# Research on the Predictive Algorithm of Wear Degree Based on Adaptive Feature Learning
## 3 Experimental Design and Verification
### Experimental Results and Analysis  
Experiment 1: Performance analysis of an algorithm with an attention fusion mechanism. Targeting the issue of low prediction accuracy for long-term sequential data in encoder-decoder architectures, the performance of the algorithm is improved by incorporating feature attention mechanisms and temporal attention mechanisms to handle long sequence information. To prove the effectiveness of the algorithm with the fusion of dual attention mechanisms, a comparative prediction experiment was conducted using a single attention mechanism and a dual attention mechanism, and the experimental results are shown in Table 3.  
From the data in Table 3, compared with the FA-Encoder-Decoder that uses the feature attention mechanism alone, the DA-Encoder-Decoder model has improved by 0.8113, 1.5848, 2.7314 (%), and 0.0389 in the four indicators of MAE, RMSE, MAPE, and R\({}^{2}\), which confirms the significant gain of adding the temporal attention mechanism for predicting long-term sequence data. Compared with the TA-Encoder-Decoder that uses the temporal attention mechanism alone, the DA-Encoder-Decoder model has improved by 0.5546, 0.6157, 2.6773 (%), and 0.0256 in the four indicators, confirming the effectiveness of adding the feature attention mechanism to improve the prediction indicators. The visualized comparative results are shown in Figure 7, with the red line representing the model with the fusion of dual attention mechanisms, which has a higher degree of fit between the prediction curve and the true value compared to the other two models with single attention mechanisms. The reason for this is that the feature attention mechanism can adaptively select more important features according to the contribution of each feature to the wearer, while the temporal attention mechanism can solve the problem of diluted information caused by long sequence input. Therefore, the joint action of these two attention mechanisms has a better prediction effect. \begin{table}
\begin{tabular}{c c c c} \hline \hline  & **FA-Encoder-Decoder** & **TA-Encoder-Decoder** & **DA-Encoder-Decoder** \\ \hline MAE & 4.0360 & 3.7793 & 3.2247 \\ RMSE & 5.2999 & 4.3308 & 3.7151 \\ MAPE (\%) & 12.2204 & 12.1663 & 9.4890 \\ R\({}^{2}\) & 0.8362 & 0.8495 & 0.8751 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Performance comparison of models using different attention mechanisms. Experiment 2: Performance validation of an algorithm with linear autoregressive component optimization. In order to address the problem of neural network models being insensitive to input data with large-scale changes, the ARIMA linear model is introduced to optimize the prediction results. To verify the effectiveness of the improved combined prediction model, the long short-term memory network (LSTM), gated recurrent unit (GRU), and Stacked auto-encoder (SAEs) [30] were selected for comparative experiments. The dataset remained the same, and the experimental results are shown in Table 4.  
From the data in Table 4, it can be seen that the DA-Encoder-Decoder prediction model with dual attention mechanisms is superior to the three basic neural network prediction models, LSTM, GRU, and SAEs, in four evaluation indicators. After introducing ARIMA, the DA + ARIMA combined prediction model can further improve the predictive ability by about 1.8%, 3.3%, 2.6%, and 1.6% on four evaluation indicators. The visualization comparison of the experiment is shown in Figures 8 and 9. The figures intuitively indicate that under the conditions of drastic changes in input data scale, the prediction errors of the three basic neural network algorithms are relatively large. The prediction curve fitting effect of the dual attention mechanism model is improved, but the problem of error still exists. After introducing the linear component, the combined model exhibits the closest fitting accuracy between the prediction curve and the ground truth, effectively correcting errors.