## 4 Experiment
### Experimental Results
\begin{tabular}{l c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Methods**} & \multicolumn{4}{c}{**P1 (SMAP)**} & \multicolumn{4}{c}{**T4 (MSL)**} & \multicolumn{4}{c}{**SWaT**} \\ \cline{2-13}  & **Acc** & **Pre** & **Rec** & **F1** & **Acc** & **Pre** & **Rec** & **F1** & **Acc** & **Pre** & **Rec** & **F1** \\ \hline PCA & 0.890 & 0.110 & 0.999 & 0.198 & 0.934 & 0.031 & 0.999 & 0.060 & 0.858 & 0.394 & 0.696 & 0.503 \\ LOF & 0.794 & 0.691 & 0.999 & 0.817 & 0.931 & 0.410 & 0.999 & 0.581 & 0.813 & 0.978 & 0.696 & 0.813 \\ COPOD & 0.794 & 0.319 & 0.999 & 0.484 & 0.936 & 0.108 & 0.999 & 0.195 & 0.894 & 0.503 & 0.731 & 0.596 \\ OCSVM & 0.791 & 0.366 & 0.999 & 0.536 & 0.934 & 0.108 & 0.999 & 0.195 & 0.844 & 0.214 & 0.862 & 0.343 \\ MAD\_GAN & 0.980 & 0.870 & 0.999 & 0.919 & 0.954 & 0.401 & 0.999 & 0.571 & 0.956 & 0.952 & 0.696 & 0.803 \\ OmniAnomaly & 0.982 & 0.859 & 0.999 & 0.924 & 0.965 & 0.441 & 0.999 & 0.612 & 0.959 & 0.976 & 0.969 & 0.812 \\ USAAD & 0.980 & 0.849 & 0.999 & 0.918 & 0.969 & 0.488 & 0.999 & 0.648 & 0.959 & 0.989 & 0.688 & 0.811 \\ TranAD & 0.980 & 0.857 & 0.999 & 0.918 & 0.906 & 0.246 & 0.999 & 0.395 & 0.960 & 0.997 & 0.688 & 0.814 \\ CAT-IADEF & 0.981 & 0.857 & 0.999 & 0.923 & 0.979 & 0.596 & 0.999 & 0.747 & 0.958 & 0.971 & 0.696 & 0.811 \\ \hline \hline \end{tabular} 
\begin{tabular}{l c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Methods**} & \multicolumn{4}{c}{**Valve1-4 (SKAB)**} & \multicolumn{4}{c}{**Valve1-14 (SKAB)**} & \multicolumn{4}{c}{**T1 (SMAP)**} \\ \cline{2-13}  & **Acc** & **Pre** & **Rec** & **F1** & **Acc** & **Pre** & **Rec** & **F1** & **Acc** & **Pre** & **Rec** & **F1** \\ \hline PCA & 0.376 & 0.319 & 0.999 & 0.483 & 0.367 & 0.350 & 0.999 & 0.519 & 0.700 & 0.300 & 0.999 & 0.461 \\ LOF & 0.323 & 0.384 & 0.999 & 0.555 & 0.350 & 0.401 & 0.999 & 0.572 & 0.472 & 0.944 & 0.999 & 0.971 \\ COPOD & 0.401 & 0.319 & 0.999 & 0.483 & 0.432 & 0.350 & 0.999 & 0.519 & 0.603 & 0.730 & 0.999 & 0.844 \\ CCSVM & 0.385 & 0.341 & 0.999 & 0.509 & 0.388 & 0.357 & 0.999 & 0.526 & 0.639 & 0.702 & 0.999 & 0.825 \\ MAD\_GAN & 0.725 & 0.544 & 0.999 & 0.702 & 0.823 & 0.679 & 0.999 & 0.804 & 0.970 & 0.909 & 0.999 & 0.953 \\ OmniAnomaly & 0.760 & 0.770 & 0.940 & 0.840 & 0.770 & 0.740 & 0.980 & 0.850 & 0.975 & 0.923 & 0.999 & 0.960 \\ USAAD & 0.912 & 0.801 & 0.999 & 0.885 & 0.929 & 0.860 & 0.999 & 0.914 & 0.971 & 0.912 & 0.999 & 0.954 \\ TranAD & 0.898 & 0.7T1 dataset, which is considered because this subset's local distribution of the time series is suitable for calculating k-fields in LOF. For the deep learning method, USAD only uses the simplest AE model and only considers the linear transformation, resulting in its insignificant effect, while CAT-IADEF adopts the CNN network and considers the relationship between different features. TranAD and MAN_GAN use an attention mechanism when detecting anomalies. TranAD uses positional encoding in the transformer structure to help capture the temporal order, which performs optimally on the SWaT dataset. OmniAnomaly is not the most prominent for all datasets except P1 because it is input sequentially, preserves essential information, and reconstructs all inputs regardless of input data, which prevents them from detecting anomalies that are close to normal trends. CAT-IADEF's confrontational training allows it to magnify \"slight\" anomalies and help it see them, even though other models will detect \"slight\" anomalies as routine data. **Anomaly explanation. ** The results of the anomaly explanation are shown in Table 3, which we tested on the P1 dataset. In Table 3, Dimension 1 is the dimension with the highest number of anomalies, and Dimension 2 is the dimension with the second highest number of anomalies. The number corresponding to the dimension is the serial number of the dimension. On the P1 dataset, for example, the dimension containing the most anomalies is Dimension 5, and the dimension with the second-most anomalies is Dimension 6.