## 3 Results and Discussion  
Figure 5 and Table 6 present the prediction results and metric scores of all the models. The improved N-BEATS model outperforms the other baseline models across all the evaluation metrics. This demonstrates that the improved N-BEATS exhibits outstanding performance in predicting the stiffness degradation sequence of wind turbine blades, particularly excelling in handling small-scale datasets and mitigating the impact of noise. In contrast, although the TCN model excels at long-sequence modeling and capturing dependencies within them, its sensitivity to small-scale datasets and high-noise data often leads to a decline in prediction accuracy. The TCN model still shows limitations in handling short time series and noisy data, making its performance slightly inferior in this experiment. Although both N-BEATS and NHiTS have shown excellent performance in multiple time series forecasting tasks, especially on large-scale datasets, where their layered architectures can effectively capture global features, the complexity of these models becomes a burden when dealing with the small-scale data in this study, resulting in slower training speeds and slightly lower generalization performance. The CNN-LSTM model, while combining the advantages of both to capture local features and model long-term dependencies in sequences, is more limited in extracting useful feature information from \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Hyperparameters** & **Value** & **Hyperparameters** & **Value** \\ \hline Backcast Window & 16 & Layers & 5 \\ Prediction Window & 5 & Width & 291 \\ Batch size & 152 & Epochs & 20 \\ Stacks & 10 & Dropout & 0.12 \\ Blocks & 4 & Learning rate & \(6.5\times 10^{-5}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Optimal hyperparameters of the improved N-BEATS. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Metric** & **MAE** & **RMSE** & **MAPE** & **R\({}^{2}\)** \\ \hline Function & MAE = \(\frac{1}{N}\sum\limits_{t=1}^{N}|y_{t}-\widetilde{y}_{t}|\) & RMSE = \(\sqrt{\frac{1}{N}\sum\limits_{t=1}^{N}(y_{t}-\widetilde{y}_{t})^{2}}\) & MAPE = \(\frac{100\%}{N}\sum\limits_{t=1}^{N}\left|\frac{y_{t}-\widetilde{y}_{t}}{y_{t}} \right|\) & R\({}^{2}=1-\frac{\sum\limits_{t=1}^{N}(y_{t}-\widetilde{y}_{t})^{2}}{\sum \limits_{t=1}^{N}(y_{t}-\overline{y})^{2}}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Evaluation functions. small-scale, high-noise datasets. As a result, its performance is inferior to that of the improved N-BEATS model. Moreover, each model exhibited a certain degree of bias during the forecasting process. This study uses the Mean Percentage Error (MPE) to measure the bias in each model's predictions. Table 7 presents the bias associated with each model's predictions. Among all models, the improved N-BEATS model, which introduces the Pinball-MAPE loss function, achieved the lowest MPE of \(-0.02\%\), significantly reducing the risk of over-prediction. To evaluate the impact of different modules on the predictive performance of the improved N-BEATS model, this study conducted two ablation experiments. Figure 6a,b, respectively, illustrate the comparison of prediction results under different loss functions and data processing methods in two sets of experiments. The first experiment examined the \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & \multicolumn{4}{c}{**Evaluation Metrics**} \\ \hline  & **MAPE(\%)** & **RMSE** & **MAE** & **R\({}^{2}\)** \\ \hline CNN-LSTM & 0.08736 & 0.00101 & 0.00083 & 0.9758 \\ TCN & 0.05886 & 0.00078 & 0.00056 & 0.9859 \\ N-BEATS & 0.07445 & 0.00091 & 0.00071 & 0.9811 \\ NHiTS & 0.04696 & 0.00058 & 0.00045 & 0.9923 \\ Improved N-BEATS & 0.04152 & 0.00049 & 0.00041 & 0.9945 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Comparison of evaluation metrics for different prediction models.