy. However, looking at the r^2 indicator alone, it seems that there is no significant difference in accuracy from other techniques. What is the reason for this? In addition, there is no significant accuracy difference, especially with the 3DCNN-LSTM method, but the learning time seems to be close to three times. Therefore, is it better to use the 3DCNN-LSTM method when there is a lack of computation or learning time?
The r^2 is not really a measure of the model's prediction accuracy, but rather it is something like describing how confident the model is in predicting the final outcome, and the model with the greater confidence and accuracy is often the best. Therefore, it is often used as a reference rather than a rigid indicator to evaluate the performance of a model. If the model predicts a result that differs significantly from the true result but the model is still confident, r^2 is often high. For 3DCNN-LSTM-AT, if it is a long-term prediction task, the result is often better than that of 3DCNN-LSTM, because the Attention mechanism plays a bigger role, after all, the historical input sequence will be longer, while if it is a common short- to medium-term prediction task, the improvement effect may not be so obvious.
