## 3 Methods
### Overview
### Impact of Dimensionality Reduction

To further explore the impact of dimensionality reduction techniques on model performance, we applied dimensionality reduction to the dataset, specifically reducing it to 4, 6, and 8 dimensions, and recorded the detailed experimental results (see Table 4). It is noteworthy that the impact of dimensionality reduction on model performance is not uniform; in fact, when the dataset dimensions were reduced to 6, we observed an effective improvement in the F1-score. This improvement indicates that reducing the data complexity to a moderate level can help the model focus more on the most critical features closely related to anomaly detection, thereby effectively enhancing the model's recognition ability and accuracy. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Dimensions Remaining** & **Precision** & **Recall** & **F1-Score** \\ \hline
4 & 97.31 & 97.16 & 97.23 \\ \hline
6 & 98.19 & 96.76 & 97.47 \\ \hline
8 & 98.37 & 95.97 & 97.15 \\ \hline Original & 97.82 & 96.76 & 97.29 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Effect of dimensionality reduction on performance of Case 1. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Models** & **Precision** & **Recall** & **F1-Score** \\ \hline OCSVM & 87.61 & 88.29 & 87.95 \\ \hline Deep-SVDD & 88.09 & 88.17 & 88.13 \\ \hline VAR & 84.52 & 82.88 & 83.69 \\ \hline LSTM & 89.99 & 90.89 & 90.89 \\ \hline LSTM-VAE & 92.83 & 91.15 & 91.98 \\ \hline BeatGAN & 90.35 & 94.37 & 92.32 \\ \hline OmniAnomaly & 97.76 & 96.88 & 97.32 \\ \hline InterFusion & 98.87 & 97.13 & 97.99 \\ \hline THOC & 94.49 & 95.23 & 94.86 \\ \hline LSSL & 95.36 & 96.71 & 96.03 \\ \hline Ours & 99.07 & 98.68 & 98.87 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Performance comparison of STformer and 10 baseline methods in Case 2. ### Ablation Study

To systematically analyze the necessity and contribution of each component in our approach, we adopted a strategy of gradually removing individual components and meticulously recorded the changes in model performance after each removal. First, we removed the UMAP (Uniform Manifold Approximation and Projection) dimensionality reduction step and labeled this version of the model as \"-WO-UMAP\". This allowed us to assess whether UMAP dimensionality reduction had a significant impact on the overall performance of STformer. Next, we removed the Time-Block function and labeled this version of the model as \"-WO-Time-Block\" to investigate the extent to which the temporal attention mechanism affects the overall model performance. Finally, we removed the Variable Block from the model and labeled it as \"-WO-Variable-Block\" to evaluate the contribution of the unique design that considers correlations among variables to the model's performance. Through these steps, we were able to gain a detailed understanding of the importance of each component to the overall model performance and the specific roles they each play. Table 5 summarizes the performance results of different ablation variants. First, after removing UMAP, the Precision was 97.82%, Recall was 96.76%, and F1-score was 97.29%. Compared to the complete model, removing UMAP led to a slight decrease in F1-score from 97.47% to 97.29%. This suggests that, while UMAP is not a decisive component, it helps select a small set of representative features from a large pool, thereby reducing the computational burden and slightly enhancing model performance. Next, after removing the Time Block, Precision dropped to 91.88%, Recall was 94.73%, and F1-score was 93.29%. This result clearly demonstrates that the Time Block is crucial for capturing subtle dynamics and trends in data sequences over time, particularly having a significant impact on Precision and F1-score. Finally, after removing the Variable Block, Precision was 98.07%, Recall dropped to 92.29%, and F1-score was 95.09%. In comparison, the complete model achieved a Precision of 98.19%, Recall of 96.76%, and F1-score of 97.47%.